From 9d2e6ef69ee6ade8a63bfca21db7b933f7fa12c1 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 15:46:24 -0500
Subject: [PATCH 01/80] started rye conversion

---
 examples/pyproject.toml                  |  30 ++++++
 examples/scripts/config.json             |  32 ------
 examples/scripts/format_notebooks.py     |  97 +++++++++++++++++
 examples/scripts/generate_readme.py      |  16 +--
 examples/scripts/upload_documentation.py | 127 -----------------------
 pyproject.toml                           |   8 +-
 6 files changed, 140 insertions(+), 170 deletions(-)
 create mode 100644 examples/pyproject.toml
 delete mode 100644 examples/scripts/config.json
 create mode 100644 examples/scripts/format_notebooks.py
 delete mode 100644 examples/scripts/upload_documentation.py

diff --git a/examples/pyproject.toml b/examples/pyproject.toml
new file mode 100644
index 000000000..18673b01f
--- /dev/null
+++ b/examples/pyproject.toml
@@ -0,0 +1,30 @@
+[project]
+name = "examples"
+version = "0.1.0"
+description = "Labelbox Python Example Notebooks"
+authors = [{ name = "Labelbox", email = "support@labelbox.com" }]
+readme = "README.md"
+requires-python = ">=3.8"
+dependencies = ["jupyter>=1.0.0"]
+
+[project.urls]
+Homepage = "https://labelbox.com/"
+Documentation = "https://labelbox-python.readthedocs.io/en/latest/"
+Tutorials = "https://docs.labelbox.com/page/tutorials"
+Repository = "https://github.com/Labelbox/labelbox-python/examples"
+Issues = "https://github.com/Labelbox/labelbox-python/issues"
+
+[tool.rye]
+managed = true
+virtual = true
+dev-dependencies = [
+    "yapf>=0.40.2",
+    "black[jupyter]>=24.4.2",
+    "databooks>=1.3.10",
+]
+
+[tool.rye.scripts]
+clean = { chain = ["clean:databooks", "clean:black", "clean:format"] }
+"clean:databooks" = 'databooks meta --no-rm-exec . --yes'
+"clean:black" = 'black --line-length 80 .'
+"clean:format" = { call = "scripts.format_notebooks" }
diff --git a/examples/scripts/config.json b/examples/scripts/config.json
deleted file mode 100644
index 6f0a0e91c..000000000
--- a/examples/scripts/config.json
+++ /dev/null
@@ -1,32 +0,0 @@
-[
-  {
-    "dir": "basics",
-    "title": "Getting started",
-    "order": 0
-  },
-  {
-    "dir": "project_configuration",
-    "title": "Project configuration",
-    "order": 1
-  },
-  {
-    "dir": "annotation_types",
-    "title": "Annotation types",
-    "order": 2
-  },
-  {
-    "dir": "annotation_import",
-    "title": "Annotation import (Ground Truth & MAL)",
-    "order": 3
-  },
-  {
-    "dir": "label_export",
-    "title": "Label export",
-    "order": 4
-  },
-  {
-    "dir": "model_diagnostics",
-    "title": "Model diagnostics",
-    "order": 5
-  }
-]
\ No newline at end of file
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
new file mode 100644
index 000000000..0050609d7
--- /dev/null
+++ b/examples/scripts/format_notebooks.py
@@ -0,0 +1,97 @@
+import glob
+import json
+from copy import deepcopy
+from concurrent.futures import ProcessPoolExecutor
+import multiprocessing
+
+from yapf.yapflib.yapf_api import FormatCode
+
+IGNORE = ["template.ipynb"]
+
+BANNER_CELL = {
+    "cell_type": "markdown",
+    "id": "db768cda",
+    "metadata": {},
+    "source": [
+        "<td>",
+        '   <a target="_blank" href="https://labelbox.com" ><img src="https://labelbox.com/blog/content/images/2021/02/logo-v4.svg" width=256/></a>',
+        "</td>",
+    ],
+}
+
+LINK_CELL = {
+    "cell_type": "markdown",
+    "id": "cb5611d0",
+    "metadata": {},
+    "source": [
+        "<td>",
+        '<a href="{colab}" target="_blank"><img',
+        'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>',
+        "</td>",
+        "\n",
+        "<td>",
+        '<a href="{github}" target="_blank"><img',
+        'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>',
+        "</td>",
+    ],
+}
+
+COLAB_TEMPLATE = "https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/{filename}"
+GITHUB_TEMPLATE = "https://github.com/Labelbox/labelbox-python/tree/develop/examples/{filename}"
+
+
+def format_cell(source):
+    for line in source.split("\n"):
+        if line.strip().startswith(("!", "%")):
+            return source
+    return FormatCode(source, style_config="google")[0]
+
+
+def add_headers(file_name):
+    with open(file_name, "r") as file:
+        data = json.load(file)
+
+    colab_path = COLAB_TEMPLATE.format(filename=file_name)
+    github_path = GITHUB_TEMPLATE.format(filename=file_name)
+
+    link_cell = deepcopy(LINK_CELL)
+
+    link_cell["source"][1] = link_cell["source"][1].format(colab=colab_path)
+    link_cell["source"][6] = link_cell["source"][6].format(github=github_path)
+
+    data["cells"] = [BANNER_CELL, link_cell] + data["cells"]
+
+    with open(file_name, "w") as file:
+        file.write(json.dumps(data, indent=4))
+
+    print("Formatted", file_name)
+
+
+def format_file(file_name):
+    with open(file_name, "r") as file:
+        data = json.load(file)
+
+    idx = 1
+    for cell in data["cells"]:
+        if cell["cell_type"] == "code":
+            cell["execution_count"] = idx
+            if isinstance(cell["source"], list):
+                cell["source"] = "".join(cell["source"])
+            cell["source"] = format_cell(cell["source"])
+            idx += 1
+            if cell["source"].endswith("\n"):
+                cell["source"] = cell["source"][:-1]
+
+    with open(file_name, "w") as file:
+        file.write(json.dumps(data, indent=4))
+    print("Formatted", file_name)
+
+
+if __name__ == "__main__":
+    files = [
+        file
+        for file in glob.glob("**/*.ipynb", recursive=True)
+        if file.split("/")[-1] not in IGNORE
+    ]
+    with ProcessPoolExecutor(10) as pool:
+        pool.map(format_file, files)
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index a12f8176c..8a5cb690a 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -14,9 +14,7 @@
 """
 
 COLAB_TEMPLATE = "https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/{filename}"
-GITHUB_TEMPLATE = (
-    "https://github.com/Labelbox/labelbox-python/tree/develop/examples/{filename}"
-)
+GITHUB_TEMPLATE = "https://github.com/Labelbox/labelbox-python/tree/develop/examples/{filename}"
 
 
 def create_header(link: str) -> str:
@@ -103,7 +101,9 @@ def make_table(base: str = "") -> str:
     Returns:
         str: markdown string file
     """
-    link_dict = make_links_dict(glob.glob("**/examples/**/*.ipynb", recursive=True))
+    link_dict = make_links_dict(
+        glob.glob("**/examples/**/*.ipynb", recursive=True)
+    )
     generated_markdown = base
     for link_list in link_dict.values():
         pandas_dict = {"Notebook": [], "Github": [], "Google Colab": []}
@@ -112,14 +112,18 @@ def make_table(base: str = "") -> str:
             pandas_dict["Notebook"].append(create_title(link))
             pandas_dict["Github"].append(
                 make_link(
-                    GITHUB_TEMPLATE.format(filename="/".join(link.split("/")[1:])),
+                    GITHUB_TEMPLATE.format(
+                        filename="/".join(link.split("/")[1:])
+                    ),
                     "https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white",
                     "Github",
                 )
             )
             pandas_dict["Google Colab"].append(
                 make_link(
-                    COLAB_TEMPLATE.format(filename="/".join(link.split("/")[1:])),
+                    COLAB_TEMPLATE.format(
+                        filename="/".join(link.split("/")[1:])
+                    ),
                     "https://colab.research.google.com/assets/colab-badge.svg",
                     "Colab",
                 )
diff --git a/examples/scripts/upload_documentation.py b/examples/scripts/upload_documentation.py
deleted file mode 100644
index ea987f6b7..000000000
--- a/examples/scripts/upload_documentation.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import glob
-import json
-import os
-
-import click
-import nbformat
-import requests
-from nbconvert import MarkdownExporter
-
-README_AUTH = os.getenv('README_AUTH')
-README_ENDPOINT = "https://dash.readme.com/api/v1/docs"
-README_DOC_ENDPOINT = "https://dash.readme.com/api/v1/docs/"
-CATEGORY_ID = '61fb645198ad91004246bd5f'
-CATEGORY_SLUG = 'tutorials'
-
-
-def upload_doc(path, section):
-    title = path.split('/')[-1].replace(".ipynb",
-                                        '').capitalize().replace('_', ' ')
-
-    with open(path) as fb:
-        nb = nbformat.reads(json.dumps(json.load(fb)), as_version=4)
-
-    nb.cells = [nb.cells[1]] + nb.cells[3:]
-    nb.cells[0]['source'] += '\n'
-    exporter = MarkdownExporter()
-
-    body, resources = exporter.from_notebook_node(nb)
-
-    payload = {
-        "hidden":
-            True,
-        "title":
-            f'{section["slug"]}-' + title.replace(' ', '-').replace("(", ''),
-        "category":
-            CATEGORY_ID,
-        "parentDoc":
-            section['id'],
-        "body":
-            body
-    }
-
-    headers = {
-        "Accept": "application/json",
-        "Content-Type": "application/json",
-        "Authorization": README_AUTH
-    }
-
-    response = requests.post(README_ENDPOINT, json=payload, headers=headers)
-    response.raise_for_status()
-    data = response.json()
-    change_name(data['slug'], title, headers, hidden=False)
-
-
-def make_sections(sections):
-    for section in sections:
-        print(section)
-        payload = {
-            "hidden": True,
-            "order": section['order'],
-            "title": 'section-' + section['dir'] + '-notebooks',
-            "category": CATEGORY_ID
-        }
-        headers = {
-            "Accept": "application/json",
-            "Content-Type": "application/json",
-            "Authorization": README_AUTH
-        }
-
-        response = requests.post(README_ENDPOINT, json=payload, headers=headers)
-        data = response.json()
-
-        section['id'] = data['id']
-        section['slug'] = data['slug']
-
-        change_name(data["slug"], section['title'], headers, False)
-
-    return sections
-
-
-def change_name(slug, title, headers, hidden=True):
-    resp = requests.put(f'{README_DOC_ENDPOINT}/{slug}',
-                        json={
-                            "hidden": hidden,
-                            "title": title,
-                            "category": CATEGORY_ID
-                        },
-                        headers=headers)
-    resp.raise_for_status()
-
-
-def erase_category_docs(cat_slug):
-    headers = {"Accept": "application/json", "Authorization": README_AUTH}
-
-    response = requests.request(
-        "GET",
-        f'https://dash.readme.com/api/v1/categories/{cat_slug}/docs',
-        headers=headers)
-    docs = response.json()
-    for doc in docs:
-        for child in doc["children"]:
-            resp = requests.delete(f'{README_DOC_ENDPOINT}/{child["slug"]}',
-                                   headers=headers)
-        resp = requests.delete(f'{README_DOC_ENDPOINT}/{doc["slug"]}',
-                               headers=headers)
-
-
-@click.command()
-@click.option('--config-path')
-# @click.option('--output-path')
-def main(config_path):
-    # print(input_path)
-    erase_category_docs(CATEGORY_SLUG)
-    with open(config_path) as fb:
-        config = json.load(fb)
-        config = make_sections(config)
-
-    for section in config:
-        print(section, '\n------')
-        for path in glob.glob(f'{section["dir"]}/**.ipynb'):
-            print('*', path)
-            upload_doc(path, section)
-        print('-------')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/pyproject.toml b/pyproject.toml
index ecd80c176..82c6d2f71 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,9 +2,7 @@
 name = "labelbox-python"
 version = "0.1.0"
 description = "Labelbox Python API"
-authors = [
-    { name = "Labelbox", email = "engineering@labelbox.com" }
-]
+authors = [{ name = "Labelbox", email = "engineering@labelbox.com" }]
 dependencies = [
     "sphinx-multiproject>=1.0.0rc1",
     "sphinx>=7.1.2",
@@ -29,7 +27,7 @@ dev-dependencies = [
 ]
 
 [tool.rye.workspace]
-members = ["libs/*"]
+members = ["libs/*", "examples"]
 
 [tool.pytest.ini_options]
 addopts = "-rP -vvv --reruns 2 --reruns-delay 10 --durations=20 -n auto --cov=labelbox --import-mode=importlib"
@@ -38,4 +36,4 @@ markers = """
 """
 
 [tool.rye.scripts]
-docs = "sphinx-build ./docs ./dist"
\ No newline at end of file
+docs = "sphinx-build ./docs ./dist"

From 1dd2d7413dcc837f0e5d279514193a5f3d3f025e Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 15:47:48 -0500
Subject: [PATCH 02/80] removed unneeded files

---
 examples/.python-version                      |   1 +
 examples/Makefile                             |   3 -
 .../2560px-Kitano_Street_Kobe01s5s4110.jpg    | Bin 1445451 -> 0 bytes
 examples/assets/mapillary_sample.csv          | 100 ------------------
 examples/assets/ti_basics.png                 | Bin 2573693 -> 0 bytes
 examples/format_notebooks.py                  |  90 ----------------
 examples/test_notebooks.py                    |  74 -------------
 7 files changed, 1 insertion(+), 267 deletions(-)
 create mode 100644 examples/.python-version
 delete mode 100644 examples/Makefile
 delete mode 100644 examples/assets/2560px-Kitano_Street_Kobe01s5s4110.jpg
 delete mode 100644 examples/assets/mapillary_sample.csv
 delete mode 100644 examples/assets/ti_basics.png
 delete mode 100644 examples/format_notebooks.py
 delete mode 100644 examples/test_notebooks.py

diff --git a/examples/.python-version b/examples/.python-version
new file mode 100644
index 000000000..9ad6380c1
--- /dev/null
+++ b/examples/.python-version
@@ -0,0 +1 @@
+3.8.18
diff --git a/examples/Makefile b/examples/Makefile
deleted file mode 100644
index e815dbe42..000000000
--- a/examples/Makefile
+++ /dev/null
@@ -1,3 +0,0 @@
-
-upload:
-	python scripts/upload_documentation.py --config-path  'scripts/config.json'
\ No newline at end of file
diff --git a/examples/assets/2560px-Kitano_Street_Kobe01s5s4110.jpg b/examples/assets/2560px-Kitano_Street_Kobe01s5s4110.jpg
deleted file mode 100644
index 456d3fd3b8c75be4e0f67a85eda7254935e6f57a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/examples/assets/mapillary_sample.csv b/examples/assets/mapillary_sample.csv
deleted file mode 100644
index 39946b292..000000000
--- a/examples/assets/mapillary_sample.csv
+++ /dev/null
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Ef<ZWo_W%F@

diff --git a/examples/format_notebooks.py b/examples/format_notebooks.py
deleted file mode 100644
index 11b63f999..000000000
--- a/examples/format_notebooks.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import glob
-import json
-from copy import deepcopy
-
-from yapf.yapflib.yapf_api import FormatCode
-
-BANNER_CELL = {
-    "cell_type": "markdown",
-    "id": "db768cda",
-    "metadata": {},
-    "source": [
-        "<td>",
-        '   <a target="_blank" href="https://labelbox.com" ><img src="https://labelbox.com/blog/content/images/2021/02/logo-v4.svg" width=256/></a>',
-        "</td>",
-    ],
-}
-
-LINK_CELL = {
-    "cell_type": "markdown",
-    "id": "cb5611d0",
-    "metadata": {},
-    "source": [
-        "<td>",
-        '<a href="{colab}" target="_blank"><img',
-        'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>',
-        "</td>",
-        "\n",
-        "<td>",
-        '<a href="{github}" target="_blank"><img',
-        'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>',
-        "</td>",
-    ],
-}
-
-COLAB_TEMPLATE = "https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/{filename}"
-GITHUB_TEMPLATE = (
-    "https://github.com/Labelbox/labelbox-python/tree/develop/examples/{filename}"
-)
-
-
-def format_cell(source):
-    for line in source.split("\n"):
-        if line.strip().startswith(("!", "%")):
-            return source
-    return FormatCode(source, style_config="google")[0]
-
-
-def add_headers(file_name):
-    with open(file_name, "r") as file:
-        data = json.load(file)
-
-    colab_path = COLAB_TEMPLATE.format(filename=file_name)
-    github_path = GITHUB_TEMPLATE.format(filename=file_name)
-
-    link_cell = deepcopy(LINK_CELL)
-
-    link_cell["source"][1] = link_cell["source"][1].format(colab=colab_path)
-    link_cell["source"][6] = link_cell["source"][6].format(github=github_path)
-
-    data["cells"] = [BANNER_CELL, link_cell] + data["cells"]
-
-    with open(file_name, "w") as file:
-        file.write(json.dumps(data, indent=4))
-
-    print("Formatted", file_name)
-
-
-def format_file(file_name):
-    with open(file_name, "r") as file:
-        data = json.load(file)
-
-    idx = 1
-    for cell in data["cells"]:
-        if cell["cell_type"] == "code":
-            cell["execution_count"] = idx
-            if isinstance(cell["source"], list):
-                cell["source"] = "".join(cell["source"])
-            cell["source"] = format_cell(cell["source"])
-            idx += 1
-            if cell["source"].endswith("\n"):
-                cell["source"] = cell["source"][:-1]
-
-    with open(file_name, "w") as file:
-        file.write(json.dumps(data, indent=4))
-    print("Formatted", file_name)
-
-
-if __name__ == "__main__":
-    for file in glob.glob("**/*.ipynb", recursive=True):
-        format_file(file)
diff --git a/examples/test_notebooks.py b/examples/test_notebooks.py
deleted file mode 100644
index 96ec06c78..000000000
--- a/examples/test_notebooks.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-Runs example notebooks to ensure that they are not throwing an error. 
-"""
-
-import pathlib
-import pytest
-
-import nbformat
-from nbconvert.preprocessors import ExecutePreprocessor
-
-examples_path = pathlib.Path(__file__).parent
-notebook_paths = examples_path.glob('**/*.ipynb')
-filtered_notebook_paths = [
-    path for path in notebook_paths if '.ipynb_checkpoints' not in str(path)
-]
-relative_notebook_paths = [
-    str(p.relative_to(examples_path)) for p in filtered_notebook_paths
-]
-
-
-def run_notebook(filename):
-    with open(filename) as ff:
-        nb_in = nbformat.read(ff, nbformat.NO_CONVERT)
-
-    ep = ExecutePreprocessor(timeout=1200, kernel_name='python3')
-
-    ep.preprocess(nb_in)
-
-
-SKIP_LIST = [
-    'extras/classification-confusion-matrix.ipynb',
-    'label_export/images.ipynb',
-    'label_export/text.ipynb',
-    'label_export/video.ipynb',
-    'annotation_types/converters.ipynb',
-    'integrations/detectron2/coco_panoptic.ipynb',
-    'integrations/tlt/detectnet_v2_bounding_box.ipynb',
-    'basics/datasets.ipynb',
-    'basics/data_rows.ipynb',
-    'basics/labels.ipynb',
-    'basics/data_row_metadata.ipynb',
-    'model_diagnostics/custom_metrics_basics.ipynb',
-    'basics/user_management.ipynb',
-    'integrations/tlt/labelbox_upload.ipynb',
-    'model_diagnostics/custom_metrics_demo.ipynb',
-    'model_diagnostics/model_diagnostics_demo.ipynb',
-    'integrations/databricks/',
-    'integrations/detectron2/coco_object.ipynb',
-    'project_configuration/webhooks.ipynb',
-    'basics/projects.ipynb',
-    'model_diagnostics/model_diagnostics_guide.ipynb',
-]
-
-
-def skip_notebook(notebook_path):
-    for skip_path in SKIP_LIST:
-        if notebook_path.startswith(skip_path):
-            return True
-    return False
-
-
-run_notebook_paths = [
-    path for path in relative_notebook_paths if not skip_notebook(path)
-]
-
-
-@pytest.mark.skip(
-    'Need some more work to run reliably, e.g. figuring out how to deal with '
-    'max number of models per org, therefore skipping from CI. However, this '
-    'test can be run locally after updating notebooks to ensure notebooks '
-    'are working.')
-@pytest.mark.parametrize("notebook_path", run_notebook_paths)
-def test_notebooks_run_without_errors(notebook_path):
-    run_notebook(examples_path / notebook_path)

From f956e3e862a36bfef2a59d736fcc1d7cadd88320 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 18:40:26 -0500
Subject: [PATCH 03/80] worked on format file script

---
 examples/scripts/format_notebooks.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index 0050609d7..d3550c53b 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -2,7 +2,7 @@
 import json
 from copy import deepcopy
 from concurrent.futures import ProcessPoolExecutor
-import multiprocessing
+import string
 
 from yapf.yapflib.yapf_api import FormatCode
 
@@ -43,7 +43,7 @@
 def format_cell(source):
     for line in source.split("\n"):
         if line.strip().startswith(("!", "%")):
-            return source
+            return source.replace("!", "%")
     return FormatCode(source, style_config="google")[0]
 
 
@@ -71,14 +71,21 @@ def format_file(file_name):
     with open(file_name, "r") as file:
         data = json.load(file)
 
-    idx = 1
+    colab_path = COLAB_TEMPLATE.format(filename=file_name)
+    github_path = GITHUB_TEMPLATE.format(filename=file_name)
+
+    link_cell = deepcopy(LINK_CELL)
+
+    link_cell["source"][1] = link_cell["source"][1].format(colab=colab_path)
+    link_cell["source"][6] = link_cell["source"][6].format(github=github_path)
+
+    data["cells"] = [BANNER_CELL, link_cell] + data["cells"][2:]
+
     for cell in data["cells"]:
         if cell["cell_type"] == "code":
-            cell["execution_count"] = idx
             if isinstance(cell["source"], list):
                 cell["source"] = "".join(cell["source"])
             cell["source"] = format_cell(cell["source"])
-            idx += 1
             if cell["source"].endswith("\n"):
                 cell["source"] = cell["source"][:-1]
 

From 062b536da0277dacc593cbf3ce4902cca6b6013d Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:26:45 -0500
Subject: [PATCH 04/80] added workflows finished everything up

---
 .../workflows/notebooks/actions/actions.yml   | 28 ++++++++
 .github/workflows/notebooks/create-readme.yml | 57 +++++++++++++++
 .github/workflows/notebooks/format.yml        | 43 ++++++++++++
 examples/pyproject.toml                       |  9 ++-
 examples/scripts/format_notebooks.py          | 27 ++-----
 examples/scripts/generate_readme.py           | 28 +++++---
 requirements-dev.lock                         | 70 +++++++++++++++++++
 7 files changed, 229 insertions(+), 33 deletions(-)
 create mode 100644 .github/workflows/notebooks/actions/actions.yml
 create mode 100644 .github/workflows/notebooks/create-readme.yml
 create mode 100644 .github/workflows/notebooks/format.yml

diff --git a/.github/workflows/notebooks/actions/actions.yml b/.github/workflows/notebooks/actions/actions.yml
new file mode 100644
index 000000000..50bfe552e
--- /dev/null
+++ b/.github/workflows/notebooks/actions/actions.yml
@@ -0,0 +1,28 @@
+name: Labelbox Example Notebook Setup
+
+inputs:
+  rye-version:
+    required: true
+  python-version:
+    required: true
+    
+runs:
+  using: "composite"
+  steps:
+    - name: Install the latest version of rye
+      uses: eifinger/setup-rye@v2
+      with:
+        version: ${{ inputs.rye-version }}
+        enable-cache: true
+    - name: Rye Setup
+      shell: bash
+      run: |
+        rye config --set-bool behavior.use-uv=true
+    - name: Python setup
+      shell: bash
+      run: rye pin ${{ inputs.python-version }}
+    - name: Environment setup
+      working-directory: examples
+      shell: bash
+      run: | 
+        rye sync -f --update-all
\ No newline at end of file
diff --git a/.github/workflows/notebooks/create-readme.yml b/.github/workflows/notebooks/create-readme.yml
new file mode 100644
index 000000000..3403a5c9c
--- /dev/null
+++ b/.github/workflows/notebooks/create-readme.yml
@@ -0,0 +1,57 @@
+name: Labelbox Example Notebook Update Readme
+
+on:
+  push:
+    branches: [develop]
+    paths:
+      - examples/**
+  pull_request:
+    branches: [develop]
+    paths:
+      - examples/**
+
+permissions:
+  contents: write
+  pull-requests: write
+  
+jobs:
+  if: github.event.pull_request.merged == False
+  changes:
+    runs-on: ubuntu-latest
+    # Set job outputs to values from filter step
+    outputs:
+      addedOrModified: ${{ steps.filter.outputs.addedOrModified }}
+    steps:
+    - uses: dorny/paths-filter@v3
+      id: filter
+      with:
+        filters: |
+          addedOrModified:
+            - added|deleted: 'examples/**.ipynb'
+  update:
+    needs: changes
+    if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ github.head_ref }}
+          fetch-depth: 0
+      - uses: ./.github/workflows/actions
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: ${{ inputs.python-version }}
+      - name: Create readme
+        working-directory: examples
+        run: rye run create-readme
+      - name: Commit changes
+        run: |
+            git config --local user.email "github-actions[bot]@users.noreply.github.com"
+            git config --local user.name "github-actions[bot]"
+            git add examples/.
+            git commit -m ":memo: README updated" || exit 0
+      - name: Push changes
+        uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          branch: ${{ github.head_ref }}
\ No newline at end of file
diff --git a/.github/workflows/notebooks/format.yml b/.github/workflows/notebooks/format.yml
new file mode 100644
index 000000000..c1b8381e7
--- /dev/null
+++ b/.github/workflows/notebooks/format.yml
@@ -0,0 +1,43 @@
+name: Labelbox Example Notebook Format
+
+on:
+  push:
+    branches: [develop]
+    paths:
+      - examples/**
+  pull_request:
+    branches: [develop]
+    paths:
+      - examples/**
+
+permissions:
+  contents: write
+  pull-requests: write
+  
+jobs:
+  if: github.event.pull_request.merged == False
+  update:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ github.head_ref }}
+          fetch-depth: 0
+      - uses: ./.github/workflows/actions
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: ${{ inputs.python-version }}
+      - name: Format
+        working-directory: examples
+        run: rye run clean
+      - name: Commit changes
+        run: |
+            git config --local user.email "github-actions[bot]@users.noreply.github.com"
+            git config --local user.name "github-actions[bot]"
+            git add examples/.
+            git commit -m ":art: Cleaned" || exit 0
+      - name: Push changes
+        uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          branch: ${{ github.head_ref }}
\ No newline at end of file
diff --git a/examples/pyproject.toml b/examples/pyproject.toml
index 18673b01f..90a640780 100644
--- a/examples/pyproject.toml
+++ b/examples/pyproject.toml
@@ -4,12 +4,13 @@ version = "0.1.0"
 description = "Labelbox Python Example Notebooks"
 authors = [{ name = "Labelbox", email = "support@labelbox.com" }]
 readme = "README.md"
+# Python version matches labelbox SDK
 requires-python = ">=3.8"
-dependencies = ["jupyter>=1.0.0"]
+dependencies = []
 
 [project.urls]
 Homepage = "https://labelbox.com/"
-Documentation = "https://labelbox-python.readthedocs.io/en/latest/"
+Documentation = "https://docs.labelbox.com/"
 Tutorials = "https://docs.labelbox.com/page/tutorials"
 Repository = "https://github.com/Labelbox/labelbox-python/examples"
 Issues = "https://github.com/Labelbox/labelbox-python/issues"
@@ -21,6 +22,8 @@ dev-dependencies = [
     "yapf>=0.40.2",
     "black[jupyter]>=24.4.2",
     "databooks>=1.3.10",
+    # higher versions dont support python 3.8
+    "pandas==1.5.3",
 ]
 
 [tool.rye.scripts]
@@ -28,3 +31,5 @@ clean = { chain = ["clean:databooks", "clean:black", "clean:format"] }
 "clean:databooks" = 'databooks meta --no-rm-exec . --yes'
 "clean:black" = 'black --line-length 80 .'
 "clean:format" = { call = "scripts.format_notebooks" }
+"create-readme" = { call = "scripts.generate_readme:main(True)" }
+"create-doc-readme" = { call = "scripts.generate_readme:main(False)" }
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index d3550c53b..274b2ddfb 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -2,10 +2,12 @@
 import json
 from copy import deepcopy
 from concurrent.futures import ProcessPoolExecutor
-import string
-
 from yapf.yapflib.yapf_api import FormatCode
 
+"""
+Script used to format notebooks. To mass change any links or headers cells change the variables below. Hooks into github action CI tools.
+"""
+
 IGNORE = ["template.ipynb"]
 
 BANNER_CELL = {
@@ -46,27 +48,6 @@ def format_cell(source):
             return source.replace("!", "%")
     return FormatCode(source, style_config="google")[0]
 
-
-def add_headers(file_name):
-    with open(file_name, "r") as file:
-        data = json.load(file)
-
-    colab_path = COLAB_TEMPLATE.format(filename=file_name)
-    github_path = GITHUB_TEMPLATE.format(filename=file_name)
-
-    link_cell = deepcopy(LINK_CELL)
-
-    link_cell["source"][1] = link_cell["source"][1].format(colab=colab_path)
-    link_cell["source"][6] = link_cell["source"][6].format(github=github_path)
-
-    data["cells"] = [BANNER_CELL, link_cell] + data["cells"]
-
-    with open(file_name, "w") as file:
-        file.write(json.dumps(data, indent=4))
-
-    print("Formatted", file_name)
-
-
 def format_file(file_name):
     with open(file_name, "r") as file:
         data = json.load(file)
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 8a5cb690a..04dccaddb 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -1,6 +1,7 @@
 import pandas
 import glob
 from collections import defaultdict
+import argparse
 
 """
 Script used to generate readme programmatically works by taking the links of all the notebooks
@@ -27,7 +28,7 @@ def create_header(link: str) -> str:
         str: formatted file path for header
     """
     # Splits up link uses directory name
-    split_link = link.split("/")[1].replace("_", " ").split(" ")
+    split_link = link.split("/")[0].replace("_", " ").split(" ")
     header = []
 
     # Capitalize first letter of each word
@@ -73,10 +74,10 @@ def make_link(link: str, photo: str, link_type: str) -> str:
     Returns:
         str: anchor tag with image
     """
-    return f'<a href="{link}" target="_blank"><img src="{photo}" alt="Open In {link_type}" onclick="(function prevent(e){{e.preventDefault()}}()"></a>'
+    return f'<a href="{link}" target="_blank"><img src="{photo}" alt="Open In {link_type}"></a>'
 
 
-def make_links_dict(links: str) -> defaultdict[list]:
+def make_links_dict(links: str):
     """Creates dictionary needed for pandas to generate the table takes all the links and makes each directory its own table
 
     Args:
@@ -87,7 +88,7 @@ def make_links_dict(links: str) -> defaultdict[list]:
     """
     link_dict = defaultdict(list)
     for link in links:
-        split_link = link.split("/")[1]
+        split_link = link.split("/")[0]
         link_dict[split_link].append(link)
     return link_dict
 
@@ -102,7 +103,7 @@ def make_table(base: str = "") -> str:
         str: markdown string file
     """
     link_dict = make_links_dict(
-        glob.glob("**/examples/**/*.ipynb", recursive=True)
+        glob.glob("**/*.ipynb", recursive=True)
     )
     generated_markdown = base
     for link_list in link_dict.values():
@@ -130,9 +131,20 @@ def make_table(base: str = "") -> str:
             )
         df = pandas.DataFrame(pandas_dict)
         generated_markdown += f"{df.to_html(col_space={'Notebook':400}, index=False, escape=False, justify='left')}\n\n"
-    return generated_markdown
+    return f"{generated_markdown.rstrip()}\n"
 
+def main(github: bool):
+    """
+    Args:
+        github (bool): if this is the readme for github. 
+    """
+    if github:
+        with open("./README.md", "w") as readme:
+            readme.write(make_table(SDK_EXAMPLE_HEADER))
+    else:
+        with open("./tmp.html", "w") as readme:
+            readme.write(make_table())
 
 if __name__ == "__main__":
-    with open("./examples/README.md", "w") as readme:
-        readme.write(f"{make_table(SDK_EXAMPLE_HEADER).rstrip()}\n")
+    main(True)
+
diff --git a/requirements-dev.lock b/requirements-dev.lock
index 67971060f..c2b2a6b2b 100644
--- a/requirements-dev.lock
+++ b/requirements-dev.lock
@@ -12,13 +12,20 @@ alabaster==0.7.13
     # via sphinx
 annotated-types==0.6.0
     # via pydantic
+appnope==0.1.4
+    # via ipython
+asttokens==2.4.1
+    # via stack-data
 attrs==23.2.0
     # via jsonschema
     # via referencing
 babel==2.14.0
     # via sphinx
+backcall==0.2.0
+    # via ipython
 beautifulsoup4==4.12.3
     # via nbconvert
+black==24.4.2
 bleach==6.1.0
     # via nbconvert
 cachetools==5.3.3
@@ -28,11 +35,17 @@ certifi==2024.2.2
 charset-normalizer==3.3.2
     # via requests
 click==8.1.7
+    # via black
     # via typer
+commonmark==0.9.1
+    # via rich
 coverage==7.4.4
     # via pytest-cov
+databooks==1.3.10
 decopatch==1.4.10
     # via pytest-cases
+decorator==5.1.1
+    # via ipython
 defusedxml==0.7.1
     # via nbconvert
 docutils==0.20.1
@@ -42,10 +55,16 @@ exceptiongroup==1.2.0
     # via pytest
 execnet==2.0.2
     # via pytest-xdist
+executing==2.0.1
+    # via stack-data
 fastjsonschema==2.19.1
     # via nbformat
 geojson==3.1.0
     # via labelbox
+gitdb==4.0.11
+    # via gitpython
+gitpython==3.1.43
+    # via databooks
 google-api-core==2.17.1
     # via labelbox
 google-auth==2.28.2
@@ -66,6 +85,10 @@ importlib-resources==6.3.1
     # via jsonschema-specifications
 iniconfig==2.0.0
     # via pytest
+ipython==8.12.3
+    # via black
+jedi==0.19.1
+    # via ipython
 jinja2==3.1.3
     # via nbconvert
     # via sphinx
@@ -88,10 +111,13 @@ makefun==1.15.2
 markupsafe==2.1.5
     # via jinja2
     # via nbconvert
+matplotlib-inline==0.1.7
+    # via ipython
 mistune==3.0.2
     # via nbconvert
 mypy==1.9.0
 mypy-extensions==1.0.0
+    # via black
     # via mypy
 nbclient==0.10.0
     # via nbconvert
@@ -99,35 +125,57 @@ nbconvert==7.16.2
 nbformat==5.10.3
     # via nbclient
     # via nbconvert
+numpy==1.24.4
+    # via pandas
 packaging==24.0
+    # via black
     # via nbconvert
     # via pytest
     # via pytest-cases
     # via pytest-rerunfailures
     # via sphinx
+pandas==1.5.3
 pandocfilters==1.5.1
     # via nbconvert
+parso==0.8.4
+    # via jedi
+pathspec==0.12.1
+    # via black
+pexpect==4.9.0
+    # via ipython
+pickleshare==0.7.5
+    # via ipython
 pkgutil-resolve-name==1.3.10
     # via jsonschema
 platformdirs==4.2.0
+    # via black
     # via jupyter-core
     # via yapf
 pluggy==1.4.0
     # via pytest
+prompt-toolkit==3.0.43
+    # via ipython
 protobuf==4.25.3
     # via google-api-core
     # via googleapis-common-protos
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
 pyasn1==0.5.1
     # via pyasn1-modules
     # via rsa
 pyasn1-modules==0.3.0
     # via google-auth
 pydantic==2.6.4
+    # via databooks
     # via labelbox
 pydantic-core==2.16.3
     # via pydantic
 pygments==2.17.2
+    # via ipython
     # via nbconvert
+    # via rich
     # via sphinx
 pytest==8.1.1
     # via pytest-cov
@@ -142,8 +190,10 @@ pytest-xdist==3.5.0
 python-dateutil==2.8.2
     # via jupyter-client
     # via labelbox
+    # via pandas
 pytz==2024.1
     # via babel
+    # via pandas
 pyzmq==25.1.2
     # via jupyter-client
 referencing==0.34.0
@@ -155,14 +205,19 @@ requests==2.31.0
     # via google-api-core
     # via labelbox
     # via sphinx
+rich==12.6.0
+    # via databooks
 rpds-py==0.18.0
     # via jsonschema
     # via referencing
 rsa==4.9
     # via google-auth
 six==1.16.0
+    # via asttokens
     # via bleach
     # via python-dateutil
+smmap==5.0.1
+    # via gitdb
 snowballstemmer==2.2.0
     # via sphinx
 soupsieve==2.5
@@ -186,13 +241,19 @@ sphinxcontrib-qthelp==1.0.3
     # via sphinx
 sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
+stack-data==0.6.3
+    # via ipython
 strenum==0.4.15
     # via labelbox
 tinycss2==1.2.1
     # via nbconvert
+tokenize-rt==5.2.0
+    # via black
 toml-cli==0.6.0
 tomli==2.0.1
+    # via black
     # via coverage
+    # via databooks
     # via mypy
     # via pytest
     # via yapf
@@ -203,12 +264,15 @@ tornado==6.4
 tqdm==4.66.2
     # via labelbox
 traitlets==5.14.2
+    # via ipython
     # via jupyter-client
     # via jupyter-core
+    # via matplotlib-inline
     # via nbclient
     # via nbconvert
     # via nbformat
 typer==0.9.0
+    # via databooks
     # via toml-cli
 types-pillow==10.2.0.20240311
 types-python-dateutil==2.9.0.20240316
@@ -216,13 +280,19 @@ types-requests==2.31.0.20240311
 types-tqdm==4.66.0.20240106
 typing-extensions==4.10.0
     # via annotated-types
+    # via black
+    # via databooks
+    # via ipython
     # via mypy
     # via pydantic
     # via pydantic-core
+    # via rich
     # via typer
 urllib3==2.2.1
     # via requests
     # via types-requests
+wcwidth==0.2.13
+    # via prompt-toolkit
 webencodings==0.5.1
     # via bleach
     # via tinycss2

From 0dfabb43b243700b9e180478820a4697360feaaa Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:32:34 -0500
Subject: [PATCH 05/80] moved workflow around

---
 .../actions => actions/notebooks}/actions.yml |  0
 ...-readme.yml => notebook-create-readme.yml} |  2 +-
 .../format.yml => notebook-format.yml}        |  2 +-
 .github/workflows/notebooks.yml               | 39 -------------------
 4 files changed, 2 insertions(+), 41 deletions(-)
 rename .github/{workflows/notebooks/actions => actions/notebooks}/actions.yml (100%)
 rename .github/workflows/{notebooks/create-readme.yml => notebook-create-readme.yml} (97%)
 rename .github/workflows/{notebooks/format.yml => notebook-format.yml} (96%)
 delete mode 100644 .github/workflows/notebooks.yml

diff --git a/.github/workflows/notebooks/actions/actions.yml b/.github/actions/notebooks/actions.yml
similarity index 100%
rename from .github/workflows/notebooks/actions/actions.yml
rename to .github/actions/notebooks/actions.yml
diff --git a/.github/workflows/notebooks/create-readme.yml b/.github/workflows/notebook-create-readme.yml
similarity index 97%
rename from .github/workflows/notebooks/create-readme.yml
rename to .github/workflows/notebook-create-readme.yml
index 3403a5c9c..b78f6cfb9 100644
--- a/.github/workflows/notebooks/create-readme.yml
+++ b/.github/workflows/notebook-create-readme.yml
@@ -37,7 +37,7 @@ jobs:
         with:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
-      - uses: ./.github/workflows/actions
+      - uses: ./.github/actions/notebooks
         with:
           rye-version: ${{ vars.RYE_VERSION }}
           python-version: ${{ inputs.python-version }}
diff --git a/.github/workflows/notebooks/format.yml b/.github/workflows/notebook-format.yml
similarity index 96%
rename from .github/workflows/notebooks/format.yml
rename to .github/workflows/notebook-format.yml
index c1b8381e7..6269331dd 100644
--- a/.github/workflows/notebooks/format.yml
+++ b/.github/workflows/notebook-format.yml
@@ -23,7 +23,7 @@ jobs:
         with:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
-      - uses: ./.github/workflows/actions
+      - uses: ./.github/actions/notebooks
         with:
           rye-version: ${{ vars.RYE_VERSION }}
           python-version: ${{ inputs.python-version }}
diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
deleted file mode 100644
index 1e3977a80..000000000
--- a/.github/workflows/notebooks.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-name: Labelbox Python SDK Notebooks (Develop)
-
-on:
-  push:
-    branches: [develop]
-    paths:
-      - examples/**
-  pull_request:
-    branches: [develop]
-    paths:
-      - examples/**
-
-permissions:
-  contents: write
-  pull-requests: write
-  
-jobs:
-  update:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
-          fetch-depth: 0
-      - name: Install databooks
-        run: pip install databooks
-      - name: Remove notebook metadata
-        run: echo "y" | databooks meta examples/. --rm-outs
-      - name: Commit changes
-        run: |
-            git config --local user.email "github-actions[bot]@users.noreply.github.com"
-            git config --local user.name "github-actions[bot]"
-            git add examples/.
-            git commit -m "Removed metadata" || exit 0
-      - name: Push changes
-        uses: ad-m/github-push-action@master
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          branch: ${{ github.head_ref }}

From 4917afcbddb6baa8fa3886d92d48eaa9da6db9f4 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:35:16 -0500
Subject: [PATCH 06/80] added some notebooks to trigger

---
 examples/project_configuration/README.md      |   2 -
 .../project_configuration/project_setup.ipynb | 416 ++++++-------
 .../queue_management.ipynb                    | 570 +++++++-----------
 examples/project_configuration/webhooks.ipynb | 552 +++++++----------
 4 files changed, 592 insertions(+), 948 deletions(-)
 delete mode 100644 examples/project_configuration/README.md

diff --git a/examples/project_configuration/README.md b/examples/project_configuration/README.md
deleted file mode 100644
index a907bd5ce..000000000
--- a/examples/project_configuration/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-- This section describes advanced usage of project configuration.
-- Make sure you are comfortable with the basics/project section first.
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index 19538c905..198e5bbdf 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -1,242 +1,178 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=190/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Project Setup\n",
-        "* This notebok describes how to create and configure a project\n",
-        "* This is the same as creating a new project in the editor and going through all of the steps."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
-        "    * An ontology must be set\n",
-        "    * A Batch must be created"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox[data]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from labelbox.schema.quality_mode import QualityMode\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY=\"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Dataset\n",
-        "* Create dataset and attach data\n",
-        "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset = client.create_dataset(name=\"project_setup_demo\")\n",
-        "global_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n",
-        "## Example image\n",
-        "uploads = []\n",
-        "# Generate data rows\n",
-        "for i in range(1,5):\n",
-        "    uploads.append({\n",
-        "        'row_data':  f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\": global_keys[i-1]\n",
-        "    })\n",
-        "task = dataset.create_data_rows(uploads)\n",
-        "task.wait_till_done()\n",
-        "print(\"ERRORS: \" , task.errors)\n",
-        "print(\"RESULT URL: \", task.result_url)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Or Select an Ontology\n",
-        "* Optionally create an ontology or select from an existing one.\n",
-        "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
-        "   "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\n",
-        "ontology = client.create_ontology(\"project-setup-demo-ontology\",\n",
-        "                                  ontology_builder.asdict())\n",
-        "# Select existing ontology\n",
-        "#ontology = client.get_ontology(\"<ontology_id>\")\n",
-        "#ontology = existing_project.ontology()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Project and Setup the Editor\n",
-        "* Setting up a project will add an ontology and will enable labeling to begin\n",
-        "* Creating batches will add all data_rows belonging to the dataset to the queue."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "batch_project = client.create_project(name=\"Project Setup Demo\",\n",
-        "                                quality_mode=QualityMode.Consensus, # For benchmarks use quality_mode = QualityMode.Benchmark\n",
-        "                                media_type=lb.MediaType.Image)\n",
-        "\n",
-        "\n",
-        "batch_project.setup_editor(ontology)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add data to your projects "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "## When creating a batch you can also setup the data rows priority\n",
-        "batch = batch_project.create_batch(\n",
-        "  \"batch-demo-4\", # Each batch in a project must have a unique name\n",
-        "  global_keys=global_keys, # A list of data rows or data row ids\n",
-        "  priority=5, # priority between 1(Highest) - 5(lowest)\n",
-        "  consensus_settings={\n",
-        "    \"number_of_labels\": 2,\n",
-        "    \"coverage_percentage\": 1\n",
-        "  }\n",
-        ")\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Review"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Note setup_complete will be None if it fails.\n",
-        "print(batch_project.setup_complete)\n",
-        "print(batch_project.ontology())\n",
-        "print([ds.name for ds in batch_project.batches()])\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# batch_project.delete()\n",
-        "# dataset_project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Project Setup\n",
+                "* This notebok describes how to create and configure a project\n",
+                "* This is the same as creating a new project in the editor and going through all of the steps."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
+                "    * An ontology must be set\n",
+                "    * A Batch must be created"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox[data]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Dataset\n",
+                "* Create dataset and attach data\n",
+                "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Or Select an Ontology\n",
+                "* Optionally create an ontology or select from an existing one.\n",
+                "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
+                "   "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Project and Setup the Editor\n",
+                "* Setting up a project will add an ontology and will enable labeling to begin\n",
+                "* Creating batches will add all data_rows belonging to the dataset to the queue."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Add data to your projects "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Review"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/queue_management.ipynb b/examples/project_configuration/queue_management.ipynb
index 26a03b14c..fd5fad0dc 100644
--- a/examples/project_configuration/queue_management.ipynb
+++ b/examples/project_configuration/queue_management.ipynb
@@ -1,366 +1,208 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/static/images/logo-v4.svg\" width=190/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Queue Management"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* The queue is used to task labelers with specific assets\n",
-        "* We can do any of the following:\n",
-        "    * Set quality settings\n",
-        "    * Set the order of items in the queue\n",
-        "    * Set the percent of assets to review"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "%pip install -q \"labelbox[data]\"\n",
-        "%pip install -q numpy"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from labelbox.schema.quality_mode import QualityMode\n",
-        "from uuid import uuid4\n",
-        "import json"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Set up demo project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Create project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create Labelbox  project\n",
-        "\n",
-        "project = client.create_project(\n",
-        "    name=\"batch-test-project\",\n",
-        "    description=\"a description\",\n",
-        "    quality_mode=QualityMode.\n",
-        "    Benchmark,  # For Consensus projects use quality_mode = QualityMode.Consensus\n",
-        "    media_type=lb.MediaType.Image,\n",
-        ")\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"queue_dataset\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Create ontology and attach to project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "classification_features = [\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.CHECKLIST,\n",
-        "        name=\"Quality Issues\",\n",
-        "        options=[\n",
-        "            lb.Option(value=\"blurry\", label=\"Blurry\"),\n",
-        "            lb.Option(value=\"distorted\", label=\"Distorted\"),\n",
-        "        ],\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[], classifications=classification_features\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\n",
-        "    \"Ontology from new features\",\n",
-        "    ontology_builder.asdict(),\n",
-        "    media_type=lb.MediaType.Image,\n",
-        ")\n",
-        "\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add data to your dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Example image\n",
-        "uploads = []\n",
-        "global_keys = []\n",
-        "# Generate data rows\n",
-        "for i in range(1, 5):\n",
-        "    global_key = str(uuid4())\n",
-        "    row = {\n",
-        "        \"row_data\":\n",
-        "            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\":\n",
-        "            global_key,\n",
-        "    }\n",
-        "    global_keys.append(global_key)\n",
-        "    uploads.append(row)\n",
-        "\n",
-        "data_rows = dataset.create_data_rows(uploads)\n",
-        "data_rows.wait_till_done()\n",
-        "print(\"Errors\", data_rows.errors)\n",
-        "print(\"Dataset status: \", data_rows.status)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Attach data to your project and set data row priority"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Create batches\n",
-        "\n",
-        "# Create the batch\n",
-        "\n",
-        "batch = project.create_batch(\n",
-        "    \"batch-demo\",  # Each batch in a project must have a unique name\n",
-        "    global_keys=global_keys[\n",
-        "        0:2],  # A list of data rows, data row ids or global keys\n",
-        "    priority=\n",
-        "    5,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n",
-        ")\n",
-        "\n",
-        "batch2 = project.create_batch(\n",
-        "    \"batch-demo-2\",  # Each batch in a project must have a unique name\n",
-        "    # Provide a slice of the data since you can't import assets with global keys that already exist in the project.\n",
-        "    global_keys=global_keys[\n",
-        "        2:4],  # A list of data rows, data row ids or global keys\n",
-        "    priority=\n",
-        "    1,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)\n",
-        "print(\"Batch2: \", batch2)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(\n",
-        "    \"View the results here:\", f\"https://app.labelbox.com/projects/{project.uid}\"\n",
-        ")\n",
-        "# Click `start labeling` to see the images in order"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Queue Order\n",
-        "- Add priority for each data row\n",
-        "- Update priority for each data row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_task = project.export()\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get data rows from project\n",
-        "data_rows = []\n",
-        "\n",
-        "\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "    data_row = json.loads(output.json_str)\n",
-        "    data_rows.append(\n",
-        "        lb.GlobalKey(data_row[\"data_row\"][\"global_key\"])\n",
-        "    )  # Convert json data row into data row identifier object\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "    export_task.get_stream(\n",
-        "        converter=lb.JsonConverter(), stream_type=lb.StreamType.ERRORS\n",
-        "    ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "    export_json = export_task.get_stream(\n",
-        "        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT\n",
-        "    ).start(stream_handler=json_stream_handler)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get label parameter overrides (LPOs)\n",
-        "project_lpos = project.labeling_parameter_overrides()\n",
-        "\n",
-        "for lpo in project_lpos:\n",
-        "    print(lpo)\n",
-        "    print(\"Data row:\", lpo.data_row().uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add LPOs\n",
-        "lpos = []\n",
-        "priority = 1\n",
-        "for data_row in data_rows:\n",
-        "    lpos.append((data_row, priority))\n",
-        "    priority += 1\n",
-        "\n",
-        "project.set_labeling_parameter_overrides(lpos)\n",
-        "\n",
-        "# Check results\n",
-        "project_lpos = list(project.labeling_parameter_overrides())\n",
-        "\n",
-        "for lpo in project_lpos:\n",
-        "    print(lpo)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Update LPOs\n",
-        "global_keys = []\n",
-        "for data_row in data_rows:\n",
-        "    global_keys.append(data_row.key)\n",
-        "\n",
-        "project.update_data_row_labeling_priority(\n",
-        "    data_rows=lb.GlobalKeys(global_keys), priority=1\n",
-        ")\n",
-        "\n",
-        "# Check results\n",
-        "project_lpos = list(project.labeling_parameter_overrides())\n",
-        "\n",
-        "for lpo in project_lpos:\n",
-        "    print(lpo)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Queue Management"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* The queue is used to task labelers with specific assets\n",
+                "* We can do any of the following:\n",
+                "    * Set quality settings\n",
+                "    * Set the order of items in the queue\n",
+                "    * Set the percent of assets to review"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q numpy",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nfrom uuid import uuid4\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Set up demo project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create Labelbox  project\n\nproject = client.create_project(\n    name=\"batch-test-project\",\n    description=\"a description\",\n    quality_mode=QualityMode.\n    Benchmark,  # For Consensus projects use quality_mode = QualityMode.Consensus\n    media_type=lb.MediaType.Image,\n)\n\ndataset = client.create_dataset(name=\"queue_dataset\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create ontology and attach to project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Add data to your dataset"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Example image\nuploads = []\nglobal_keys = []\n# Generate data rows\nfor i in range(1, 5):\n    global_key = str(uuid4())\n    row = {\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_key,\n    }\n    global_keys.append(global_key)\n    uploads.append(row)\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"Errors\", data_rows.errors)\nprint(\"Dataset status: \", data_rows.status)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Attach data to your project and set data row priority"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "######## Create batches\n\n# Create the batch\n\nbatch = project.create_batch(\n    \"batch-demo\",  # Each batch in a project must have a unique name\n    global_keys=global_keys[\n        0:2],  # A list of data rows, data row ids or global keys\n    priority=\n    5,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nbatch2 = project.create_batch(\n    \"batch-demo-2\",  # Each batch in a project must have a unique name\n    # Provide a slice of the data since you can't import assets with global keys that already exist in the project.\n    global_keys=global_keys[\n        2:4],  # A list of data rows, data row ids or global keys\n    priority=\n    1,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nprint(\"Batch: \", batch)\nprint(\"Batch2: \", batch2)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(\"View the results here:\",\n      f\"https://app.labelbox.com/projects/{project.uid}\")\n# Click `start labeling` to see the images in order",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Queue Order\n",
+                "- Add priority for each data row\n",
+                "- Update priority for each data row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "export_task = project.export()\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Get data rows from project\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(lb.GlobalKey(data_row[\"data_row\"][\"global_key\"])\n                    )  # Convert json data row into data row identifier object\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Get label parameter overrides (LPOs)\nproject_lpos = project.labeling_parameter_overrides()\n\nfor lpo in project_lpos:\n    print(lpo)\n    print(\"Data row:\", lpo.data_row().uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Add LPOs\nlpos = []\npriority = 1\nfor data_row in data_rows:\n    lpos.append((data_row, priority))\n    priority += 1\n\nproject.set_labeling_parameter_overrides(lpos)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Update LPOs\nglobal_keys = []\nfor data_row in data_rows:\n    global_keys.append(data_row.key)\n\nproject.update_data_row_labeling_priority(data_rows=lb.GlobalKeys(global_keys),\n                                          priority=1)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index ec54de860..f69472e60 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -1,344 +1,212 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Webhook Configuration"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Webhooks are supported for the following events:\n",
-        "* label_created\n",
-        "* label_updated\n",
-        "* label_deleted"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox\n",
-        "!pip install requests\n",
-        "!pip install hmac\n",
-        "!pip install hashlib\n",
-        "!pip install flask\n",
-        "!pip install Werkzeug"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from flask import Flask, request\n",
-        "import hmac\n",
-        "import hashlib\n",
-        "import threading\n",
-        "from werkzeug.serving import run_simple\n",
-        "import json\n",
-        "import requests\n",
-        "import os\n",
-        "from getpass import getpass\n",
-        "import socket"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# If you don't want to give google access to drive you can skip this cell\n",
-        "# and manually set `API_KEY` below.\n",
-        "\n",
-        "COLAB = \"google.colab\" in str(get_ipython())\n",
-        "if COLAB:\n",
-        "    !pip install colab-env -qU\n",
-        "    from colab_env import envvar_handler\n",
-        "\n",
-        "    envvar_handler.envload()\n",
-        "\n",
-        "API_KEY = os.environ.get(\"LABELBOX_API_KEY\")\n",
-        "if not os.environ.get(\"LABELBOX_API_KEY\"):\n",
-        "    API_KEY = getpass(\"Please enter your labelbox api key\")\n",
-        "    if COLAB:\n",
-        "        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set this to a project that you want to use for the webhook\n",
-        "PROJECT_ID = \"\"\n",
-        "# Only update this if you have an on-prem deployment\n",
-        "ENDPOINT = \"https://api.labelbox.com/graphql\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# We are using port 3001 for this example.\n",
-        "# Feel free to set to whatever port you want\n",
-        "WH_PORT = 3001"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Configure NGROK (Optional)\n",
-        "* If you do not have a public ip address then follow along\n",
-        "\n",
-        "1. Create an account:\n",
-        "    https://dashboard.ngrok.com/get-started/setup\n",
-        "2. Download ngrok and extract the zip file\n",
-        "3. Add ngrok to your path\n",
-        "4. Add the authtoken `ngrok authtoken <token>`"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "if not COLAB:\n",
-        "    os.system(f\"ngrok http {WH_PORT} &\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Configure server to receive requests"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# This can be any secret that matches your webhook config (we will set later)\n",
-        "secret = b\"example_secret\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "app = Flask(__name__)\n",
-        "\n",
-        "\n",
-        "@app.route(\"/\")\n",
-        "def hello_world():\n",
-        "    return \"Hello, World!\"\n",
-        "\n",
-        "\n",
-        "@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\n",
-        "def print_webhook_info():\n",
-        "    payload = request.data\n",
-        "    computed_signature = hmac.new(secret, msg=payload,\n",
-        "                                  digestmod=hashlib.sha1).hexdigest()\n",
-        "    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n",
-        "        print(\n",
-        "            \"Error: computed_signature does not match signature provided in the headers\"\n",
-        "        )\n",
-        "        return \"Error\", 500, 200\n",
-        "\n",
-        "    print(\"=========== New Webhook Delivery ============\")\n",
-        "    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n",
-        "    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n",
-        "    print(\"Payload: %s\" %\n",
-        "          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n",
-        "    return \"Success\"\n",
-        "\n",
-        "\n",
-        "thread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\n",
-        "thread.start()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Test server"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(requests.get(\"http://localhost:3001\").text)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Webhook"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "- Set ip address if your ip is publicly accessible.\n",
-        "- Otherwise use the following to get ngrok public_url"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "if not COLAB:\n",
-        "    res = requests.get(\"http://localhost:4040/api/tunnels\")\n",
-        "    assert (res.status_code == 200\n",
-        "           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n",
-        "    tunnels = res.json()[\"tunnels\"]\n",
-        "    tunnel = [\n",
-        "        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n",
-        "    ]\n",
-        "    tunnel = tunnel[0]  # Should only be one..\n",
-        "    public_url = tunnel[\"public_url\"]\n",
-        "else:\n",
-        "    public_url = f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\"\n",
-        "print(public_url)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set project to limit the scope to a single project\n",
-        "project = client.get_project(PROJECT_ID)\n",
-        "topics = {topic.value for topic in lb.Webhook.Topic}\n",
-        "# For Global Webhooks (Global = per workspace) project = None\n",
-        "webhook = lb.Webhook.create(client,\n",
-        "                         topics=topics,\n",
-        "                         url=public_url,\n",
-        "                         secret=secret.decode(),\n",
-        "                         project=project)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Ok so we should be configured assuming everything is setup correctly.\n",
-        "# Go to the following url and make a new label to see if it works\n",
-        "print(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Update Webhook"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# url, topics, and status can all be updated\n",
-        "updated_url = f\"{public_url}/webhook-endpoint\"\n",
-        "print(updated_url)\n",
-        "webhook.update(url=updated_url)\n",
-        "# Go to the following url and try one last time.\n",
-        "# Any supported action should work (create, delete, or update a label)\n",
-        "print(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### List and delete all webhooks"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# DELETE:\n",
-        "webhook.update(status=lb.Webhook.Status.INACTIVE.value)\n",
-        "\n",
-        "# FETCH ALL WEBHOOKS:\n",
-        "org = client.get_organization()\n",
-        "webhooks = org.webhooks()\n",
-        "\n",
-        "# Run this to clear all.\n",
-        "# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n",
-        "# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n",
-        "# for webhook in webhooks:\n",
-        "#    print(webhook)\n",
-        "#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Webhook Configuration"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Webhooks are supported for the following events:\n",
+                "* label_created\n",
+                "* label_updated\n",
+                "* label_deleted"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox\n%pip install requests\n%pip install hmac\n%pip install hashlib\n%pip install flask\n%pip install Werkzeug",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# If you don't want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Configure NGROK (Optional)\n",
+                "* If you do not have a public ip address then follow along\n",
+                "\n",
+                "1. Create an account:\n",
+                "    https://dashboard.ngrok.com/get-started/setup\n",
+                "2. Download ngrok and extract the zip file\n",
+                "3. Add ngrok to your path\n",
+                "4. Add the authtoken `ngrok authtoken <token>`"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Configure server to receive requests"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Test server"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "print(requests.get(\"http://localhost:3001\").text)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Webhook"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "- Set ip address if your ip is publicly accessible.\n",
+                "- Otherwise use the following to get ngrok public_url"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Update Webhook"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### List and delete all webhooks"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file

From 9fe622bd48829fc7431561f225734016732bd416 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:38:04 -0500
Subject: [PATCH 07/80] fixed conditional

---
 .github/workflows/notebook-create-readme.yml | 2 +-
 .github/workflows/notebook-format.yml        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/notebook-create-readme.yml b/.github/workflows/notebook-create-readme.yml
index b78f6cfb9..9f692eb4e 100644
--- a/.github/workflows/notebook-create-readme.yml
+++ b/.github/workflows/notebook-create-readme.yml
@@ -15,7 +15,7 @@ permissions:
   pull-requests: write
   
 jobs:
-  if: github.event.pull_request.merged == False
+  if: github.event.pull_request.merged == 'false'
   changes:
     runs-on: ubuntu-latest
     # Set job outputs to values from filter step
diff --git a/.github/workflows/notebook-format.yml b/.github/workflows/notebook-format.yml
index 6269331dd..7d58f609f 100644
--- a/.github/workflows/notebook-format.yml
+++ b/.github/workflows/notebook-format.yml
@@ -15,7 +15,7 @@ permissions:
   pull-requests: write
   
 jobs:
-  if: github.event.pull_request.merged == False
+  if: github.event.pull_request.merged == 'false'
   update:
     runs-on: ubuntu-latest
     steps:

From 32c6766cb1f93c60f8d20dab39acdc4fcccac93a Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:40:03 -0500
Subject: [PATCH 08/80] fixed conditional

---
 .github/workflows/notebook-create-readme.yml | 2 +-
 .github/workflows/notebook-format.yml        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/notebook-create-readme.yml b/.github/workflows/notebook-create-readme.yml
index 9f692eb4e..b2076f677 100644
--- a/.github/workflows/notebook-create-readme.yml
+++ b/.github/workflows/notebook-create-readme.yml
@@ -15,7 +15,7 @@ permissions:
   pull-requests: write
   
 jobs:
-  if: github.event.pull_request.merged == 'false'
+  if: github.event.pull_request.merged == false
   changes:
     runs-on: ubuntu-latest
     # Set job outputs to values from filter step
diff --git a/.github/workflows/notebook-format.yml b/.github/workflows/notebook-format.yml
index 7d58f609f..4206f025f 100644
--- a/.github/workflows/notebook-format.yml
+++ b/.github/workflows/notebook-format.yml
@@ -15,7 +15,7 @@ permissions:
   pull-requests: write
   
 jobs:
-  if: github.event.pull_request.merged == 'false'
+  if: github.event.pull_request.merged == false
   update:
     runs-on: ubuntu-latest
     steps:

From b2c755cb72345c57a1f9abfcaf4f05daca001cc3 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:44:05 -0500
Subject: [PATCH 09/80] fixed conditional

---
 .github/workflows/notebook-create-readme.yml | 3 ++-
 .github/workflows/notebook-format.yml        | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/notebook-create-readme.yml b/.github/workflows/notebook-create-readme.yml
index b2076f677..d0b40dd0d 100644
--- a/.github/workflows/notebook-create-readme.yml
+++ b/.github/workflows/notebook-create-readme.yml
@@ -15,8 +15,8 @@ permissions:
   pull-requests: write
   
 jobs:
-  if: github.event.pull_request.merged == false
   changes:
+    if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
     # Set job outputs to values from filter step
     outputs:
@@ -29,6 +29,7 @@ jobs:
           addedOrModified:
             - added|deleted: 'examples/**.ipynb'
   update:
+    if: github.event.pull_request.merged == false
     needs: changes
     if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
     runs-on: ubuntu-latest
diff --git a/.github/workflows/notebook-format.yml b/.github/workflows/notebook-format.yml
index 4206f025f..8cbf9a8de 100644
--- a/.github/workflows/notebook-format.yml
+++ b/.github/workflows/notebook-format.yml
@@ -15,8 +15,8 @@ permissions:
   pull-requests: write
   
 jobs:
-  if: github.event.pull_request.merged == false
   update:
+    if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From e03c04984ea865b5edc5d01f8e60d492853539f6 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:45:48 -0500
Subject: [PATCH 10/80] fixed conditional

---
 .github/workflows/notebook-create-readme.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/notebook-create-readme.yml b/.github/workflows/notebook-create-readme.yml
index d0b40dd0d..12ca5da18 100644
--- a/.github/workflows/notebook-create-readme.yml
+++ b/.github/workflows/notebook-create-readme.yml
@@ -29,9 +29,8 @@ jobs:
           addedOrModified:
             - added|deleted: 'examples/**.ipynb'
   update:
-    if: github.event.pull_request.merged == false
+    if: github.event.pull_request.merged == false &&	${{ needs.changes.outputs.addedOrModified == 'true' }}
     needs: changes
-    if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From 242ed0d9279714ac43a689cac85c46b5a0cf3ecb Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:50:26 -0500
Subject: [PATCH 11/80] fixed action

---
 .github/actions/notebooks/{actions.yml => action.yml} | 11 -----------
 1 file changed, 11 deletions(-)
 rename .github/actions/notebooks/{actions.yml => action.yml} (63%)

diff --git a/.github/actions/notebooks/actions.yml b/.github/actions/notebooks/action.yml
similarity index 63%
rename from .github/actions/notebooks/actions.yml
rename to .github/actions/notebooks/action.yml
index 50bfe552e..4e5b46ee8 100644
--- a/.github/actions/notebooks/actions.yml
+++ b/.github/actions/notebooks/action.yml
@@ -1,26 +1,15 @@
 name: Labelbox Example Notebook Setup
-
-inputs:
-  rye-version:
-    required: true
-  python-version:
-    required: true
     
 runs:
-  using: "composite"
   steps:
     - name: Install the latest version of rye
       uses: eifinger/setup-rye@v2
       with:
-        version: ${{ inputs.rye-version }}
         enable-cache: true
     - name: Rye Setup
       shell: bash
       run: |
         rye config --set-bool behavior.use-uv=true
-    - name: Python setup
-      shell: bash
-      run: rye pin ${{ inputs.python-version }}
     - name: Environment setup
       working-directory: examples
       shell: bash

From 39e842314a35106bca979391a8a51ae3ebc64df8 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 20:52:30 -0500
Subject: [PATCH 12/80] added back composite

---
 .github/actions/notebooks/action.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/actions/notebooks/action.yml b/.github/actions/notebooks/action.yml
index 4e5b46ee8..631e61328 100644
--- a/.github/actions/notebooks/action.yml
+++ b/.github/actions/notebooks/action.yml
@@ -1,6 +1,7 @@
 name: Labelbox Example Notebook Setup
     
-runs:
+runs:  
+  using: "composite"
   steps:
     - name: Install the latest version of rye
       uses: eifinger/setup-rye@v2

From 70f1baaf326c7e03c39408bc0490aa6736db2c10 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 01:53:15 +0000
Subject: [PATCH 13/80] :memo: README updated

---
 examples/README.md | 320 ++++++++++++++++++++++-----------------------
 1 file changed, 160 insertions(+), 160 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index 08f083bc2..47cca5044 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,7 +5,7 @@
 
 - Run in google colab, view the notebooks on github, or clone the repo and run locally
 
-<h2>Basics</h2>
+<h2>Model Experiments</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -17,49 +17,24 @@
   </thead>
   <tbody>
     <tr>
-      <td>Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Batches</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Projects</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>User Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Export V1 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Custom Metrics Demo</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Data Row Metadata</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Model Slices</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_slices.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Ontologies</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Custom Metrics Basics</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Data Rows</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Model Predictions to Project</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
@@ -77,13 +52,13 @@
   <tbody>
     <tr>
       <td>Object Detection</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/object_detection.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/object_detection.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Project Configuration</h2>
+<h2>Exports</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -95,24 +70,19 @@
   </thead>
   <tbody>
     <tr>
-      <td>Project Setup</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Webhooks</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Composite Mask Export</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/composite_mask_export.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Queue Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Export Data</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_data.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_data.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Annotation Import</h2>
+<h2>Basics</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -124,64 +94,49 @@
   </thead>
   <tbody>
     <tr>
-      <td>Import Labeled Dataset Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Audio</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Text</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Ontologies</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Tiled</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Data Rows</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Batches</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>PDF</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/PDF.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/PDF.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Export V1 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_v1_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational LLM Data Generation</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Projects</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/projects.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>DICOM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Custom Embeddings</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Data Row Metadata</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_row_metadata.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/data_row_metadata.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>HTML</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Basics</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational LLM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>User Management</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
@@ -198,49 +153,49 @@
   </thead>
   <tbody>
     <tr>
-      <td>Conversational Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>PDF Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Text Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>HTML Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>HTML Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Conversational Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational LLM Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Image Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Geospatial Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Text Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>PDF Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Geospatial Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Video Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Conversational LLM Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Image Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Video Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Exports</h2>
+<h2>Extras</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -252,19 +207,14 @@
   </thead>
   <tbody>
     <tr>
-      <td>Composite Mask Export</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Export Data</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Classification Confusion Matrix</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/classification_confusion_matrix.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Integrations</h2>
+<h2>Project Configuration</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -276,29 +226,24 @@
   </thead>
   <tbody>
     <tr>
-      <td>Meta SAM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-    </tr>
-    <tr>
-      <td>Meta SAM Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Project Setup</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_setup.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_setup.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Langchain</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Webhooks</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/webhooks.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/webhooks.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Huggingface Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Queue Management</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/queue_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/queue_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Model Experiments</h2>
+<h2>Integrations</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -310,29 +255,29 @@
   </thead>
   <tbody>
     <tr>
-      <td>Model Slices</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Huggingface Custom Embeddings</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Custom Metrics Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Langchain</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/langchain/langchain.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/langchain/langchain.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Custom Metrics Demo</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Meta SAM Video</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Model Predictions to Project</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Meta SAM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Extras</h2>
+<h2>Annotation Import</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -344,9 +289,64 @@
   </thead>
   <tbody>
     <tr>
-      <td>Classification Confusion Matrix</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" onclick="(function prevent(e){e.preventDefault()}()"></a></td>
+      <td>Conversational LLM Data Generation</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Video</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Text</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/text.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Audio</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Conversational</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Import Labeled Dataset Image</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>PDF</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/pdf.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Image</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>DICOM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/dicom.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/dicom.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Conversational LLM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>HTML</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/html.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Tiled</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>

From 7f92bf8966f33c1af72c76644d1b5121a5f83116 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:10:13 -0500
Subject: [PATCH 14/80] combined workflows

---
 .github/workflows/notebook-format.yml         | 43 -------------------
 ...otebook-create-readme.yml => notebook.yml} | 33 ++++++++++++--
 2 files changed, 30 insertions(+), 46 deletions(-)
 delete mode 100644 .github/workflows/notebook-format.yml
 rename .github/workflows/{notebook-create-readme.yml => notebook.yml} (57%)

diff --git a/.github/workflows/notebook-format.yml b/.github/workflows/notebook-format.yml
deleted file mode 100644
index 8cbf9a8de..000000000
--- a/.github/workflows/notebook-format.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-name: Labelbox Example Notebook Format
-
-on:
-  push:
-    branches: [develop]
-    paths:
-      - examples/**
-  pull_request:
-    branches: [develop]
-    paths:
-      - examples/**
-
-permissions:
-  contents: write
-  pull-requests: write
-  
-jobs:
-  update:
-    if: github.event.pull_request.merged == false
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
-          fetch-depth: 0
-      - uses: ./.github/actions/notebooks
-        with:
-          rye-version: ${{ vars.RYE_VERSION }}
-          python-version: ${{ inputs.python-version }}
-      - name: Format
-        working-directory: examples
-        run: rye run clean
-      - name: Commit changes
-        run: |
-            git config --local user.email "github-actions[bot]@users.noreply.github.com"
-            git config --local user.name "github-actions[bot]"
-            git add examples/.
-            git commit -m ":art: Cleaned" || exit 0
-      - name: Push changes
-        uses: ad-m/github-push-action@master
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          branch: ${{ github.head_ref }}
\ No newline at end of file
diff --git a/.github/workflows/notebook-create-readme.yml b/.github/workflows/notebook.yml
similarity index 57%
rename from .github/workflows/notebook-create-readme.yml
rename to .github/workflows/notebook.yml
index 12ca5da18..6fc4e8acd 100644
--- a/.github/workflows/notebook-create-readme.yml
+++ b/.github/workflows/notebook.yml
@@ -1,4 +1,4 @@
-name: Labelbox Example Notebook Update Readme
+name: Labelbox Example Notebook Format
 
 on:
   push:
@@ -15,7 +15,34 @@ permissions:
   pull-requests: write
   
 jobs:
+  format:
+    if: github.event.pull_request.merged == false
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ github.head_ref }}
+          fetch-depth: 0
+      - uses: ./.github/actions/notebooks
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: ${{ inputs.python-version }}
+      - name: Format
+        working-directory: examples
+        run: rye run clean
+      - name: Commit changes
+        run: |
+            git config --local user.email "github-actions[bot]@users.noreply.github.com"
+            git config --local user.name "github-actions[bot]"
+            git add examples/.
+            git commit -m ":art: Cleaned" || exit 0
+      - name: Push changes
+        uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          branch: ${{ github.head_ref }}
   changes:
+    needs: format
     if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
     # Set job outputs to values from filter step
@@ -28,9 +55,9 @@ jobs:
         filters: |
           addedOrModified:
             - added|deleted: 'examples/**.ipynb'
-  update:
-    if: github.event.pull_request.merged == false &&	${{ needs.changes.outputs.addedOrModified == 'true' }}
+  create:
     needs: changes
+    if: github.event.pull_request.merged == false && needs.changes.outputs.addedOrModified == true
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From eaad2ec94b35c58e34cf1826ff73dd70473f2de6 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:12:16 +0000
Subject: [PATCH 15/80] :art: Cleaned

---
 examples/annotation_import/audio.ipynb        |  764 +++----
 .../annotation_import/conversational.ipynb    |  934 +++------
 .../conversational_LLM.ipynb                  |  953 +++------
 .../conversational_LLM_data_generation.ipynb  |  720 +++----
 examples/annotation_import/dicom.ipynb        |  726 +++----
 examples/annotation_import/html.ipynb         |  881 +++-----
 examples/annotation_import/image.ipynb        | 1492 ++++----------
 .../import_labeled_dataset_image.ipynb        |  590 ++----
 examples/annotation_import/pdf.ipynb          | 1535 ++++----------
 examples/annotation_import/text.ipynb         |  930 +++------
 examples/annotation_import/tiled.ipynb        | 1446 ++++---------
 examples/annotation_import/video.ipynb        | 1687 ++++-----------
 examples/basics/basics.ipynb                  |  448 ++--
 examples/basics/batches.ipynb                 |  784 +++----
 examples/basics/custom_embeddings.ipynb       |  659 +++---
 examples/basics/data_row_metadata.ipynb       |  758 +++----
 examples/basics/data_rows.ipynb               |  854 +++-----
 .../basics/export_v1_migration_support.ipynb  | 1620 ++++++---------
 examples/basics/ontologies.ipynb              |  759 +++----
 examples/basics/projects.ipynb                |  854 ++++----
 examples/basics/user_management.ipynb         |  546 +++--
 examples/exports/composite_mask_export.ipynb  |  633 +++---
 examples/exports/export_data.ipynb            | 1420 +++++--------
 .../classification_confusion_matrix.ipynb     |  333 +--
 examples/foundry/object_detection.ipynb       |  607 +++---
 .../huggingface_custom_embeddings.ipynb       |  378 ++--
 .../integrations/langchain/langchain.ipynb    |  566 ++---
 examples/integrations/sam/meta_sam.ipynb      |  718 +++----
 .../integrations/sam/meta_sam_video.ipynb     |  887 +++-----
 .../custom_metrics_basics.ipynb               |  648 +++---
 .../custom_metrics_demo.ipynb                 | 1665 ++++-----------
 .../model_predictions_to_project.ipynb        |  675 +++---
 examples/model_experiments/model_slices.ipynb |  581 +++---
 .../conversational_LLM_predictions.ipynb      | 1212 ++++-------
 .../conversational_predictions.ipynb          | 1139 ++++-------
 .../geospatial_predictions.ipynb              | 1693 ++++-----------
 .../prediction_upload/html_predictions.ipynb  | 1033 +++-------
 .../prediction_upload/image_predictions.ipynb | 1619 +++++----------
 .../prediction_upload/pdf_predictions.ipynb   | 1588 ++++----------
 .../prediction_upload/text_predictions.ipynb  | 1088 ++++------
 .../prediction_upload/video_predictions.ipynb | 1818 ++++-------------
 examples/scripts/format_notebooks.py          |    1 +
 examples/scripts/generate_readme.py           |    9 +-
 43 files changed, 12877 insertions(+), 27374 deletions(-)

diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb
index 052a4fc03..1ff7a91f3 100644
--- a/examples/annotation_import/audio.ipynb
+++ b/examples/annotation_import/audio.ipynb
@@ -1,467 +1,301 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Audio Annotation Import\n",
-        "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n",
-        "\n",
-        "Suported annotations that can be uploaded through the SDK\n",
-        "\n",
-        "* Classification Radio \n",
-        "* Classification Checklist \n",
-        "* Classification Free Text \n",
-        "\n",
-        "**Not** supported annotations\n",
-        "\n",
-        "* Bouding box\n",
-        "* NER\n",
-        "* Polygon \n",
-        "* Point\n",
-        "* Polyline \n",
-        "* Segmentation Mask\n",
-        "\n",
-        "MAL and Label Import:\n",
-        "\n",
-        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Notes:\n",
-        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q 'labelbox[data]'"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace with your API key\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for Audio"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Classification free text #####\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"text_audio\",\n",
-        "    value=lb_types.Text(answer=\"free text audio annotation\"),\n",
-        ")\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "    'name': 'text_audio',\n",
-        "    'answer': 'free text audio annotation',\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Checklist Classification #######\n",
-        "\n",
-        "checklist_annotation= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_audio\",\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        " )\n",
-        "\n",
-        "\n",
-        "checklist_annotation_ndjson = {\n",
-        "    'name': 'checklist_audio',\n",
-        "    'answers': [\n",
-        "        {'name': 'first_checklist_answer'},\n",
-        "        {'name': 'second_checklist_answer'}\n",
-        "    ],\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Radio Classification ######\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_audio\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"second_radio_answer\")))\n",
-        "\n",
-        "radio_annotation_ndjson = {\n",
-        "    'name': 'radio_audio',\n",
-        "    'answer': {\n",
-        "        'name': 'first_radio_answer'\n",
-        "    },\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annotations - putting it all together "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create one Labelbox dataset\n",
-        "\n",
-        "global_key = \"sample-audio-1.mp3\"\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\", task.errors)\n",
-        "print(\"Failed data rows: \", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an ontology\n",
-        "\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"text_audio\"),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_audio\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_audio\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Audio Annotations\", ontology_builder.asdict(), media_type=lb.MediaType.Audio)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "## Step 3: Create a labeling project\n",
-        "Connect the ontology to the labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create Labelbox project\n",
-        "project = client.create_project(name=\"audio_project\",\n",
-        "                                    media_type=lb.MediaType.Audio)\n",
-        "\n",
-        "# Setup your ontology\n",
-        "project.setup_editor(ontology) # Connect your ontology and editor to your project"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup Batches and Ontology\n",
-        "\n",
-        "# Create a batch to send to your MAL project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-audio-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the annotations payload\n",
-        "Create the annotations payload using the snippets of code above\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python annotation\n",
-        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "    data={\"global_key\" : global_key },\n",
-        "    annotations=[\n",
-        "      text_annotation,\n",
-        "      checklist_annotation,\n",
-        "      radio_annotation\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### NDJSON annotations \n",
-        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [text_annotation_ndjson,\n",
-        "                    checklist_annotation_ndjson,\n",
-        "                    radio_annotation_ndjson]:\n",
-        "  annotations.update({\n",
-        "      'dataRow': {\n",
-        "          'globalKey': global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Assisted Labeling (MAL)\n",
-        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload our label using Model-Assisted Labeling\n",
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
-        "    predictions=label)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload label for this data row in project\n",
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Audio Annotation Import\n",
+                "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n",
+                "\n",
+                "Suported annotations that can be uploaded through the SDK\n",
+                "\n",
+                "* Classification Radio \n",
+                "* Classification Checklist \n",
+                "* Classification Free Text \n",
+                "\n",
+                "**Not** supported annotations\n",
+                "\n",
+                "* Bouding box\n",
+                "* NER\n",
+                "* Polygon \n",
+                "* Point\n",
+                "* Polyline \n",
+                "* Segmentation Mask\n",
+                "\n",
+                "MAL and Label Import:\n",
+                "\n",
+                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Notes:\n",
+                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q 'labelbox[data]'",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Replace with your API key\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for Audio"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_audio\",\n    value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_audio\",\n    \"answer\": \"free text audio annotation\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_audio\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_audio\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_audio\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_audio\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annotations - putting it all together "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an ontology\n",
+                "\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT,\n                      name=\"text_audio\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_audio\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_audio\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology Audio Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Audio,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "\n",
+                "## Step 3: Create a labeling project\n",
+                "Connect the ontology to the labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n                                media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-audio-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the annotations payload\n",
+                "Create the annotations payload using the snippets of code above\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python annotation\n",
+                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[text_annotation, checklist_annotation, radio_annotation],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### NDJSON annotations \n",
+                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Assisted Labeling (MAL)\n",
+                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational.ipynb b/examples/annotation_import/conversational.ipynb
index 35277b667..139b6e494 100644
--- a/examples/annotation_import/conversational.ipynb
+++ b/examples/annotation_import/conversational.ipynb
@@ -1,621 +1,317 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Conversational Text Annotation Import\n",
-        "* This notebook will provide examples of each supported annotation type for conversational text assets, and also  cover MAL and Label Import methods:\n",
-        "\n",
-        "Supported annotations that can be uploaded through the SDK\n",
-        "\n",
-        "* Classification Radio \n",
-        "* Classification Checklist \n",
-        "* Classification Free Text \n",
-        "* NER\n",
-        "\n",
-        "\n",
-        "**Not** supported annotations\n",
-        "\n",
-        "* Relationships\n",
-        "* Bouding box \n",
-        "* Polygon \n",
-        "* Point\n",
-        "* Polyline \n",
-        "* Segmentation Mask \n",
-        "\n",
-        "MAL and Label Import:\n",
-        "\n",
-        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Notes:\n",
-        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace with your API key\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for conversational text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# message based classifications\n",
-        "ner_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"ner\",\n",
-        "    value=lb_types.ConversationEntity(\n",
-        "        start=0,\n",
-        "        end=8,\n",
-        "        message_id=\"4\"\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "ner_annotation_ndjson = {\n",
-        "        \"name\": \"ner\",\n",
-        "        \"location\": {\n",
-        "            \"start\": 0,\n",
-        "            \"end\": 8\n",
-        "        },\n",
-        "        \"messageId\": \"4\"\n",
-        "    }"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Classification free text #####\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"text_convo\",\n",
-        "    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n",
-        "    message_id=\"0\"\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "    \"name\": \"text_convo\",\n",
-        "    \"answer\": \"the answer to the text questions right here\",\n",
-        "    \"messageId\": \"0\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Checklist Classification #######\n",
-        "\n",
-        "checklist_annotation= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_convo\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  message_id=\"2\"\n",
-        " )\n",
-        "\n",
-        "\n",
-        "checklist_annotation_ndjson = {\n",
-        "    \"name\": \"checklist_convo\",\n",
-        "    \"answers\": [\n",
-        "        {\"name\": \"first_checklist_answer\"},\n",
-        "        {\"name\": \"second_checklist_answer\"}\n",
-        "    ],\n",
-        "    \"messageId\": \"2\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Radio Classification ######\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_convo\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\")),\n",
-        "    message_id=\"0\"\n",
-        ")\n",
-        "\n",
-        "radio_annotation_ndjson = {\n",
-        "    \"name\": \"radio_convo\",\n",
-        "    \"answer\": {\n",
-        "        \"name\": \"first_radio_answer\"\n",
-        "    },\n",
-        "    \"messageId\": \"0\",\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# ############ global nested classifications ###########\n",
-        "# Message based\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  message_id=\"10\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "# Message based\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"messageId\": \"10\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\n",
-        "            \"name\": \"first_sub_checklist_answer\"\n",
-        "          }\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}\n",
-        "# Global\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "# Global\n",
-        "nested_radio_annotation_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annotations - putting it all together "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create one Labelbox dataset\n",
-        "\n",
-        "global_key = \"conversation-1.json\"\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"conversational_annotation_import_demo_dataset\")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\", task.errors)\n",
-        "print(\"Failed data rows: \", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an ontology\n",
-        "\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the text annotation, we provided the `name` as `text_convo`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_convo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  tools=[\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER,name=\"ner\"),\n",
-        "    ],\n",
-        "  classifications=[\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      name=\"text_convo\"),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      name=\"checklist_convo\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_convo\",\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      scope = lb.Classification.Scope.INDEX,\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ])\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        scope = lb.Classification.Scope.GLOBAL,\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ])\n",
-        "          ]\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Conversation Annotations\", ontology_builder.asdict())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "## Step 3: Create a labeling project\n",
-        "Connect the ontology to the labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create Labelbox project\n",
-        "project = client.create_project(name=\"Conversational Text Annotation Import Demo\",\n",
-        "                                    media_type=lb.MediaType.Conversational)\n",
-        "\n",
-        "# Setup your ontology\n",
-        "project.setup_editor(ontology) # Connect your ontology and editor to your project"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a batch to send to your MAL project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-convo-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the annotations payload\n",
-        "Create the annotations payload using the snippets of code above\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. However,for conversational texts NDJSON is the only supported format. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python annotation\n",
-        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "    data={\"global_key\" : global_key },\n",
-        "    annotations=[\n",
-        "      ner_annotation,\n",
-        "      text_annotation,\n",
-        "      checklist_annotation,\n",
-        "      radio_annotation,\n",
-        "      nested_radio_annotation,\n",
-        "      nested_checklist_annotation\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### NDJSON annotations \n",
-        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [\n",
-        "    ner_annotation_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    checklist_annotation_ndjson,\n",
-        "    radio_annotation_ndjson,\n",
-        "    nested_checklist_annotation_ndjson,\n",
-        "    nested_radio_annotation_ndjson\n",
-        "    ]:\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Assisted Labeling (MAL)\n",
-        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload our label using Model-Assisted Labeling\n",
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
-        "    predictions=label)\n",
-        "\n",
-        "upload_job.wait_until_done()\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload label for this data row in project\n",
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "     client = client,\n",
-        "     project_id = project.uid,\n",
-        "     name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "     labels=label)\n",
-        "\n",
-        "upload_job.wait_until_done()\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Conversational Text Annotation Import\n",
+                "* This notebook will provide examples of each supported annotation type for conversational text assets, and also  cover MAL and Label Import methods:\n",
+                "\n",
+                "Supported annotations that can be uploaded through the SDK\n",
+                "\n",
+                "* Classification Radio \n",
+                "* Classification Checklist \n",
+                "* Classification Free Text \n",
+                "* NER\n",
+                "\n",
+                "\n",
+                "**Not** supported annotations\n",
+                "\n",
+                "* Relationships\n",
+                "* Bouding box \n",
+                "* Polygon \n",
+                "* Point\n",
+                "* Polyline \n",
+                "* Segmentation Mask \n",
+                "\n",
+                "MAL and Label Import:\n",
+                "\n",
+                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Notes:\n",
+                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Replace with your API key\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for conversational text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# message based classifications\nner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n    message_id=\"0\",\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions right here\",\n    \"messageId\": \"0\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"2\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n    \"messageId\": \"0\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# ############ global nested classifications ###########\n# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annotations - putting it all together "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an ontology\n",
+                "\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we create the text annotation, we provided the `name` as `text_convo`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_convo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "\n",
+                "## Step 3: Create a labeling project\n",
+                "Connect the ontology to the labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the annotations payload\n",
+                "Create the annotations payload using the snippets of code above\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. However,for conversational texts NDJSON is the only supported format. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python annotation\n",
+                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### NDJSON annotations \n",
+                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Assisted Labeling (MAL)\n",
+                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index 05edb17bc..d69df8f46 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -1,647 +1,310 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
-        "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set up"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace with your API key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Supported annotations for conversational text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Entity "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ner_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"ner\",\n",
-        "    value=lb_types.ConversationEntity(\n",
-        "        start=0,\n",
-        "        end=8,\n",
-        "        message_id=\"message-1\"\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "ner_annotation_ndjson = {\n",
-        "        \"name\": \"ner\",\n",
-        "        \"location\": {\n",
-        "            \"start\": 0,\n",
-        "            \"end\": 8\n",
-        "        },\n",
-        "        \"messageId\": \"message-1\"\n",
-        "    }"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Radio (single-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Choose the best response\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"Response B\")))\n",
-        "\n",
-        "\n",
-        "\n",
-        "radio_annotation_ndjson = {\n",
-        "    \"name\": \"Choose the best response\",\n",
-        "    \"answer\": {\n",
-        "      \"name\": \"Response B\"\n",
-        "    }\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Provide a reason for your choice\",\n",
-        "    value=lb_types.Text(answer=\"the answer to the text questions right here\")\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "    \"name\": \"Provide a reason for your choice\",\n",
-        "    \"answer\": \"This is the more concise answer\"\n",
-        "\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Checklist (multi-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "checklist_annotation= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_convo\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  message_id=\"message-1\" # Message specific annotation\n",
-        " )\n",
-        "\n",
-        "\n",
-        "checklist_annotation_ndjson = {\n",
-        "    \"name\": \"checklist_convo\",\n",
-        "    \"answers\": [\n",
-        "        {\"name\": \"first_checklist_answer\"},\n",
-        "        {\"name\": \"second_checklist_answer\"}\n",
-        "    ],\n",
-        "    \"messageId\": \"message-1\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Nested radio and checklist"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# Message based\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  message_id=\"message-1\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "# Message based\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"messageId\": \"message-1\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\n",
-        "            \"name\": \"first_sub_checklist_answer\",\n",
-        "          }\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}\n",
-        "# Global\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "#Global\n",
-        "nested_radio_annotation_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
-        "\n",
-        "```\n",
-        "\"modelOutputs\" : [\n",
-        "  {\n",
-        "      \"title\": \"Name of the response option\",\n",
-        "      \"content\": \"Content of the response\",\n",
-        "      \"modelConfigName\": \"Name of model configuration\"\n",
-        "  }\n",
-        "]\n",
-        "```\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Example of row_data with model outputs"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "pairwise_shopping_2 =  \"\"\"\n",
-        " {\n",
-        "  \"type\": \"application/vnd.labelbox.conversational\",\n",
-        "  \"version\": 1,\n",
-        "  \"messages\": [\n",
-        "    {\n",
-        "      \"messageId\": \"message-0\",\n",
-        "      \"timestampUsec\": 1530718491,\n",
-        "      \"content\": \"Hi! How can I help?\",\n",
-        "      \"user\": {\n",
-        "        \"userId\": \"Bot 002\",\n",
-        "        \"name\": \"Bot\"\n",
-        "      },\n",
-        "      \"align\": \"left\",\n",
-        "      \"canLabel\": false\n",
-        "    },\n",
-        "    {\n",
-        "      \"messageId\": \"message-1\",\n",
-        "      \"timestampUsec\": 1530718503,\n",
-        "      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n",
-        "      \"user\": {\n",
-        "        \"userId\": \"User 00686\",\n",
-        "        \"name\": \"User\"\n",
-        "      },\n",
-        "      \"align\": \"right\",\n",
-        "      \"canLabel\": true\n",
-        "    }\n",
-        "\n",
-        "  ],\n",
-        "  \"modelOutputs\": [\n",
-        "    {\n",
-        "      \"title\": \"Response A\",\n",
-        "      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n",
-        "      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"title\": \"Response B\",\n",
-        "      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n",
-        "      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n",
-        "    }\n",
-        "  ]\n",
-        "}\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"pairwise_shooping_asset\"\n",
-        "\n",
-        "# Upload data rows\n",
-        "convo_data = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "# Create a dataset\n",
-        "dataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n",
-        "# Create a datarows\n",
-        "task = dataset.create_data_rows([convo_data])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create an ontology with relevant classifications\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  tools=[\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER,name=\"ner\"),\n",
-        "  ],\n",
-        "  classifications=[\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      scope=lb.Classification.Scope.GLOBAL,\n",
-        "      name=\"Choose the best response\",\n",
-        "      options=[lb.Option(value=\"Response A\"), lb.Option(value=\"Response B\"), lb.Option(value=\"Tie\")]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"Provide a reason for your choice\"\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      name=\"checklist_convo\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      scope = lb.Classification.Scope.INDEX,\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ])\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        scope = lb.Classification.Scope.GLOBAL,\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ])\n",
-        "          ]\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Pairwise comparison ontology\", ontology_builder.asdict(), media_type=lb.MediaType.Conversational)\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create Labelbox project\n",
-        "project = client.create_project(name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n",
-        "                                    media_type=lb.MediaType.Conversational)\n",
-        "\n",
-        "# Setup your ontology\n",
-        "project.setup_editor(ontology) # Connect your ontology and editor to your project"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a batch to send to your project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-convo-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Python annotation"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "    data={\"global_key\" : global_key },\n",
-        "    annotations=[\n",
-        "      ner_annotation,\n",
-        "      text_annotation,\n",
-        "      checklist_annotation,\n",
-        "      radio_annotation,\n",
-        "      nested_radio_annotation,\n",
-        "      nested_checklist_annotation\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "NDJSON annotation"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [\n",
-        "    ner_annotation_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    checklist_annotation_ndjson,\n",
-        "    radio_annotation_ndjson,\n",
-        "    nested_checklist_annotation_ndjson,\n",
-        "    nested_radio_annotation_ndjson\n",
-        "    ]:\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6: Upload annotations to a project as pre-labels or complete labels "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Model Assisted Labeling (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
-        "    predictions=label)\n",
-        "\n",
-        "upload_job.wait_until_done()\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
+                "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Set up"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Replace with your API key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Supported annotations for conversational text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Entity "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Radio (single-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Checklist (multi-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Nested radio and checklist"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
+                "\n",
+                "```\n",
+                "\"modelOutputs\" : [\n",
+                "  {\n",
+                "      \"title\": \"Name of the response option\",\n",
+                "      \"content\": \"Content of the response\",\n",
+                "      \"modelConfigName\": \"Name of model configuration\"\n",
+                "  }\n",
+                "]\n",
+                "```\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Example of row_data with model outputs"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Python annotation"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "NDJSON annotation"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6: Upload annotations to a project as pre-labels or complete labels "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Model Assisted Labeling (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational_LLM_data_generation.ipynb b/examples/annotation_import/conversational_LLM_data_generation.ipynb
index 6215f9c95..f620a3a8d 100644
--- a/examples/annotation_import/conversational_LLM_data_generation.ipynb
+++ b/examples/annotation_import/conversational_LLM_data_generation.ipynb
@@ -1,453 +1,271 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# LLM Data Generation with MAL and Ground Truth\n",
-        "This demo is meant to showcase how to generate prompts and responses to fine-tune large language models (LLMs) using MAL and Ground truth"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Set up "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for LLM data generation\n",
-        "Currently, we only support NDJson format for prompt and responses"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Prompt:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "prompt_annotation_ndjson = {\n",
-        "  \"name\": \"Follow the prompt and select answers\",\n",
-        "  \"answer\": \"This is an example of a prompt\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Responses:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Radio (single-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "response_radio_annotation_ndjson= {\n",
-        "  \"name\": \"response_radio\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"response_a\"\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Only NDJson is currently supported\n",
-        "response_text_annotation_ndjson = {\n",
-        "  \"name\": \"Provide a reason for your choice\",\n",
-        "  \"answer\": \"This is an example of a response text\"\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Checklist (multi-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "response_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"response_checklist\",\n",
-        "  \"answer\": [\n",
-        "    {\n",
-        "      \"name\": \"response_a\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"name\": \"response_c\"\n",
-        "    }\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Create a project and data rows in Labelbox UI\n",
-        "\n",
-        "Currently we do not support this workflow through the SDK.\n",
-        "#### Workflow:\n",
-        "\n",
-        "1. Navigate to annotate and select ***New project***\n",
-        "\n",
-        "2. Select ***LLM data generation*** and then select ***Humans generate prompts and responses***\n",
-        "\n",
-        "3. Name your project, select ***create a new dataset*** and name your dataset. (data rows will be generated automatically in \n",
-        "this step)\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Enter the project id\n",
-        "project_id = \"\"\n",
-        "\n",
-        "# Select one of the global keys from the data rows generated\n",
-        "global_key = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2 : Create/select an Ontology in Labelbox UI"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Currently we do not support this workflow through the SDK\n",
-        "#### Workflow: \n",
-        "1. In your project, navigate to ***Settings*** and ***Label editor***\n",
-        "\n",
-        "2. Click on ***Edit***\n",
-        "\n",
-        "3. Create a new ontology and add the features used in this demo\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### For this demo the following ontology was generated in the UI: "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_json = \"\"\"\n",
-        "{\n",
-        " \"tools\": [],\n",
-        " \"relationships\": [],\n",
-        " \"classifications\": [\n",
-        "  {\n",
-        "   \"schemaNodeId\": \"clpvq9d0002yt07zy0khq42rp\",\n",
-        "   \"featureSchemaId\": \"clpvq9d0002ys07zyf2eo9p14\",\n",
-        "   \"type\": \"prompt\",\n",
-        "   \"name\": \"Follow the prompt and select answers\",\n",
-        "   \"archived\": false,\n",
-        "   \"required\": true,\n",
-        "   \"options\": [],\n",
-        "   \"instructions\": \"Follow the prompt and select answers\",\n",
-        "   \"minCharacters\": 5,\n",
-        "   \"maxCharacters\": 100\n",
-        "  },\n",
-        "  {\n",
-        "   \"schemaNodeId\": \"clpvq9d0002yz07zy0fjg28z7\",\n",
-        "   \"featureSchemaId\": \"clpvq9d0002yu07zy28ik5w3i\",\n",
-        "   \"type\": \"response-radio\",\n",
-        "   \"name\": \"response_radio\",\n",
-        "   \"instructions\": \"response_radio\",\n",
-        "   \"scope\": \"global\",\n",
-        "   \"required\": true,\n",
-        "   \"archived\": false,\n",
-        "   \"options\": [\n",
-        "    {\n",
-        "     \"schemaNodeId\": \"clpvq9d0002yw07zyci2q5adq\",\n",
-        "     \"featureSchemaId\": \"clpvq9d0002yv07zyevmz1yoj\",\n",
-        "     \"value\": \"response_a\",\n",
-        "     \"label\": \"response_a\",\n",
-        "     \"position\": 0,\n",
-        "     \"options\": []\n",
-        "    },\n",
-        "    {\n",
-        "     \"schemaNodeId\": \"clpvq9d0002yy07zy8pe48zdj\",\n",
-        "     \"featureSchemaId\": \"clpvq9d0002yx07zy0jvmdxk8\",\n",
-        "     \"value\": \"response_b\",\n",
-        "     \"label\": \"response_b\",\n",
-        "     \"position\": 1,\n",
-        "     \"options\": []\n",
-        "    }\n",
-        "   ]\n",
-        "  },\n",
-        "  {\n",
-        "   \"schemaNodeId\": \"clpvq9d0002z107zygf8l62ys\",\n",
-        "   \"featureSchemaId\": \"clpvq9d0002z007zyg26115f9\",\n",
-        "   \"type\": \"response-text\",\n",
-        "   \"name\": \"provide_a_reason_for_your_choice\",\n",
-        "   \"instructions\": \"Provide a reason for your choice\",\n",
-        "   \"scope\": \"global\",\n",
-        "   \"required\": true,\n",
-        "   \"archived\": false,\n",
-        "   \"options\": [],\n",
-        "   \"minCharacters\": 5,\n",
-        "   \"maxCharacters\": 100\n",
-        "  },\n",
-        "  {\n",
-        "   \"schemaNodeId\": \"clpvq9d0102z907zy8b10hjcj\",\n",
-        "   \"featureSchemaId\": \"clpvq9d0002z207zy6xla7f82\",\n",
-        "   \"type\": \"response-checklist\",\n",
-        "   \"name\": \"response_checklist\",\n",
-        "   \"instructions\": \"response_checklist\",\n",
-        "   \"scope\": \"global\",\n",
-        "   \"required\": true,\n",
-        "   \"archived\": false,\n",
-        "   \"options\": [\n",
-        "    {\n",
-        "     \"schemaNodeId\": \"clpvq9d0102z407zy0adq0rfr\",\n",
-        "     \"featureSchemaId\": \"clpvq9d0002z307zy6dqb8xsw\",\n",
-        "     \"value\": \"response_a\",\n",
-        "     \"label\": \"response_a\",\n",
-        "     \"position\": 0,\n",
-        "     \"options\": []\n",
-        "    },\n",
-        "    {\n",
-        "     \"schemaNodeId\": \"clpvq9d0102z607zych8b2z5d\",\n",
-        "     \"featureSchemaId\": \"clpvq9d0102z507zyfwfgacrn\",\n",
-        "     \"value\": \"response_c\",\n",
-        "     \"label\": \"response_c\",\n",
-        "     \"position\": 1,\n",
-        "     \"options\": []\n",
-        "    },\n",
-        "    {\n",
-        "     \"schemaNodeId\": \"clpvq9d0102z807zy03y7gysp\",\n",
-        "     \"featureSchemaId\": \"clpvq9d0102z707zyh61y5o3u\",\n",
-        "     \"value\": \"response_d\",\n",
-        "     \"label\": \"response_d\",\n",
-        "     \"position\": 2,\n",
-        "     \"options\": []\n",
-        "    }\n",
-        "   ]\n",
-        "  }\n",
-        " ],\n",
-        " \"realTime\": false\n",
-        "}\n",
-        "\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [\n",
-        "    prompt_annotation_ndjson,\n",
-        "    response_radio_annotation_ndjson,\n",
-        "    response_text_annotation_ndjson,\n",
-        "    response_checklist_annotation_ndjson\n",
-        "    ]:\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Upload annotations to a project as pre-labels or complete labels"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.get_project(project_id=project_id)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Assisted Labeling (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
-        "    predictions=label_ndjson)\n",
-        "\n",
-        "upload_job.wait_until_done()\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "    labels=label_ndjson)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# LLM Data Generation with MAL and Ground Truth\n",
+                "This demo is meant to showcase how to generate prompts and responses to fine-tune large language models (LLMs) using MAL and Ground truth"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Set up "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for LLM data generation\n",
+                "Currently, we only support NDJson format for prompt and responses"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Prompt:"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "prompt_annotation_ndjson = {\n    \"name\": \"Follow the prompt and select answers\",\n    \"answer\": \"This is an example of a prompt\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Responses:"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Radio (single-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "response_radio_annotation_ndjson = {\n    \"name\": \"response_radio\",\n    \"answer\": {\n        \"name\": \"response_a\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Only NDJson is currently supported\nresponse_text_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is an example of a response text\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Checklist (multi-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "response_checklist_annotation_ndjson = {\n    \"name\": \"response_checklist\",\n    \"answer\": [{\n        \"name\": \"response_a\"\n    }, {\n        \"name\": \"response_c\"\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Create a project and data rows in Labelbox UI\n",
+                "\n",
+                "Currently we do not support this workflow through the SDK.\n",
+                "#### Workflow:\n",
+                "\n",
+                "1. Navigate to annotate and select ***New project***\n",
+                "\n",
+                "2. Select ***LLM data generation*** and then select ***Humans generate prompts and responses***\n",
+                "\n",
+                "3. Name your project, select ***create a new dataset*** and name your dataset. (data rows will be generated automatically in \n",
+                "this step)\n",
+                "\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Enter the project id\nproject_id = \"\"\n\n# Select one of the global keys from the data rows generated\nglobal_key = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2 : Create/select an Ontology in Labelbox UI"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Currently we do not support this workflow through the SDK\n",
+                "#### Workflow: \n",
+                "1. In your project, navigate to ***Settings*** and ***Label editor***\n",
+                "\n",
+                "2. Click on ***Edit***\n",
+                "\n",
+                "3. Create a new ontology and add the features used in this demo\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### For this demo the following ontology was generated in the UI: "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_json = \"\"\"\n{\n \"tools\": [],\n \"relationships\": [],\n \"classifications\": [\n  {\n   \"schemaNodeId\": \"clpvq9d0002yt07zy0khq42rp\",\n   \"featureSchemaId\": \"clpvq9d0002ys07zyf2eo9p14\",\n   \"type\": \"prompt\",\n   \"name\": \"Follow the prompt and select answers\",\n   \"archived\": false,\n   \"required\": true,\n   \"options\": [],\n   \"instructions\": \"Follow the prompt and select answers\",\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002yz07zy0fjg28z7\",\n   \"featureSchemaId\": \"clpvq9d0002yu07zy28ik5w3i\",\n   \"type\": \"response-radio\",\n   \"name\": \"response_radio\",\n   \"instructions\": \"response_radio\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0002yw07zyci2q5adq\",\n     \"featureSchemaId\": \"clpvq9d0002yv07zyevmz1yoj\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0002yy07zy8pe48zdj\",\n     \"featureSchemaId\": \"clpvq9d0002yx07zy0jvmdxk8\",\n     \"value\": \"response_b\",\n     \"label\": \"response_b\",\n     \"position\": 1,\n     \"options\": []\n    }\n   ]\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002z107zygf8l62ys\",\n   \"featureSchemaId\": \"clpvq9d0002z007zyg26115f9\",\n   \"type\": \"response-text\",\n   \"name\": \"provide_a_reason_for_your_choice\",\n   \"instructions\": \"Provide a reason for your choice\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [],\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0102z907zy8b10hjcj\",\n   \"featureSchemaId\": \"clpvq9d0002z207zy6xla7f82\",\n   \"type\": \"response-checklist\",\n   \"name\": \"response_checklist\",\n   \"instructions\": \"response_checklist\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0102z407zy0adq0rfr\",\n     \"featureSchemaId\": \"clpvq9d0002z307zy6dqb8xsw\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z607zych8b2z5d\",\n     \"featureSchemaId\": \"clpvq9d0102z507zyfwfgacrn\",\n     \"value\": \"response_c\",\n     \"label\": \"response_c\",\n     \"position\": 1,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z807zy03y7gysp\",\n     \"featureSchemaId\": \"clpvq9d0102z707zyh61y5o3u\",\n     \"value\": \"response_d\",\n     \"label\": \"response_d\",\n     \"position\": 2,\n     \"options\": []\n    }\n   ]\n  }\n ],\n \"realTime\": false\n}\n\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        prompt_annotation_ndjson,\n        response_radio_annotation_ndjson,\n        response_text_annotation_ndjson,\n        response_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Upload annotations to a project as pre-labels or complete labels"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.get_project(project_id=project_id)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Assisted Labeling (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/dicom.ipynb b/examples/annotation_import/dicom.ipynb
index f7beb1822..34771d7c9 100644
--- a/examples/annotation_import/dicom.ipynb
+++ b/examples/annotation_import/dicom.ipynb
@@ -1,462 +1,268 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td> "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# DICOM Annotation Import\n",
-        "\n",
-        "* Annotations must be created and uploaded using NDJSON\n",
-        "* Supported annotations that can be uploaded through the SDK:\n",
-        "    * Polyline\n",
-        "    * Segmentation masks     \n",
-        "* **NOT** supported:\n",
-        "    * Bounding box\n",
-        "    * Point\n",
-        "    * Polygons\n",
-        "    * Free form text classifications\n",
-        "    * Radio classifications \n",
-        "    * Checklist classifications"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q 'labelbox[data]'"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Replace with your API key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY=None\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for DICOM\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Polyline ########\n",
-        "polyline_annotation = [\n",
-        "    lb_types.DICOMObjectAnnotation(\n",
-        "        name=\"line_dicom\",\n",
-        "        group_key=lb_types.GroupKey.AXIAL,\n",
-        "        frame=1,\n",
-        "        value=lb_types.Line(points=[\n",
-        "            lb_types.Point(x=10, y=10),\n",
-        "            lb_types.Point(x=200, y=20),\n",
-        "            lb_types.Point(x=250, y=250)\n",
-        "        ]),\n",
-        "        segment_index=0,\n",
-        "        keyframe=True,\n",
-        "    ),\n",
-        "    lb_types.DICOMObjectAnnotation(\n",
-        "        name=\"line_dicom\",\n",
-        "        group_key=lb_types.GroupKey.AXIAL,\n",
-        "        frame=20,\n",
-        "        value=lb_types.Line(points=[\n",
-        "            lb_types.Point(x=10, y=10),\n",
-        "            lb_types.Point(x=200, y=10),\n",
-        "            lb_types.Point(x=300, y=300)\n",
-        "        ]),\n",
-        "        segment_index=1,\n",
-        "        keyframe=True,\n",
-        "    ),\n",
-        "]\n",
-        "\n",
-        "polyline_annotation_ndjson = {\n",
-        "  'name': 'line_dicom',\n",
-        "  'groupKey': 'axial', # should be 'axial', 'sagittal', or 'coronal'\n",
-        "  'segments': [\n",
-        "    {\n",
-        "    'keyframes': [{\n",
-        "        'frame': 1,\n",
-        "        'line': [\n",
-        "            {'x': 10, 'y': 10},\n",
-        "            {'x': 200, 'y': 20},\n",
-        "            {'x': 250, 'y': 250},\n",
-        "        ]\n",
-        "    }]},\n",
-        "    {\n",
-        "    'keyframes' : [{\n",
-        "        'frame': 20,\n",
-        "        'line': [\n",
-        "            {'x': 10, 'y': 10},\n",
-        "            {'x': 200, 'y': 10},\n",
-        "            {'x': 300, 'y': 300},\n",
-        "        ]\n",
-        "    }]}\n",
-        "    ],\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Segmentation Masks ########\n",
-        "\n",
-        "mask_annotation = [\n",
-        "    lb_types.DICOMMaskAnnotation(\n",
-        "    group_key='axial',\n",
-        "    frames=[\n",
-        "        lb_types.MaskFrame(\n",
-        "            index=1,\n",
-        "            instance_uri=\"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\"\n",
-        "        ),\n",
-        "        lb_types.MaskFrame(\n",
-        "            index=5,\n",
-        "            instance_uri=\"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\"\n",
-        "        )\n",
-        "    ],\n",
-        "    instances=[\n",
-        "        lb_types.MaskInstance(\n",
-        "            color_rgb=(255, 255, 255),\n",
-        "            name=\"segmentation_mask_dicom\"\n",
-        "        )\n",
-        "    ])\n",
-        "]\n",
-        "\n",
-        "mask_annotation_ndjson = {\n",
-        "    'groupKey': 'axial',\n",
-        "    'masks': {\n",
-        "        'frames': [{\n",
-        "            'index': 1,\n",
-        "            'instanceURI': \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\"\n",
-        "        }, {\n",
-        "            'index': 5,\n",
-        "            'instanceURI': \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\"\n",
-        "        }],\n",
-        "        'instances': [\n",
-        "            {\n",
-        "                'colorRGB': (255, 255, 255),\n",
-        "                'name': 'segmentation_mask_dicom'\n",
-        "            }\n",
-        "        ]\n",
-        "    },\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annotations - putting it all together"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"sample-dicom-1.dcm\"\n",
-        "asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-dicom-1.dcm\",\n",
-        "    \"global_key\": global_key,\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"dicom_demo_dataset\")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors :\",task.errors)\n",
-        "print(\"Failed data rows:\" ,task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 2: Create/select an ontology\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the line annotation above, we provided the `name` as `line_dicom`. Now, when we setup our ontology, we must ensure that the name of my line tool is also `line_dicom`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-        "\n",
-        "\n",
-        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"segmentation_mask_dicom\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_dicom\"),\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology DICOM Annotations\", ontology_builder.asdict(), media_type=lb.MediaType.Dicom)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 3: Create a labeling project \n",
-        "Connect the ontology to the labeling project."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
-        "# Queue mode will be deprecated once dataset mode is deprecated\n",
-        "\n",
-        "project = client.create_project(name=\"dicom_project_demo\", media_type=lb.MediaType.Dicom)\n",
-        "\n",
-        "## connect ontology to your project\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create batches\n",
-        "\n",
-        "# Create a batch to send to your MAL project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-dicom-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # a list of data row objects, data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 5: Create the annotations payload \n",
-        "Create the annotations payload using the snippets of code above.\n",
-        "\n",
-        "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python Annotation Types"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "annotations_list = polyline_annotation + mask_annotation\n",
-        "labels = [\n",
-        "    lb_types.Label(\n",
-        "        data={\"global_key\" : global_key },\n",
-        "        annotations=annotations_list\n",
-        "    )\n",
-        "]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### NDJSON annotations\n",
-        "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "\n",
-        "for annotation in [\n",
-        "    polyline_annotation_ndjson,\n",
-        "    mask_annotation_ndjson\n",
-        "]:\n",
-        "  annotation.update({\n",
-        "      'dataRow': {\n",
-        "          'globalKey': global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotation)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
-        "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model-Assisted Labeling (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload MAL label for this data row in project\n",
-        "upload_job_mal = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"mal_import_job-\" + str(uuid.uuid4()),\n",
-        "    predictions=labels)\n",
-        "\n",
-        "upload_job_mal.wait_until_done();\n",
-        "print(\"Errors:\", upload_job_mal.errors)\n",
-        "print(\"Status of uploads: \", upload_job_mal.statuses)\n",
-        "print(\"   \")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_label_import = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name = \"label_import_job-\" + str(uuid.uuid4()),\n",
-        "    labels=labels\n",
-        ")\n",
-        "\n",
-        "upload_job_label_import.wait_until_done()\n",
-        "print(\"Errors:\", upload_job_label_import.errors)\n",
-        "print(\"Status of uploads: \", upload_job_label_import.statuses)\n",
-        "print(\"   \")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Delete Project\n",
-        "# project.delete()\n",
-        "# dataset.delete()\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# DICOM Annotation Import\n",
+                "\n",
+                "* Annotations must be created and uploaded using NDJSON\n",
+                "* Supported annotations that can be uploaded through the SDK:\n",
+                "    * Polyline\n",
+                "    * Segmentation masks     \n",
+                "* **NOT** supported:\n",
+                "    * Bounding box\n",
+                "    * Point\n",
+                "    * Polygons\n",
+                "    * Free form text classifications\n",
+                "    * Radio classifications \n",
+                "    * Checklist classifications"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q 'labelbox[data]'",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Replace with your API key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for DICOM\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "######## Polyline ########\npolyline_annotation = [\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=1,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=20),\n            lb_types.Point(x=250, y=250),\n        ]),\n        segment_index=0,\n        keyframe=True,\n    ),\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=20,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=10),\n            lb_types.Point(x=300, y=300),\n        ]),\n        segment_index=1,\n        keyframe=True,\n    ),\n]\n\npolyline_annotation_ndjson = {\n    \"name\":\n        \"line_dicom\",\n    \"groupKey\":\n        \"axial\",  # should be 'axial', 'sagittal', or 'coronal'\n    \"segments\": [\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    1,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 20\n                    },\n                    {\n                        \"x\": 250,\n                        \"y\": 250\n                    },\n                ],\n            }]\n        },\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    20,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 300,\n                        \"y\": 300\n                    },\n                ],\n            }]\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Segmentation Masks ########\n\nmask_annotation = [\n    lb_types.DICOMMaskAnnotation(\n        group_key=\"axial\",\n        frames=[\n            lb_types.MaskFrame(\n                index=1,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n            lb_types.MaskFrame(\n                index=5,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n        ],\n        instances=[\n            lb_types.MaskInstance(color_rgb=(255, 255, 255),\n                                  name=\"segmentation_mask_dicom\")\n        ],\n    )\n]\n\nmask_annotation_ndjson = {\n    \"groupKey\": \"axial\",\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\":\n                    1,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n            {\n                \"index\":\n                    5,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": (255, 255, 255),\n            \"name\": \"segmentation_mask_dicom\"\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annotations - putting it all together"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"sample-dicom-1.dcm\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-dicom-1.dcm\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"dicom_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors :\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 2: Create/select an ontology\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we create the line annotation above, we provided the `name` as `line_dicom`. Now, when we setup our ontology, we must ensure that the name of my line tool is also `line_dicom`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+                "\n",
+                "\n",
+                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(\n        tool=lb.Tool.Type.RASTER_SEGMENTATION,\n        name=\"segmentation_mask_dicom\",\n    ),\n    lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_dicom\"),\n])\n\nontology = client.create_ontology(\n    \"Ontology DICOM Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Dicom,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 3: Create a labeling project \n",
+                "Connect the ontology to the labeling project."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"dicom_project_demo\",\n                                media_type=lb.MediaType.Dicom)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create batches\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-dicom-demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # a list of data row objects, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 5: Create the annotations payload \n",
+                "Create the annotations payload using the snippets of code above.\n",
+                "\n",
+                "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python Annotation Types"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "annotations_list = polyline_annotation + mask_annotation\nlabels = [\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=annotations_list)\n]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### NDJSON annotations\n",
+                "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\n\nfor annotation in [polyline_annotation_ndjson, mask_annotation_ndjson]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
+                "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model-Assisted Labeling (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job-\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)\nprint(\"   \")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/html.ipynb b/examples/annotation_import/html.ipynb
index 4a1e388a1..d4bcc1b9b 100644
--- a/examples/annotation_import/html.ipynb
+++ b/examples/annotation_import/html.ipynb
@@ -1,577 +1,308 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# HTML Annotation Import\n",
-        "* This notebook will provide examples of each supported annotation type for HTML assets, and also  cover MAL and Label Import methods:\n",
-        "\n",
-        "Suported annotations that can be uploaded through the SDK\n",
-        "\n",
-        "* Classification Radio \n",
-        "* Classification Checklist \n",
-        "* Classification Free Text \n",
-        "\n",
-        "**Not** supported annotations\n",
-        "\n",
-        "* Bouding box\n",
-        "* NER\n",
-        "* Polygon \n",
-        "* Point\n",
-        "* Polyline \n",
-        "* Segmentation Mask\n",
-        "\n",
-        "MAL and Label Import:\n",
-        "\n",
-        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Notes:\n",
-        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace with your API key\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for HTML"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Classification free text #####\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"text_html\",\n",
-        "    value=lb_types.Text(answer=\"sample text\"),\n",
-        ")\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "    \"name\": \"text_html\",\n",
-        "    \"answer\": \"sample text\",\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Checklist Classification #######\n",
-        "\n",
-        "checklist_annotation= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_html\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        " )\n",
-        "\n",
-        "\n",
-        "checklist_annotation_ndjson = {\n",
-        "    \"name\": \"checklist_html\",\n",
-        "    \"answers\": [\n",
-        "        {\"name\": \"first_checklist_answer\"},\n",
-        "        {\"name\": \"second_checklist_answer\"}\n",
-        "    ],\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Radio Classification ######\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_html\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"second_radio_answer\")))\n",
-        "\n",
-        "radio_annotation_ndjson = {\n",
-        "    \"name\": \"radio_html\",\n",
-        "    \"answer\": {\n",
-        "        \"name\": \"first_radio_answer\"\n",
-        "    },\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\")\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation_ndjson= {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
-        "\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\")]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n",
-        "\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annotations - putting it all together "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create one Labelbox dataset\n",
-        "\n",
-        "global_key = \"sample_html_1.html\"\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_1.html\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(\n",
-        "    name=\"html_annotation_import_demo_dataset\",\n",
-        "    iam_integration=None # Removing this argument will default to the organziation's default iam integration\n",
-        ")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\", task.errors)\n",
-        "print(\"Failed data rows: \", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an ontology\n",
-        "\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the text annotation, we provided the `name` as `text_html`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_html`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"text_html\"),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_html\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_html\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"nested_radio_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\",\n",
-        "          options=[\n",
-        "              lb.Classification(\n",
-        "                class_type=lb.Classification.Type.RADIO,\n",
-        "                name=\"sub_radio_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                ]\n",
-        "            ),\n",
-        "          ]\n",
-        "        )\n",
-        "      ],\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology HTML Annotations\", ontology_builder.asdict(), media_type=lb.MediaType.Html)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "## Step 3: Create a labeling project\n",
-        "Connect the ontology to the labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create Labelbox project\n",
-        "project = client.create_project(name=\"HTML Import Annotation Demo\",\n",
-        "                                    media_type=lb.MediaType.Html)\n",
-        "\n",
-        "# Setup your ontology\n",
-        "project.setup_editor(ontology) # Connect your ontology and editor to your project"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup Batches and Ontology\n",
-        "\n",
-        "# Create a batch to send to your MAL project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-html-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the annotations payload\n",
-        "Create the annotations payload using the snippets of code above\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python annotation\n",
-        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "   data={\"global_key\" : global_key },\n",
-        "    annotations=[\n",
-        "      text_annotation,\n",
-        "      checklist_annotation,\n",
-        "      radio_annotation,\n",
-        "      nested_checklist_annotation,\n",
-        "      nested_radio_annotation\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### NDJSON annotations \n",
-        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [text_annotation_ndjson,\n",
-        "                    checklist_annotation_ndjson,\n",
-        "                    radio_annotation_ndjson,\n",
-        "                    nested_radio_annotation_ndjson,\n",
-        "                    nested_checklist_annotation_ndjson\n",
-        "                    ]:\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Assisted Labeling (MAL)\n",
-        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload our label using Model-Assisted Labeling\n",
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
-        "    predictions=label)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload label for this data row in project\n",
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# HTML Annotation Import\n",
+                "* This notebook will provide examples of each supported annotation type for HTML assets, and also  cover MAL and Label Import methods:\n",
+                "\n",
+                "Suported annotations that can be uploaded through the SDK\n",
+                "\n",
+                "* Classification Radio \n",
+                "* Classification Checklist \n",
+                "* Classification Free Text \n",
+                "\n",
+                "**Not** supported annotations\n",
+                "\n",
+                "* Bouding box\n",
+                "* NER\n",
+                "* Polygon \n",
+                "* Point\n",
+                "* Polyline \n",
+                "* Segmentation Mask\n",
+                "\n",
+                "MAL and Label Import:\n",
+                "\n",
+                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Notes:\n",
+                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Replace with your API key\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for HTML"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_html\",\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_html\",\n    \"answer\": \"sample text\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_html\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_html\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_html\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_html\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annotations - putting it all together "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample_html_1.html\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_1.html\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"html_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an ontology\n",
+                "\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we create the text annotation, we provided the `name` as `text_html`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_html`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_html\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_html\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_html\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"nested_checklist_question\",\n        options=[\n            lb.Option(\n                \"first_checklist_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.CHECKLIST,\n                        name=\"sub_checklist_question\",\n                        options=[lb.Option(\"first_sub_checklist_answer\")],\n                    )\n                ],\n            )\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"nested_radio_question\",\n        options=[\n            lb.Option(\n                value=\"first_radio_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.RADIO,\n                        name=\"sub_radio_question\",\n                        options=[lb.Option(value=\"first_sub_radio_answer\")],\n                    ),\n                ],\n            )\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology HTML Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "\n",
+                "## Step 3: Create a labeling project\n",
+                "Connect the ontology to the labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create Labelbox project\nproject = client.create_project(name=\"HTML Import Annotation Demo\",\n                                media_type=lb.MediaType.Html)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-html-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the annotations payload\n",
+                "Create the annotations payload using the snippets of code above\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python annotation\n",
+                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### NDJSON annotations \n",
+                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Assisted Labeling (MAL)\n",
+                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/image.ipynb b/examples/annotation_import/image.ipynb
index ec6e64d6c..aaccd029d 100644
--- a/examples/annotation_import/image.ipynb
+++ b/examples/annotation_import/image.ipynb
@@ -1,1059 +1,437 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Image annotation import\n",
-        "This notebook will provide examples of each supported annotation type for image assets.\n",
-        "\n",
-        "### [Model-assisted labeling (MAL)](https://docs.labelbox.com/docs/model-assisted-labeling)\n",
-        "\n",
-        "* This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.\n",
-        "\n",
-        "The imported annotations will be pre-populated in the labeling editor. However, in order to convert the pre-labels to real annotations, a human labeler will still need to open the Data Row in the Editor and submit it. This functionality is designed to speed up human labeling.\n",
-        "\n",
-        "### [Import ground truth](https://docs.labelbox.com/docs/import-ground-truth)\n",
-        "\n",
-        "* This  workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.\n",
-        "\n",
-        "### Python annotation types vs NDJSON\n",
-        "**Python annotation type (recommended)**\n",
-        "- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.\n",
-        "\n",
-        "- Allows you to build annotations locally with local file paths, numpy arrays, or URLs\n",
-        "\n",
-        "- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox\n",
-        "\n",
-        "- It supports one-level nested classification (free text / radio / checklist) under the object or classification annotation.\n",
-        "\n",
-        "**NDJSON**\n",
-        "- Skip formatting annotation payload in the Python Annotation Types format just to convert back to NDJSON\n",
-        "\n",
-        "- Ability to create the payload in the NDJSON import format directly\n",
-        "\n",
-        "- It supports any levels of nested classification (free text / radio / checklist) under the object or classification annotation."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import uuid\n",
-        "from PIL import Image\n",
-        "import requests\n",
-        "import base64\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "from io import BytesIO\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API key\n",
-        "\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY =\"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for image\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification : Radio (single-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"second_radio_answer\")))\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_annotation_ndjson = {\n",
-        "    \"name\": \"radio_question\",\n",
-        "    \"answer\": {\n",
-        "        \"name\": \"second_radio_answer\"\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Checklist (multi-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.Checklist(answer=[\n",
-        "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n",
-        "    ]))\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_annotation_ndjson = {\n",
-        "    \"name\": \"checklist_question\",\n",
-        "    \"answer\": [{\n",
-        "        \"name\": \"first_checklist_answer\"\n",
-        "    }, {\n",
-        "        \"name\": \"second_checklist_answer\"\n",
-        "    }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Nested radio and checklist\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "# NDJSON\n",
-        "nested_radio_annotation_ndjson = {\n",
-        "    \"name\": \"nested_radio_question\",\n",
-        "    \"answer\": {\n",
-        "        \"name\": \"first_radio_answer\",\n",
-        "          \"classifications\": [{\n",
-        "              \"name\": \"sub_radio_question\",\n",
-        "              \"answer\": {\n",
-        "                  \"name\": \"first_sub_radio_answer\"\n",
-        "              }\n",
-        "          }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "    \"name\": \"nested_checklist_question\",\n",
-        "    \"answer\": [{\n",
-        "        \"name\": \"first_checklist_answer\",\n",
-        "        \"classifications\": [{\n",
-        "            \"name\": \"sub_checklist_question\",\n",
-        "            \"answer\": {\n",
-        "                \"name\": \"first_sub_checklist_answer\"\n",
-        "            }\n",
-        "        }]\n",
-        "    }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"free_text\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.Text(answer=\"sample text\"))\n",
-        "\n",
-        "# NDJSON\n",
-        "text_annotation_ndjson = {\n",
-        "    \"name\": \"free_text\",\n",
-        "    \"answer\": \"sample text\",\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Relationship with bounding box\n",
-        "> **NOTE:**  \n",
-        "> Only supported for MAL imports"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "bbox_source = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=2096, y=1264),\n",
-        "        end=lb_types.Point(x=2240, y=1689),\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "bbox_target = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=2272, y=1346),\n",
-        "        end=lb_types.Point(x=2416, y=1704),\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "relationship = lb_types.RelationshipAnnotation(\n",
-        "    name=\"relationship\",\n",
-        "    value=lb_types.Relationship(\n",
-        "        source=bbox_source,\n",
-        "        target=bbox_target,\n",
-        "        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n",
-        "    ))\n",
-        "\n",
-        "## Only supported for MAL imports\n",
-        "uuid_source = str(uuid.uuid4())\n",
-        "uuid_target = str(uuid.uuid4())\n",
-        "\n",
-        "bbox_source_ndjson = {\n",
-        "    \"uuid\": uuid_source,\n",
-        "    \"name\": \"bounding_box\",\n",
-        "    \"bbox\": {\n",
-        "        \"top\": 1264.0,\n",
-        "        \"left\": 2096.0,\n",
-        "        \"height\": 425.0,\n",
-        "        \"width\": 144.0\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "bbox_target_ndjson = {\n",
-        "    \"uuid\": uuid_target,\n",
-        "    \"name\": \"bounding_box\",\n",
-        "    \"bbox\": {\n",
-        "        \"top\": 1346.0,\n",
-        "        \"left\": 2272.0,\n",
-        "        \"height\": 358.0,\n",
-        "        \"width\": 144.0\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "relationship_ndjson = {\n",
-        "    \"name\": \"relationship\",\n",
-        "    \"relationship\": {\n",
-        "        \"source\": uuid_source,\n",
-        "        \"target\": uuid_target,\n",
-        "        \"type\": \"unidirectional\"\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding box"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=1915, y=1307),  # x= left + width , y = top + height\n",
-        "    ))\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_annotation_ndjson = {\n",
-        "    \"name\": \"bounding_box\",\n",
-        "    \"bbox\": {\n",
-        "        \"top\": 977,\n",
-        "        \"left\": 1690,\n",
-        "        \"height\": 330,\n",
-        "        \"width\": 225\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding box with nested classification"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"sub_radio_question\",\n",
-        "            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "                name=\"first_sub_radio_answer\")))\n",
-        "    ])\n",
-        "\n",
-        "## NDJSON\n",
-        "bbox_with_radio_subclass_ndjson = {\n",
-        "    \"name\": \"bbox_with_radio_subclass\",\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"sub_radio_question\",\n",
-        "        \"answer\": {\n",
-        "            \"name\": \"first_sub_radio_answer\"\n",
-        "        }\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "        \"top\": 933,\n",
-        "        \"left\": 541,\n",
-        "        \"height\": 191,\n",
-        "        \"width\": 330\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polygon"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "polygon_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"polygon\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n",
-        "        points=[\n",
-        "            lb_types.Point(x=1489.581, y=183.934),\n",
-        "            lb_types.Point(x=2278.306, y=256.885),\n",
-        "            lb_types.Point(x=2428.197, y=200.437),\n",
-        "            lb_types.Point(x=2560.0, y=335.419),\n",
-        "            lb_types.Point(x=2557.386, y=503.165),\n",
-        "            lb_types.Point(x=2320.596, y=503.103),\n",
-        "            lb_types.Point(x=2156.083, y=628.943),\n",
-        "            lb_types.Point(x=2161.111, y=785.519),\n",
-        "            lb_types.Point(x=2002.115, y=894.647),\n",
-        "            lb_types.Point(x=1838.456, y=877.874),\n",
-        "            lb_types.Point(x=1436.53, y=874.636),\n",
-        "            lb_types.Point(x=1411.403, y=758.579),\n",
-        "            lb_types.Point(x=1353.853, y=751.74),\n",
-        "            lb_types.Point(x=1345.264, y=453.461),\n",
-        "            lb_types.Point(x=1426.011, y=421.129)\n",
-        "        ]))\n",
-        "\n",
-        "# NDJSON\n",
-        "polygon_annotation_ndjson = {\n",
-        "  \"name\": \"polygon\",\n",
-        "  \"polygon\": [\n",
-        "    {\"x\": 1489.581, \"y\": 183.934},\n",
-        "    {\"x\": 2278.306, \"y\": 256.885},\n",
-        "    {\"x\": 2428.197, \"y\": 200.437},\n",
-        "    {\"x\": 2560.0, \"y\": 335.419},\n",
-        "    {\"x\": 2557.386, \"y\": 503.165},\n",
-        "    {\"x\": 2320.596, \"y\": 503.103},\n",
-        "    {\"x\": 2156.083, \"y\": 628.943},\n",
-        "    {\"x\": 2161.111, \"y\": 785.519},\n",
-        "    {\"x\": 2002.115, \"y\": 894.647},\n",
-        "    {\"x\": 1838.456, \"y\": 877.874},\n",
-        "    {\"x\": 1436.53, \"y\": 874.636},\n",
-        "    {\"x\": 1411.403, \"y\": 758.579},\n",
-        "    {\"x\": 1353.853, \"y\": 751.74},\n",
-        "    {\"x\": 1345.264, \"y\": 453.461},\n",
-        "    {\"x\": 1426.011, \"y\": 421.129},\n",
-        "    {\"x\": 1489.581, \"y\": 183.934}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Composite mask upload using different mask tools from the project's ontology\n",
-        "This example shows how to assigned different annotations (mask instances) from a composite mask using different mask tools"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# First we need to extract all the unique colors from the composite mask\n",
-        "def extract_rgb_colors_from_url(image_url):\n",
-        "    response = requests.get(image_url)\n",
-        "    img = Image.open(BytesIO(response.content))\n",
-        "\n",
-        "    colors = set()\n",
-        "    for x in range(img.width):\n",
-        "        for y in range(img.height):\n",
-        "            pixel = img.getpixel((x, y))\n",
-        "            if pixel[:3] != (0,0,0):\n",
-        "                colors.add(pixel[:3])  # Get only the RGB values\n",
-        "\n",
-        "    return colors"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "cp_mask_url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/composite_mask.png\"\n",
-        "colors = extract_rgb_colors_from_url(cp_mask_url)\n",
-        "response = requests.get(cp_mask_url)\n",
-        "\n",
-        "mask_data = lb.types.MaskData(im_bytes=response.content) # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\n",
-        "rgb_colors_for_mask_with_text_subclass_tool = [(73, 39, 85), (111, 87, 176), (23, 169, 254)]\n",
-        "\n",
-        "cp_mask = []\n",
-        "for color in colors:\n",
-        "  # We are assigning the color related to the mask_with_text_subclass tool by identifying the unique RGB colors\n",
-        "  if color in rgb_colors_for_mask_with_text_subclass_tool:\n",
-        "    cp_mask.append(\n",
-        "      lb_types.ObjectAnnotation(\n",
-        "        name = \"mask_with_text_subclass\", # must match your ontology feature\"s name\n",
-        "        value=lb_types.Mask(\n",
-        "          mask=mask_data,\n",
-        "          color=color),\n",
-        "        classifications=[\n",
-        "          lb_types.ClassificationAnnotation(\n",
-        "            name=\"sub_free_text\",\n",
-        "            value=lb_types.Text(answer=\"free text answer sample\")\n",
-        "          )]\n",
-        "      )\n",
-        "  )\n",
-        "  else:\n",
-        "     # Create ObjectAnnotation for other masks\n",
-        "    cp_mask.append(\n",
-        "      lb_types.ObjectAnnotation(\n",
-        "        name=\"mask\",\n",
-        "        value=lb_types.Mask(\n",
-        "            mask=mask_data,\n",
-        "            color=color\n",
-        "        )\n",
-        "    )\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "# NDJSON using bytes array\n",
-        "cp_mask_ndjson = []\n",
-        "\n",
-        "#Using bytes array.\n",
-        "response = requests.get(cp_mask_url)\n",
-        "im_bytes = base64.b64encode(response.content).decode('utf-8')\n",
-        "for color in colors:\n",
-        "  if color in rgb_colors_for_mask_with_text_subclass_tool:\n",
-        "    cp_mask_ndjson.append({\n",
-        "      \"name\": \"mask_with_text_subclass\",\n",
-        "      \"mask\": {\"imBytes\": im_bytes,\n",
-        "      \"colorRGB\": color },\n",
-        "      \"classifications\":[{\n",
-        "        \"name\": \"sub_free_text\",\n",
-        "        \"answer\": \"free text answer\"\n",
-        "      }]\n",
-        "    }\n",
-        "  )\n",
-        "  else:\n",
-        "    cp_mask_ndjson.append({\n",
-        "          \"name\": \"mask\",\n",
-        "          \"classifications\": [],\n",
-        "          \"mask\": {\n",
-        "            \"imBytes\": im_bytes,\n",
-        "            \"colorRGB\": color\n",
-        "      }\n",
-        "    }\n",
-        "  )\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Point"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "point_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"point\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.Point(x=1166.606, y=1441.768),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "point_annotation_ndjson = {\n",
-        "    \"name\": \"point\",\n",
-        "    \"classifications\": [],\n",
-        "    \"point\": {\n",
-        "        \"x\": 1166.606,\n",
-        "        \"y\": 1441.768\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polyline"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "polyline_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"polyline\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.Line(  # Coordinates for the keypoints in your polyline\n",
-        "        points=[\n",
-        "            lb_types.Point(x=2534.353, y=249.471),\n",
-        "            lb_types.Point(x=2429.492, y=182.092),\n",
-        "            lb_types.Point(x=2294.322, y=221.962),\n",
-        "            lb_types.Point(x=2224.491, y=180.463),\n",
-        "            lb_types.Point(x=2136.123, y=204.716),\n",
-        "            lb_types.Point(x=1712.247, y=173.949),\n",
-        "            lb_types.Point(x=1703.838, y=84.438),\n",
-        "            lb_types.Point(x=1579.772, y=82.61),\n",
-        "            lb_types.Point(x=1583.442, y=167.552),\n",
-        "            lb_types.Point(x=1478.869, y=164.903),\n",
-        "            lb_types.Point(x=1418.941, y=318.149),\n",
-        "            lb_types.Point(x=1243.128, y=400.815),\n",
-        "            lb_types.Point(x=1022.067, y=319.007),\n",
-        "            lb_types.Point(x=892.367, y=379.216),\n",
-        "            lb_types.Point(x=670.273, y=364.408),\n",
-        "            lb_types.Point(x=613.114, y=288.16),\n",
-        "            lb_types.Point(x=377.559, y=238.251),\n",
-        "            lb_types.Point(x=368.087, y=185.064),\n",
-        "            lb_types.Point(x=246.557, y=167.286),\n",
-        "            lb_types.Point(x=236.648, y=285.61),\n",
-        "            lb_types.Point(x=90.929, y=326.412)\n",
-        "        ]),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "polyline_annotation_ndjson = {\n",
-        "  \"name\": \"polyline\",\n",
-        "  \"classifications\": [],\n",
-        "  \"line\": [\n",
-        "    {\"x\": 2534.353, \"y\": 249.471},\n",
-        "    {\"x\": 2429.492, \"y\": 182.092},\n",
-        "    {\"x\": 2294.322, \"y\": 221.962},\n",
-        "    {\"x\": 2224.491, \"y\": 180.463},\n",
-        "    {\"x\": 2136.123, \"y\": 204.716},\n",
-        "    {\"x\": 1712.247, \"y\": 173.949},\n",
-        "    {\"x\": 1703.838, \"y\": 84.438},\n",
-        "    {\"x\": 1579.772, \"y\": 82.61},\n",
-        "    {\"x\": 1583.442, \"y\": 167.552},\n",
-        "    {\"x\": 1478.869, \"y\": 164.903},\n",
-        "    {\"x\": 1418.941, \"y\": 318.149},\n",
-        "    {\"x\": 1243.128, \"y\": 400.815},\n",
-        "    {\"x\": 1022.067, \"y\": 319.007},\n",
-        "    {\"x\": 892.367, \"y\": 379.216},\n",
-        "    {\"x\": 670.273, \"y\": 364.408},\n",
-        "    {\"x\": 613.114, \"y\": 288.16},\n",
-        "    {\"x\": 377.559, \"y\": 238.251},\n",
-        "    {\"x\": 368.087, \"y\": 185.064},\n",
-        "    {\"x\": 246.557, \"y\": 167.286},\n",
-        "    {\"x\": 236.648, \"y\": 285.61},\n",
-        "    {\"x\": 90.929, \"y\": 326.412}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# End-to-end example: Import pre-labels or ground truth"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into catalog\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as batch to the project\n",
-        "global_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n",
-        "\n",
-        "test_img_url = {\n",
-        "    \"row_data\":\n",
-        "        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n",
-        "    \"global_key\":\n",
-        "        global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"image-demo-dataset\")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(dataset)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an ontology\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we created the bounding box annotation above, we provided the `name` as `bounding_box`. Now, when we setup our ontology, we must ensure that the name of the bounding box tool is also `bounding_box`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    classifications=[  # List of Classification objects\n",
-        "        lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
-        "                          name=\"radio_question\",\n",
-        "                          options=[\n",
-        "                              lb.Option(value=\"first_radio_answer\"),\n",
-        "                              lb.Option(value=\"second_radio_answer\")\n",
-        "                          ]),\n",
-        "        lb.Classification(class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                          name=\"checklist_question\",\n",
-        "                          options=[\n",
-        "                              lb.Option(value=\"first_checklist_answer\"),\n",
-        "                              lb.Option(value=\"second_checklist_answer\")\n",
-        "                          ]),\n",
-        "        lb.Classification(class_type=lb.Classification.Type.TEXT,\n",
-        "                          name=\"free_text\"),\n",
-        "        lb.Classification(\n",
-        "            class_type=lb.Classification.Type.RADIO,\n",
-        "            name=\"nested_radio_question\",\n",
-        "            options=[\n",
-        "                lb.Option(\"first_radio_answer\",\n",
-        "                          options=[\n",
-        "                              lb.Classification(\n",
-        "                                  class_type=lb.Classification.Type.RADIO,\n",
-        "                                  name=\"sub_radio_question\",\n",
-        "                                  options=[lb.Option(\"first_sub_radio_answer\")])\n",
-        "                          ])\n",
-        "            ]),\n",
-        "        lb.Classification(\n",
-        "            class_type=lb.Classification.Type.CHECKLIST,\n",
-        "            name=\"nested_checklist_question\",\n",
-        "            options=[\n",
-        "                lb.Option(\n",
-        "                    \"first_checklist_answer\",\n",
-        "                    options=[\n",
-        "                        lb.Classification(\n",
-        "                            class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                            name=\"sub_checklist_question\",\n",
-        "                            options=[lb.Option(\"first_sub_checklist_answer\")])\n",
-        "                    ])\n",
-        "            ]),\n",
-        "    ],\n",
-        "    tools=[  # List of Tool objects\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX,\n",
-        "                name=\"bbox_with_radio_subclass\",\n",
-        "                classifications=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(value=\"first_sub_radio_answer\")]),\n",
-        "                ]),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION,\n",
-        "                name=\"mask_with_text_subclass\",\n",
-        "                classifications=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.TEXT,\n",
-        "                        name=\"sub_free_text\")\n",
-        "                    ]\n",
-        "                ),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")\n",
-        "    ])\n",
-        "\n",
-        "ontology = client.create_ontology(\"Image Annotation Import Demo Ontology\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Image\n",
-        "                                  )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a labeling project\n",
-        "Connect the ontology to the labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
-        "# Queue mode will be deprecated once dataset mode is deprecated\n",
-        "project = client.create_project(name=\"Image Annotation Import Demo\",\n",
-        "                                media_type=lb.MediaType.Image)\n",
-        "\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send a batch of data rows to the project\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "batch = project.create_batch(\n",
-        "    \"image-demo-batch\",  # each batch in a project must have a unique name\n",
-        "    global_keys=[\n",
-        "        global_key\n",
-        "    ],  # paginated collection of data row objects, list of data row ids or global keys\n",
-        "    priority=1  # priority between 1(highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(f\"Batch: {batch}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the annotations payload\n",
-        "\n",
-        "Create the annotations payload using the snippets of code above\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Python annotations\n",
-        "\n",
-        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "annotations = [\n",
-        "    radio_annotation,\n",
-        "    nested_radio_annotation,\n",
-        "    checklist_annotation,\n",
-        "    nested_checklist_annotation,\n",
-        "    text_annotation,\n",
-        "    bbox_annotation,\n",
-        "    bbox_with_radio_subclass_annotation,\n",
-        "    polygon_annotation,\n",
-        "    point_annotation,\n",
-        "    polyline_annotation,\n",
-        "    bbox_source,\n",
-        "    bbox_target,\n",
-        "    relationship\n",
-        "] + cp_mask\n",
-        "\n",
-        "label.append(\n",
-        "    lb_types.Label(data={\"global_key\" : global_key },\n",
-        "                   annotations=annotations))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### NDJSON annotations\n",
-        "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "annotations = [\n",
-        "    radio_annotation_ndjson,\n",
-        "    nested_radio_annotation_ndjson,\n",
-        "    nested_checklist_annotation_ndjson,\n",
-        "    checklist_annotation_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    bbox_annotation_ndjson,\n",
-        "    bbox_with_radio_subclass_ndjson,\n",
-        "    polygon_annotation_ndjson,\n",
-        "    point_annotation_ndjson,\n",
-        "    polyline_annotation_ndjson,\n",
-        "    bbox_source_ndjson,\n",
-        "    bbox_target_ndjson,\n",
-        "    relationship_ndjson,  ## Only supported for MAL imports\n",
-        "] + cp_mask_ndjson\n",
-        "\n",
-        "for annotation in annotations:\n",
-        "    annotation.update({\n",
-        "        \"dataRow\": {\n",
-        "            \"globalKey\": global_key\n",
-        "        }\n",
-        "    })\n",
-        "    label_ndjson.append(annotation)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6: Upload annotations to a project as pre-labels or ground truth\n",
-        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Option A: Upload to a labeling project as pre-labels (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# upload MAL labels for this data row in project\n",
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client=client,\n",
-        "    project_id=project.uid,\n",
-        "    name=\"mal_job\" + str(uuid.uuid4()),\n",
-        "    predictions=label\n",
-        ")\n",
-        "upload_job.wait_until_done()\n",
-        "\n",
-        "print(f\"Errors: {upload_job.errors}\")\n",
-        "print(f\"Status of uploads: {upload_job.statuses}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Option B: Upload to a labeling project using ground truth"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Relationships are not supported with LabelImport\n",
-        "# For this demo either run MAL or Ground Truth, not both\n",
-        "\n",
-        "# Upload label for this data row in project\n",
-        "# upload_job = lb.LabelImport.create_from_objects(\n",
-        "#     client = client,\n",
-        "#     project_id = project.uid,\n",
-        "#     name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "#     labels=label)\n",
-        "\n",
-        "# print(\"Errors:\", upload_job.errors)\n",
-        "# print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Image annotation import\n",
+                "This notebook will provide examples of each supported annotation type for image assets.\n",
+                "\n",
+                "### [Model-assisted labeling (MAL)](https://docs.labelbox.com/docs/model-assisted-labeling)\n",
+                "\n",
+                "* This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.\n",
+                "\n",
+                "The imported annotations will be pre-populated in the labeling editor. However, in order to convert the pre-labels to real annotations, a human labeler will still need to open the Data Row in the Editor and submit it. This functionality is designed to speed up human labeling.\n",
+                "\n",
+                "### [Import ground truth](https://docs.labelbox.com/docs/import-ground-truth)\n",
+                "\n",
+                "* This  workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.\n",
+                "\n",
+                "### Python annotation types vs NDJSON\n",
+                "**Python annotation type (recommended)**\n",
+                "- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.\n",
+                "\n",
+                "- Allows you to build annotations locally with local file paths, numpy arrays, or URLs\n",
+                "\n",
+                "- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox\n",
+                "\n",
+                "- It supports one-level nested classification (free text / radio / checklist) under the object or classification annotation.\n",
+                "\n",
+                "**NDJSON**\n",
+                "- Skip formatting annotation payload in the Python Annotation Types format just to convert back to NDJSON\n",
+                "\n",
+                "- Ability to create the payload in the NDJSON import format directly\n",
+                "\n",
+                "- It supports any levels of nested classification (free text / radio / checklist) under the object or classification annotation."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API key\n",
+                "\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for image\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification : Radio (single-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Checklist (multi-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Nested radio and checklist\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "nested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Relationship with bounding box\n",
+                "> **NOTE:**  \n",
+                "> Only supported for MAL imports"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2096, y=1264),\n        end=lb_types.Point(x=2240, y=1689),\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2272, y=1346),\n        end=lb_types.Point(x=2416, y=1704),\n    ),\n)\n\nrelationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"uuid\": uuid_source,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1264.0,\n        \"left\": 2096.0,\n        \"height\": 425.0,\n        \"width\": 144.0\n    },\n}\n\nbbox_target_ndjson = {\n    \"uuid\": uuid_target,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1346.0,\n        \"left\": 2272.0,\n        \"height\": 358.0,\n        \"width\": 144.0\n    },\n}\n\nrelationship_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding box"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding box with nested classification"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\")),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\"\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polygon"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",  # must match your ontology feature\"s name\n    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n        points=[\n            lb_types.Point(x=1489.581, y=183.934),\n            lb_types.Point(x=2278.306, y=256.885),\n            lb_types.Point(x=2428.197, y=200.437),\n            lb_types.Point(x=2560.0, y=335.419),\n            lb_types.Point(x=2557.386, y=503.165),\n            lb_types.Point(x=2320.596, y=503.103),\n            lb_types.Point(x=2156.083, y=628.943),\n            lb_types.Point(x=2161.111, y=785.519),\n            lb_types.Point(x=2002.115, y=894.647),\n            lb_types.Point(x=1838.456, y=877.874),\n            lb_types.Point(x=1436.53, y=874.636),\n            lb_types.Point(x=1411.403, y=758.579),\n            lb_types.Point(x=1353.853, y=751.74),\n            lb_types.Point(x=1345.264, y=453.461),\n            lb_types.Point(x=1426.011, y=421.129),\n        ]),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Composite mask upload using different mask tools from the project's ontology\n",
+                "This example shows how to assigned different annotations (mask instances) from a composite mask using different mask tools"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First we need to extract all the unique colors from the composite mask\ndef extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "cp_mask_url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/composite_mask.png\"\ncolors = extract_rgb_colors_from_url(cp_mask_url)\nresponse = requests.get(cp_mask_url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nrgb_colors_for_mask_with_text_subclass_tool = [\n    (73, 39, 85),\n    (111, 87, 176),\n    (23, 169, 254),\n]\n\ncp_mask = []\nfor color in colors:\n    # We are assigning the color related to the mask_with_text_subclass tool by identifying the unique RGB colors\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask.append(\n            lb_types.ObjectAnnotation(\n                name=\n                \"mask_with_text_subclass\",  # must match your ontology feature\"s name\n                value=lb_types.Mask(mask=mask_data, color=color),\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_free_text\",\n                        value=lb_types.Text(answer=\"free text answer sample\"),\n                    )\n                ],\n            ))\n    else:\n        # Create ObjectAnnotation for other masks\n        cp_mask.append(\n            lb_types.ObjectAnnotation(name=\"mask\",\n                                      value=lb_types.Mask(mask=mask_data,\n                                                          color=color)))\n\n# NDJSON using bytes array\ncp_mask_ndjson = []\n\n# Using bytes array.\nresponse = requests.get(cp_mask_url)\nim_bytes = base64.b64encode(response.content).decode(\"utf-8\")\nfor color in colors:\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask_ndjson.append({\n            \"name\":\n                \"mask_with_text_subclass\",\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n            \"classifications\": [{\n                \"name\": \"sub_free_text\",\n                \"answer\": \"free text answer\"\n            }],\n        })\n    else:\n        cp_mask_ndjson.append({\n            \"name\": \"mask\",\n            \"classifications\": [],\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n        })",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Point"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",  # must match your ontology feature\"s name\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point\",\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polyline"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",  # must match your ontology feature\"s name\n    value=lb_types.Line(  # Coordinates for the keypoints in your polyline\n        points=[\n            lb_types.Point(x=2534.353, y=249.471),\n            lb_types.Point(x=2429.492, y=182.092),\n            lb_types.Point(x=2294.322, y=221.962),\n            lb_types.Point(x=2224.491, y=180.463),\n            lb_types.Point(x=2136.123, y=204.716),\n            lb_types.Point(x=1712.247, y=173.949),\n            lb_types.Point(x=1703.838, y=84.438),\n            lb_types.Point(x=1579.772, y=82.61),\n            lb_types.Point(x=1583.442, y=167.552),\n            lb_types.Point(x=1478.869, y=164.903),\n            lb_types.Point(x=1418.941, y=318.149),\n            lb_types.Point(x=1243.128, y=400.815),\n            lb_types.Point(x=1022.067, y=319.007),\n            lb_types.Point(x=892.367, y=379.216),\n            lb_types.Point(x=670.273, y=364.408),\n            lb_types.Point(x=613.114, y=288.16),\n            lb_types.Point(x=377.559, y=238.251),\n            lb_types.Point(x=368.087, y=185.064),\n            lb_types.Point(x=246.557, y=167.286),\n            lb_types.Point(x=236.648, y=285.61),\n            lb_types.Point(x=90.929, y=326.412),\n        ]),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# End-to-end example: Import pre-labels or ground truth"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into catalog\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"image-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(dataset)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an ontology\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we created the bounding box annotation above, we provided the `name` as `bounding_box`. Now, when we setup our ontology, we must ensure that the name of the bounding box tool is also `bounding_box`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a labeling project\n",
+                "Connect the ontology to the labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"Image Annotation Import Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send a batch of data rows to the project\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "batch = project.create_batch(\n    \"image-demo-batch\",  # each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the annotations payload\n",
+                "\n",
+                "Create the annotations payload using the snippets of code above\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Python annotations\n",
+                "\n",
+                "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n    bbox_source,\n    bbox_target,\n    relationship,\n] + cp_mask\n\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### NDJSON annotations\n",
+                "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nannotations = [\n    radio_annotation_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    checklist_annotation_ndjson,\n    text_annotation_ndjson,\n    bbox_annotation_ndjson,\n    bbox_with_radio_subclass_ndjson,\n    polygon_annotation_ndjson,\n    point_annotation_ndjson,\n    polyline_annotation_ndjson,\n    bbox_source_ndjson,\n    bbox_target_ndjson,\n    relationship_ndjson,  ## Only supported for MAL imports\n] + cp_mask_ndjson\n\nfor annotation in annotations:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6: Upload annotations to a project as pre-labels or ground truth\n",
+                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Option A: Upload to a labeling project as pre-labels (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# upload MAL labels for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=label,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Option B: Upload to a labeling project using ground truth"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Relationships are not supported with LabelImport\n# For this demo either run MAL or Ground Truth, not both\n\n# Upload label for this data row in project\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=label)\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/import_labeled_dataset_image.ipynb b/examples/annotation_import/import_labeled_dataset_image.ipynb
index 52e55203d..e049f2da4 100644
--- a/examples/annotation_import/import_labeled_dataset_image.ipynb
+++ b/examples/annotation_import/import_labeled_dataset_image.ipynb
@@ -1,407 +1,187 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from labelbox.schema.data_row_metadata import DataRowMetadataField, DataRowMetadataKind\n",
-        "import datetime\n",
-        "import random\n",
-        "import os\n",
-        "import json\n",
-        "from PIL import Image\n",
-        "from labelbox.schema.ontology import OntologyBuilder, Tool\n",
-        "import requests\n",
-        "from tqdm.notebook import tqdm\n",
-        "import uuid\n",
-        "from labelbox.data.annotation_types import Label, ImageData, ObjectAnnotation, Rectangle, Point"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup Labelbox client"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Initialize the Labelbox client\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Download a public dataset\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Function to download files\n",
-        "def download_files(filemap):\n",
-        "    path, uri = filemap\n",
-        "    if not os.path.exists(path):\n",
-        "        response = requests.get(uri, stream=True)\n",
-        "        with open(path, 'wb') as f:\n",
-        "            for chunk in response.iter_content(chunk_size=8192):\n",
-        "                f.write(chunk)\n",
-        "    return path"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Download data rows and annotations\n",
-        "DATA_ROWS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json\"\n",
-        "ANNOTATIONS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json\"\n",
-        "download_files((\"data_rows.json\", DATA_ROWS_URL))\n",
-        "download_files((\"annotations.json\", ANNOTATIONS_URL))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Load data rows and annotations\n",
-        "with open('data_rows.json') as fp:\n",
-        "    data_rows = json.load(fp)\n",
-        "with open('annotations.json') as fp:\n",
-        "    annotations = json.load(fp)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new dataset\n",
-        "dataset = client.create_dataset(name=\"Geospatial vessel detection\")\n",
-        "print(f\"Created dataset with ID: {dataset.uid}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Import Data Rows with Metadata"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Here is an example of adding two metadata fields to your Data Rows: a \"captureDateTime\" field with datetime value, and a \"tag\" field with string value\n",
-        "metadata_ontology = client.get_data_row_metadata_ontology()\n",
-        "datetime_schema_id = metadata_ontology.reserved_by_name[\"captureDateTime\"].uid\n",
-        "tag_schema_id = metadata_ontology.reserved_by_name[\"tag\"].uid\n",
-        "tag_items = [\"WorldView-1\", \"WorldView-2\", \"WorldView-3\", \"WorldView-4\"]\n",
-        "\n",
-        "for datarow in tqdm(data_rows):\n",
-        "    dt = datetime.datetime.utcnow() + datetime.timedelta(days=random.random()*30) # this is random datetime value\n",
-        "    tag_item = random.choice(tag_items) # this is a random tag value\n",
-        "\n",
-        "    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.\n",
-        "    metadata_fields = [\n",
-        "                       DataRowMetadataField(schema_id=datetime_schema_id, value=dt),\n",
-        "                       DataRowMetadataField(schema_id=tag_schema_id, value=tag_item)\n",
-        "                       ]\n",
-        "\n",
-        "    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.\n",
-        "    # metadata_fields = [\n",
-        "    #                    {\"schema_id\": datetime_schema_id, \"value\": dt},\n",
-        "    #                    {\"schema_id\": tag_schema_id, \"value\": tag_item}\n",
-        "    #                    ]\n",
-        "\n",
-        "    datarow[\"metadata_fields\"] = metadata_fields"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "task = dataset.create_data_rows(data_rows)\n",
-        "task.wait_till_done()\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Examine a Data Row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "datarow = next(dataset.data_rows())\n",
-        "print(datarow)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup a labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Initialize the OntologyBuilder\n",
-        "ontology_builder = OntologyBuilder()\n",
-        "\n",
-        "# Assuming 'annotations' is defined and contains the necessary data\n",
-        "for category in annotations['categories']:\n",
-        "    print(category['name'])\n",
-        "    # Add tools to the ontology builder\n",
-        "    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category['name']))\n",
-        "\n",
-        "# Create the ontology in Labelbox\n",
-        "ontology = client.create_ontology(\"Vessel Detection Ontology\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Image)\n",
-        "print(f\"Created ontology with ID: {ontology.uid}\")\n",
-        "\n",
-        "# Create a project and set up the ontology\n",
-        "project = client.create_project(name=\"Vessel Detection\", media_type=lb.MediaType.Image)\n",
-        "project.setup_editor(ontology=ontology)\n",
-        "print(f\"Created project with ID: {project.uid}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client.enable_experimental = True\n",
-        "\n",
-        "# Minimal ExportV2 parameters focused solely on data row IDs\n",
-        "export_params = {\n",
-        "    \"data_row_details\": True  # Only export data row details\n",
-        "}\n",
-        "\n",
-        "# Initiate the streamable export task from catalog\n",
-        "dataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID\n",
-        "export_task = dataset.export(params=export_params)\n",
-        "export_task.wait_till_done()\n",
-        "print(export_task)\n",
-        "\n",
-        "data_rows = []\n",
-        "\n",
-        "# Callback used for JSON Converter to correctly collect data row IDs\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "    # Parse the JSON string to access the data\n",
-        "    data = json.loads(output.json_str)\n",
-        "\n",
-        "    # Correctly extract and append DataRow ID\n",
-        "    if 'data_row' in data and 'id' in data['data_row']:\n",
-        "        data_rows.append(data['data_row']['id'])\n",
-        "\n",
-        "# Process the stream if there are results\n",
-        "if export_task.has_result():\n",
-        "    export_task.get_stream(\n",
-        "        converter=lb.JsonConverter(),\n",
-        "        stream_type=lb.StreamType.RESULT\n",
-        "    ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)\n",
-        "sampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))\n",
-        "\n",
-        "# Create a new batch in the project and add the sampled data rows\n",
-        "batch = project.create_batch(\n",
-        "    \"Initial batch\",  # name of the batch\n",
-        "    sampled_data_rows,  # list of Data Rows\n",
-        "    1  # priority between 1-5\n",
-        ")\n",
-        "print(f\"Created batch with ID: {batch.uid}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# Set export parameters focused on data row details\n",
-        "export_params = {\n",
-        "    \"data_row_details\": True,  # Only export data row details\n",
-        "    \"batch_ids\": [batch.uid],  # Optional: Include batch ids to filter by specific batches\n",
-        "}\n",
-        "\n",
-        "# Initialize the streamable export task from project\n",
-        "export_task = project.export(params=export_params)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "data_rows = []\n",
-        "\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  data_row = json.loads(output.json_str)\n",
-        "  data_rows.append(data_row)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "labels = []\n",
-        "for datarow in data_rows:\n",
-        "    annotations_list = []\n",
-        "    # Access the 'data_row' dictionary first\n",
-        "    data_row_dict = datarow['data_row']\n",
-        "    folder = data_row_dict['external_id'].split(\"/\")[0]\n",
-        "    id = data_row_dict['external_id'].split(\"/\")[1]\n",
-        "\n",
-        "    if folder == \"positive_image_set\":\n",
-        "        for image in annotations['images']:\n",
-        "            if image['file_name'] == id:\n",
-        "                for annotation in annotations['annotations']:\n",
-        "                    if annotation['image_id'] == image['id']:\n",
-        "                        bbox = annotation['bbox']\n",
-        "                        category_id = annotation['category_id'] - 1\n",
-        "                        class_name = None\n",
-        "                        ontology = ontology_builder.asdict()  # Get the ontology dictionary\n",
-        "                        for category in ontology['tools']:\n",
-        "                            if category['name'] == annotations['categories'][category_id]['name']:\n",
-        "                                class_name = category['name']\n",
-        "                                break\n",
-        "                        if class_name:\n",
-        "                            annotations_list.append(ObjectAnnotation(\n",
-        "                                name=class_name,\n",
-        "                                value=Rectangle(start=Point(x=bbox[0], y=bbox[1]), end=Point(x=bbox[2]+bbox[0], y=bbox[3]+bbox[1]))\n",
-        "                            ))\n",
-        "    image_data = {\"uid\":data_row_dict['id']}\n",
-        "    labels.append(Label(data=image_data, annotations=annotations_list))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "    client=client,\n",
-        "    project_id=project.uid,\n",
-        "    name=f\"label_import_job_{str(uuid.uuid4())}\",\n",
-        "    labels=labels\n",
-        ")\n",
-        "\n",
-        "# Wait for the upload to finish and print the results\n",
-        "upload_job.wait_until_done()\n",
-        "\n",
-        "print(f\"Errors: {upload_job.errors}\")\n",
-        "print(f\"Status of uploads: {upload_job.statuses}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom labelbox.schema.data_row_metadata import (\n    DataRowMetadataField,\n    DataRowMetadataKind,\n)\nimport datetime\nimport random\nimport os\nimport json\nfrom PIL import Image\nfrom labelbox.schema.ontology import OntologyBuilder, Tool\nimport requests\nfrom tqdm.notebook import tqdm\nimport uuid\nfrom labelbox.data.annotation_types import (\n    Label,\n    ImageData,\n    ObjectAnnotation,\n    Rectangle,\n    Point,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup Labelbox client"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Initialize the Labelbox client\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Download a public dataset\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Function to download files\ndef download_files(filemap):\n    path, uri = filemap\n    if not os.path.exists(path):\n        response = requests.get(uri, stream=True)\n        with open(path, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n    return path",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Download data rows and annotations\nDATA_ROWS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json\"\nANNOTATIONS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json\"\ndownload_files((\"data_rows.json\", DATA_ROWS_URL))\ndownload_files((\"annotations.json\", ANNOTATIONS_URL))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Load data rows and annotations\nwith open(\"data_rows.json\") as fp:\n    data_rows = json.load(fp)\nwith open(\"annotations.json\") as fp:\n    annotations = json.load(fp)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Create a dataset"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new dataset\ndataset = client.create_dataset(name=\"Geospatial vessel detection\")\nprint(f\"Created dataset with ID: {dataset.uid}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Import Data Rows with Metadata"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Here is an example of adding two metadata fields to your Data Rows: a \"captureDateTime\" field with datetime value, and a \"tag\" field with string value\nmetadata_ontology = client.get_data_row_metadata_ontology()\ndatetime_schema_id = metadata_ontology.reserved_by_name[\"captureDateTime\"].uid\ntag_schema_id = metadata_ontology.reserved_by_name[\"tag\"].uid\ntag_items = [\"WorldView-1\", \"WorldView-2\", \"WorldView-3\", \"WorldView-4\"]\n\nfor datarow in tqdm(data_rows):\n    dt = datetime.datetime.utcnow() + datetime.timedelta(\n        days=random.random() * 30)  # this is random datetime value\n    tag_item = random.choice(tag_items)  # this is a random tag value\n\n    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.\n    metadata_fields = [\n        DataRowMetadataField(schema_id=datetime_schema_id, value=dt),\n        DataRowMetadataField(schema_id=tag_schema_id, value=tag_item),\n    ]\n\n    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.\n    # metadata_fields = [\n    #                    {\"schema_id\": datetime_schema_id, \"value\": dt},\n    #                    {\"schema_id\": tag_schema_id, \"value\": tag_item}\n    #                    ]\n\n    datarow[\"metadata_fields\"] = metadata_fields",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "task = dataset.create_data_rows(data_rows)\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Examine a Data Row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "datarow = next(dataset.data_rows())\nprint(datarow)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup a labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Initialize the OntologyBuilder\nontology_builder = OntologyBuilder()\n\n# Assuming 'annotations' is defined and contains the necessary data\nfor category in annotations[\"categories\"]:\n    print(category[\"name\"])\n    # Add tools to the ontology builder\n    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category[\"name\"]))\n\n# Create the ontology in Labelbox\nontology = client.create_ontology(\n    \"Vessel Detection Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\nprint(f\"Created ontology with ID: {ontology.uid}\")\n\n# Create a project and set up the ontology\nproject = client.create_project(name=\"Vessel Detection\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology=ontology)\nprint(f\"Created project with ID: {project.uid}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\n\n# Minimal ExportV2 parameters focused solely on data row IDs\nexport_params = {\"data_row_details\": True}  # Only export data row details\n\n# Initiate the streamable export task from catalog\ndataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID\nexport_task = dataset.export(params=export_params)\nexport_task.wait_till_done()\nprint(export_task)\n\ndata_rows = []\n\n\n# Callback used for JSON Converter to correctly collect data row IDs\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    # Parse the JSON string to access the data\n    data = json.loads(output.json_str)\n\n    # Correctly extract and append DataRow ID\n    if \"data_row\" in data and \"id\" in data[\"data_row\"]:\n        data_rows.append(data[\"data_row\"][\"id\"])\n\n\n# Process the stream if there are results\nif export_task.has_result():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.RESULT).start(\n                               stream_handler=json_stream_handler)\n\n# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)\nsampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))\n\n# Create a new batch in the project and add the sampled data rows\nbatch = project.create_batch(\n    \"Initial batch\",  # name of the batch\n    sampled_data_rows,  # list of Data Rows\n    1,  # priority between 1-5\n)\nprint(f\"Created batch with ID: {batch.uid}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Create annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set export parameters focused on data row details\nexport_params = {\n    \"data_row_details\": True,  # Only export data row details\n    \"batch_ids\": [batch.uid\n                 ],  # Optional: Include batch ids to filter by specific batches\n}\n\n# Initialize the streamable export task from project\nexport_task = project.export(params=export_params)\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nlabels = []\nfor datarow in data_rows:\n    annotations_list = []\n    # Access the 'data_row' dictionary first\n    data_row_dict = datarow[\"data_row\"]\n    folder = data_row_dict[\"external_id\"].split(\"/\")[0]\n    id = data_row_dict[\"external_id\"].split(\"/\")[1]\n\n    if folder == \"positive_image_set\":\n        for image in annotations[\"images\"]:\n            if image[\"file_name\"] == id:\n                for annotation in annotations[\"annotations\"]:\n                    if annotation[\"image_id\"] == image[\"id\"]:\n                        bbox = annotation[\"bbox\"]\n                        category_id = annotation[\"category_id\"] - 1\n                        class_name = None\n                        ontology = (ontology_builder.asdict()\n                                   )  # Get the ontology dictionary\n                        for category in ontology[\"tools\"]:\n                            if (category[\"name\"] == annotations[\"categories\"]\n                                [category_id][\"name\"]):\n                                class_name = category[\"name\"]\n                                break\n                        if class_name:\n                            annotations_list.append(\n                                ObjectAnnotation(\n                                    name=class_name,\n                                    value=Rectangle(\n                                        start=Point(x=bbox[0], y=bbox[1]),\n                                        end=Point(\n                                            x=bbox[2] + bbox[0],\n                                            y=bbox[3] + bbox[1],\n                                        ),\n                                    ),\n                                ))\n    image_data = {\"uid\": data_row_dict[\"id\"]}\n    labels.append(Label(data=image_data, annotations=annotations_list))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"label_import_job_{str(uuid.uuid4())}\",\n    labels=labels,\n)\n\n# Wait for the upload to finish and print the results\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/pdf.ipynb b/examples/annotation_import/pdf.ipynb
index 4465631f4..efd00aff2 100644
--- a/examples/annotation_import/pdf.ipynb
+++ b/examples/annotation_import/pdf.ipynb
@@ -1,1152 +1,387 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 1,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# PDF Annotation Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "Supported annotations for PDF assets \n",
-        "\n",
-        "*Annotation types*\n",
-        "- Checklist classification (including nested classifications)\n",
-        "- Radio classifications (including nested classifications)\n",
-        "- Free text classifications\n",
-        "- Bounding box\n",
-        "- Entities\n",
-        "- Relationships (only supported for MAL imports)\n",
-        "\n",
-        "\n",
-        "*NDJson*\n",
-        "- Checklist classification (including nested classifications)\n",
-        "- Radio classifications (including nested classifications)\n",
-        "- Free text classifications\n",
-        "- Bounding box \n",
-        "- Entities \n",
-        "- Relationships (only supported for MAL imports)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import uuid\n",
-        "import json\n",
-        "import requests\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Replace with your API key\n",
-        "Guides on https://docs.labelbox.com/docs/create-an-api-key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Supported Annotations"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Entity ##########\n",
-        "\n",
-        "# Annotation Types\n",
-        "entities_annotations = lb_types.ObjectAnnotation(\n",
-        "    name=\"named_entity\",\n",
-        "    value= lb_types.DocumentEntity(\n",
-        "        name=\"named_entity\",\n",
-        "        textSelections=[\n",
-        "            lb_types.DocumentTextSelection(\n",
-        "                token_ids=[],\n",
-        "                group_id=\"\",\n",
-        "                page=1\n",
-        "            )\n",
-        "        ]\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "entities_annotations_ndjson = {\n",
-        "    \"name\": \"named_entity\",\n",
-        "    \"textSelections\": [\n",
-        "        {\n",
-        "            \"tokenIds\": [\n",
-        "                \"<UUID>\",\n",
-        "            ],\n",
-        "            \"groupId\": \"<UUID>\",\n",
-        "            \"page\": 1,\n",
-        "        }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########### Radio Classification #########\n",
-        "\n",
-        "# Annotation types\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer =\n",
-        "        lb_types.ClassificationAnswer(name = \"first_radio_answer\")\n",
-        "    )\n",
-        ")\n",
-        "# NDJSON\n",
-        "radio_annotation_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\"}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############ Checklist Classification ###########\n",
-        "\n",
-        "# Annotation types\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\")\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_annotation_ndjson = {\n",
-        "  \"name\": \"checklist_question\",\n",
-        "  \"answer\": [\n",
-        "    {\"name\": \"first_checklist_answer\"},\n",
-        "    {\"name\": \"second_checklist_answer\"}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############ Bounding Box ###########\n",
-        "\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=102.771, y=135.3),  # x = left, y = top\n",
-        "        end=lb_types.Point(x=518.571, y=245.143),  # x= left + width , y = top + height\n",
-        "        page=0,\n",
-        "        unit=lb_types.RectangleUnit.POINTS\n",
-        "        )\n",
-        "    )\n",
-        "\n",
-        "bbox_annotation_ndjson = {\n",
-        "  \"name\": \"bounding_box\",\n",
-        "  \"bbox\": {\n",
-        "            \"top\": 135.3,\n",
-        "            \"left\": 102.771,\n",
-        "            \"height\": 109.843,\n",
-        "            \"width\": 415.8\n",
-        "      },\n",
-        "  \"page\": 0,\n",
-        "  \"unit\": \"POINTS\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# ############ global nested classifications ###########\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "\n",
-        "nested_radio_annotation_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############## Classification Free-form text ##############\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",  # must match your ontology feature\"s name\n",
-        "  value=lb_types.Text(answer=\"sample text\")\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######### BBOX with nested classifications #########\n",
-        "\n",
-        "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=317.271, y=226.757), # x = left, y = top\n",
-        "        end=lb_types.Point(x=566.657, y=420.986), # x= left + width , y = top + height\n",
-        "        unit=lb_types.RectangleUnit.POINTS,\n",
-        "        page=1\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(\n",
-        "          answer=lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_radio_answer\",\n",
-        "            classifications=[\n",
-        "              lb_types.ClassificationAnnotation(\n",
-        "                name=\"second_sub_radio_question\",\n",
-        "                value=lb_types.Radio(\n",
-        "                  answer=lb_types.ClassificationAnswer(\n",
-        "                    name=\"second_sub_radio_answer\"\n",
-        "                  )\n",
-        "                )\n",
-        "              )\n",
-        "            ]\n",
-        "          )\n",
-        "          )\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "bbox_with_radio_subclass_annotation_ndjson = {\n",
-        "  \"name\": \"bbox_with_radio_subclass\",\n",
-        "  \"classifications\": [\n",
-        "    {\n",
-        "      \"name\": \"sub_radio_question\",\n",
-        "      \"answer\": {\n",
-        "          \"name\": \"first_sub_radio_answer\",\n",
-        "          \"classifications\": [\n",
-        "              {\n",
-        "                  \"name\": \"second_sub_radio_question\",\n",
-        "                  \"answer\": {\n",
-        "                      \"name\": \"second_sub_radio_answer\"}\n",
-        "               }\n",
-        "            ]\n",
-        "        }\n",
-        "    }\n",
-        "  ],\n",
-        "  \"bbox\": {\n",
-        "        \"top\": 226.757,\n",
-        "        \"left\": 317.271,\n",
-        "        \"height\": 194.229,\n",
-        "        \"width\": 249.386\n",
-        "    },\n",
-        "  \"page\": 1,\n",
-        "  \"unit\": \"POINTS\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############ NER with nested classifications ########\n",
-        "\n",
-        "\n",
-        "ner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "  name=\"ner_with_checklist_subclass\",\n",
-        "  value=lb_types.DocumentEntity(\n",
-        "    name=\"ner_with_checklist_subclass\",\n",
-        "    text_selections=[\n",
-        "      lb_types.DocumentTextSelection(\n",
-        "        token_ids=[],\n",
-        "        group_id=\"\",\n",
-        "        page=1\n",
-        "      )\n",
-        "    ]\n",
-        "  ),\n",
-        "  classifications=[\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_checklist_question\",\n",
-        "      value=lb_types.Checklist(\n",
-        "      answer=[lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")]\n",
-        "      )\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "\n",
-        "ner_with_checklist_subclass_annotation_ndjson = {\n",
-        "  \"name\": \"ner_with_checklist_subclass\",\n",
-        "  \"classifications\":[\n",
-        "    {\n",
-        "      \"name\": \"sub_checklist_question\",\n",
-        "      \"answer\": [{\"name\": \"first_sub_checklist_answer\"}]\n",
-        "    }\n",
-        "  ],\n",
-        "  \"textSelections\": [\n",
-        "      {\n",
-        "          \"tokenIds\": [\n",
-        "              \"<UUID>\"\n",
-        "          ],\n",
-        "          \"groupId\": \"<UUID>\",\n",
-        "          \"page\": 1\n",
-        "      }\n",
-        "  ]\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######### Relationships ##########\n",
-        "entity_source = lb_types.ObjectAnnotation(\n",
-        "    name=\"named_entity\",\n",
-        "    value= lb_types.DocumentEntity(\n",
-        "        name=\"named_entity\",\n",
-        "        textSelections=[\n",
-        "            lb_types.DocumentTextSelection(\n",
-        "                token_ids=[],\n",
-        "                group_id=\"\",\n",
-        "                page=1\n",
-        "            )\n",
-        "        ]\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "entity_target = lb_types.ObjectAnnotation(\n",
-        "    name=\"named_entity\",\n",
-        "    value=lb_types.DocumentEntity(\n",
-        "      name=\"named_entity\",\n",
-        "      textSelections=[\n",
-        "        lb_types.DocumentTextSelection(\n",
-        "          token_ids=[],\n",
-        "          group_id=\"\",\n",
-        "          page=1\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "entity_relationship = lb_types.RelationshipAnnotation(\n",
-        "    name=\"relationship\",\n",
-        "    value=lb_types.Relationship(\n",
-        "        source=entity_source,\n",
-        "        target=entity_target,\n",
-        "        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n",
-        "    ))\n",
-        "\n",
-        "## Only supported for MAL imports\n",
-        "uuid_source = str(uuid.uuid4())\n",
-        "uuid_target = str(uuid.uuid4())\n",
-        "\n",
-        "entity_source_ndjson = {\n",
-        "  \"name\": \"named_entity\",\n",
-        "  \"uuid\": uuid_source,\n",
-        "  \"textSelections\": [\n",
-        "    {\n",
-        "      \"tokenIds\": [\n",
-        "        \"<UUID>\"\n",
-        "      ],\n",
-        "      \"groupId\": \"<UUID>\",\n",
-        "      \"page\": 1\n",
-        "    }\n",
-        "  ]\n",
-        "\n",
-        "}\n",
-        "\n",
-        "entity_target_ndjson = {\n",
-        "  \"name\": \"named_entity\",\n",
-        "  \"uuid\": uuid_target,\n",
-        "  \"textSelections\": [\n",
-        "    {\n",
-        "      \"tokenIds\": [\n",
-        "        \"<UUID>\"\n",
-        "      ],\n",
-        "      \"groupId\": \"<UUID>\",\n",
-        "      \"page\": 1\n",
-        "    }\n",
-        "  ]\n",
-        "}\n",
-        "ner_relationship_annotation_ndjson = {\n",
-        "    \"name\": \"relationship\",\n",
-        "    \"relationship\": {\n",
-        "      \"source\": uuid_source,\n",
-        "      \"target\": uuid_target,\n",
-        "      \"type\": \"unidirectional\"\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######### BBOX with relationships #############\n",
-        "# Python Annotation\n",
-        "bbox_source = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=188.257, y=68.875), # x = left, y = top\n",
-        "        end=lb_types.Point(x=270.907, y=149.556), # x = left + width , y = top + height\n",
-        "        unit=lb_types.RectangleUnit.POINTS,\n",
-        "        page=1\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "bbox_target = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=96.424, y=66.251),\n",
-        "        end=lb_types.Point(x=179.074, y=146.932),\n",
-        "        unit=lb_types.RectangleUnit.POINTS,\n",
-        "        page=1\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "bbox_relationship = lb_types.RelationshipAnnotation(\n",
-        "    name=\"relationship\",\n",
-        "    value=lb_types.Relationship(\n",
-        "        source=bbox_source,\n",
-        "        target=bbox_target,\n",
-        "        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n",
-        "    ))\n",
-        "\n",
-        "\n",
-        "## Only supported for MAL imports\n",
-        "uuid_source_2 = str(uuid.uuid4())\n",
-        "uuid_target_2 = str(uuid.uuid4())\n",
-        "\n",
-        "bbox_source_ndjson = {\n",
-        "  \"name\": \"bounding_box\",\n",
-        "  \"uuid\": uuid_source_2,\n",
-        "  \"bbox\":  {\n",
-        "            \"top\": 68.875,\n",
-        "            \"left\": 188.257,\n",
-        "            \"height\": 80.681,\n",
-        "            \"width\": 82.65\n",
-        "        },\n",
-        "  \"page\": 1,\n",
-        "  \"unit\": \"POINTS\"\n",
-        "}\n",
-        "\n",
-        "bbox_target_ndjson = {\n",
-        "  \"name\": \"bounding_box\",\n",
-        "  \"uuid\": uuid_target_2,\n",
-        "  \"bbox\":  {\n",
-        "            \"top\": 66.251,\n",
-        "            \"left\": 96.424,\n",
-        "            \"height\": 80.681,\n",
-        "            \"width\": 82.65\n",
-        "        },\n",
-        "  \"page\": 1,\n",
-        "  \"unit\": \"POINTS\"\n",
-        "}\n",
-        "\n",
-        "bbox_relationship_annotation_ndjson = {\n",
-        "    \"name\": \"relationship\",\n",
-        "    \"relationship\": {\n",
-        "      \"source\": uuid_source_2,\n",
-        "      \"target\": uuid_target_2,\n",
-        "      \"type\": \"unidirectional\"\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annotations - putting it all together "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 1: Import data rows into Catalog "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
-        "\n",
-        "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
-        "- The document must have no more than 15 pages.\n",
-        "- The file size should not exceed 20 MB.\n",
-        "\n",
-        "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
-        "\n",
-        "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
-        "\n",
-        "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"0801.3483_doc.pdf\"\n",
-        "img_url = {\n",
-        "    \"row_data\": {\n",
-        "      \"pdf_url\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n",
-        "    },\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"pdf_demo_dataset\")\n",
-        "task = dataset.create_data_rows([img_url])\n",
-        "task.wait_till_done()\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 2: Create/select an Ontology for your project\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup the ontology and link the tools created above.\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL,\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL,\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        scope = lb.Classification.Scope.GLOBAL,\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ])\n",
-        "          ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL,\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ])\n",
-        "      ]\n",
-        "    ),\n",
-        "  ],\n",
-        "  tools=[ # List of Tool objects\n",
-        "    lb.Tool( tool=lb.Tool.Type.BBOX,name=\"bounding_box\"),\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n",
-        "    lb.Tool(tool=lb.Tool.Type.RELATIONSHIP,name=\"relationship\"),\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER,\n",
-        "            name=\"ner_with_checklist_subclass\",\n",
-        "            classifications=[\n",
-        "              lb.Classification(\n",
-        "                class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                name=\"sub_checklist_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_checklist_answer\")\n",
-        "                ]\n",
-        "              )\n",
-        "          ]),\n",
-        "    lb.Tool( tool=lb.Tool.Type.BBOX,\n",
-        "            name=\"bbox_with_radio_subclass\",\n",
-        "            classifications=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.RADIO,\n",
-        "                  name=\"sub_radio_question\",\n",
-        "                  options=[\n",
-        "                    lb.Option(\n",
-        "                      value=\"first_sub_radio_answer\" ,\n",
-        "                      options=[\n",
-        "                        lb.Classification(\n",
-        "                          class_type=lb.Classification.Type.RADIO,\n",
-        "                          name=\"second_sub_radio_question\",\n",
-        "                          options=[lb.Option(\"second_sub_radio_answer\")]\n",
-        "                        )]\n",
-        "                    )]\n",
-        "                )]\n",
-        "      )]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Document Annotation Import Demo\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Document)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 3: Creating a labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"PDF_annotation_demo\",\n",
-        "                                    media_type=lb.MediaType.Document)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"PDF_annotation_batch\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 5. Create the annotation payload\n",
-        "Create the annotations payload using the snippets of code in Supported predictions section.\n",
-        "\n",
-        "Labelbox support NDJSON only for this data type.\n",
-        "\n",
-        "The resulting label should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Step 5.1: First, we need to populate the text selections for Entity annotations\n",
-        "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "To extract the generated text layer url we first need to export the data row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "client.enable_experimental = True\n",
-        "task = lb.DataRow.export(client=client,global_keys=[global_key])\n",
-        "task.wait_till_done()\n",
-        "stream = task.get_stream()\n",
-        "\n",
-        "text_layer = \"\"\n",
-        "for output in stream:\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "    text_layer = output_json['media_attributes']['text_layer_url']\n",
-        "print(text_layer)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# Helper method\n",
-        "def update_text_selections(annotation, group_id, list_tokens, page):\n",
-        "  return annotation.update({\n",
-        "    \"textSelections\": [\n",
-        "      {\n",
-        "        \"groupId\": group_id,\n",
-        "        \"tokenIds\": list_tokens,\n",
-        "        \"page\": page\n",
-        "      }\n",
-        "    ]\n",
-        "  })\n",
-        "\n",
-        "\n",
-        "# Fetch the content of the text layer\n",
-        "res = requests.get(text_layer)\n",
-        "\n",
-        "# Phrases that we want to annotation obtained from the text layer url\n",
-        "content_phrases = [\"Metal-insulator (MI) transitions have been one of the\" ,\n",
-        "                   \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n",
-        "                   \"Organic charge transfer salts based on the donor\",\n",
-        "                   \"the experimental investigations on this issue have not\"]\n",
-        "\n",
-        "# Parse the text layer\n",
-        "text_selections = []\n",
-        "text_selections_ner = []\n",
-        "text_selections_source = []\n",
-        "text_selections_target = []\n",
-        "\n",
-        "for obj in json.loads(res.text):\n",
-        "  for group in obj[\"groups\"]:\n",
-        "    if group[\"content\"] == content_phrases[0]:\n",
-        "      list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n",
-        "      # build text selections for Python Annotation Types\n",
-        "      document_text_selection = lb_types.DocumentTextSelection(groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n",
-        "      text_selections.append(document_text_selection)\n",
-        "      # build text selection for the NDJson annotations\n",
-        "      update_text_selections(annotation=entities_annotations_ndjson,\n",
-        "                             group_id=group[\"id\"], # id representing group of words\n",
-        "                             list_tokens=list_tokens, # ids representing individual words from the group\n",
-        "                             page=1)\n",
-        "    if group[\"content\"] == content_phrases[1]:\n",
-        "      list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n",
-        "      # build text selections for Python Annotation Types\n",
-        "      ner_text_selection = lb_types.DocumentTextSelection(groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n",
-        "      text_selections_ner.append(ner_text_selection)\n",
-        "      # build text selection for the NDJson annotations\n",
-        "      update_text_selections(annotation=ner_with_checklist_subclass_annotation_ndjson,\n",
-        "                             group_id=group[\"id\"], # id representing group of words\n",
-        "                             list_tokens=list_tokens_2, # ids representing individual words from the group\n",
-        "                             page=1)\n",
-        "    if group[\"content\"] == content_phrases[2]:\n",
-        "      relationship_source = [x[\"id\"] for x in group[\"tokens\"]]\n",
-        "      # build text selections for Python Annotation Types\n",
-        "      text_selection_entity_source = lb_types.DocumentTextSelection(groupId=group[\"id\"], tokenIds=relationship_source, page=1)\n",
-        "      text_selections_source.append(text_selection_entity_source)\n",
-        "      # build text selection for the NDJson annotations\n",
-        "      update_text_selections(annotation=entity_source_ndjson,\n",
-        "                             group_id=group[\"id\"], # id representing group of words\n",
-        "                             list_tokens=relationship_source, # ids representing individual words from the group\n",
-        "                             page=1)\n",
-        "    if group[\"content\"] == content_phrases[3]:\n",
-        "        relationship_target = [x[\"id\"] for x in group[\"tokens\"]]\n",
-        "        # build text selections for Python Annotation Types\n",
-        "        text_selection_entity_target =  lb_types.DocumentTextSelection(group_id=group[\"id\"], tokenIds=relationship_target, page=1)\n",
-        "        text_selections_target.append(text_selection_entity_target)\n",
-        "        # build text selections forthe NDJson annotations\n",
-        "        update_text_selections(annotation=entity_target_ndjson,\n",
-        "                               group_id=group[\"id\"], # id representing group of words\n",
-        "                               list_tokens=relationship_target, # ids representing individual words from the group\n",
-        "                               page=1)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Re-write the python annotations to include text selections (only required for python annotation types)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#re-write the entity annotation with text selections\n",
-        "entities_annotation_document_entity = lb_types.DocumentEntity(name=\"named_entity\", textSelections = text_selections)\n",
-        "entities_annotation = lb_types.ObjectAnnotation(name=\"named_entity\",value=entities_annotation_document_entity)\n",
-        "\n",
-        "# re-write the entity annotation + subclassification with text selections\n",
-        "classifications = [\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_checklist_question\",\n",
-        "      value=lb_types.Checklist(\n",
-        "      answer=[lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")]\n",
-        "      )\n",
-        "    )\n",
-        "  ]\n",
-        "ner_annotation_with_subclass = lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\", textSelections= text_selections_ner)\n",
-        "ner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(name=\"ner_with_checklist_subclass\",\n",
-        "                                                                   value=ner_annotation_with_subclass,\n",
-        "                                                                   classifications=classifications)\n",
-        "\n",
-        "#re-write the entity source and target annotations withe text selectios\n",
-        "entity_source_doc = lb_types.DocumentEntity(name=\"named_entity\", text_selections= text_selections_source)\n",
-        "entity_source = lb_types.ObjectAnnotation(name=\"named_entity\", value=entity_source_doc)\n",
-        "\n",
-        "entity_target_doc = lb_types.DocumentEntity(name=\"named_entity\",  text_selections=text_selections_target)\n",
-        "entity_target = lb_types.ObjectAnnotation(name=\"named_entity\", value=entity_target_doc)\n",
-        "\n",
-        "# re-write the entity relationship with the re-created entities\n",
-        "entity_relationship = lb_types.RelationshipAnnotation(\n",
-        "    name=\"relationship\",\n",
-        "    value=lb_types.Relationship(\n",
-        "        source=entity_source,\n",
-        "        target=entity_target,\n",
-        "        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n",
-        "    ))\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Final NDJSON and python annotations\n",
-        "print(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\n",
-        "print(f\"entities_annotation={entities_annotation}\")\n",
-        "print(f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_annotation_ndjson}\")\n",
-        "print(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")\n",
-        "print(f\"entity_source_ndjson={entity_source_ndjson}\")\n",
-        "print(f\"entity_target_ndjson={entity_target_ndjson}\")\n",
-        "print(f\"entity_source={entity_source}\")\n",
-        "print(f\"entity_target={entity_target}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python annotation\n",
-        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels = []\n",
-        "\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "        data={\"global_key\" : global_key },\n",
-        "        annotations = [\n",
-        "            entities_annotation,\n",
-        "            checklist_annotation,\n",
-        "            nested_checklist_annotation,\n",
-        "            text_annotation,\n",
-        "            radio_annotation,\n",
-        "            nested_radio_annotation,\n",
-        "            bbox_annotation,\n",
-        "            bbox_with_radio_subclass_annotation,\n",
-        "            ner_with_checklist_subclass_annotation,\n",
-        "            entity_source,\n",
-        "            entity_target,\n",
-        "            entity_relationship,# Only supported for MAL imports\n",
-        "            bbox_source,\n",
-        "            bbox_target,\n",
-        "            bbox_relationship  # Only supported for MAL imports\n",
-        "        ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### NDJson annotations\n",
-        "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "label_ndjson = []\n",
-        "for annot in [\n",
-        "    entities_annotations_ndjson,\n",
-        "    checklist_annotation_ndjson,\n",
-        "    nested_checklist_annotation_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    radio_annotation_ndjson,\n",
-        "    nested_radio_annotation_ndjson,\n",
-        "    bbox_annotation_ndjson,\n",
-        "    bbox_with_radio_subclass_annotation_ndjson,\n",
-        "    ner_with_checklist_subclass_annotation_ndjson,\n",
-        "    entity_source_ndjson,\n",
-        "    entity_target_ndjson,\n",
-        "    ner_relationship_annotation_ndjson, # Only supported for MAL imports\n",
-        "    bbox_source_ndjson,\n",
-        "    bbox_target_ndjson,\n",
-        "    bbox_relationship_annotation_ndjson # Only supported for MAL imports\n",
-        "  ]:\n",
-        "  annot.update({\n",
-        "      \"dataRow\": {\"globalKey\": global_key},\n",
-        "  })\n",
-        "  label_ndjson.append(annot)\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Import the annotation payload\n",
-        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Option A: Upload to a labeling project as pre-labels (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n",
-        "    predictions=labels)\n",
-        "\n",
-        "upload_job.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Option B: Upload to a labeling project using ground truth"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Uncomment this code when excluding relationships from label import\n",
-        "## Relationships are not currently supported for label import\n",
-        "\n",
-        "# upload_job = lb.LabelImport.create_from_objects(\n",
-        "#     client = client,\n",
-        "#     project_id = project.uid,\n",
-        "#     name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "#     labels=labels) ## Remove unsupported relationships from the labels list\n",
-        "\n",
-        "# print(\"Errors:\", upload_job.errors)\n",
-        "# print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 1,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# PDF Annotation Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "\n",
+                "Supported annotations for PDF assets \n",
+                "\n",
+                "*Annotation types*\n",
+                "- Checklist classification (including nested classifications)\n",
+                "- Radio classifications (including nested classifications)\n",
+                "- Free text classifications\n",
+                "- Bounding box\n",
+                "- Entities\n",
+                "- Relationships (only supported for MAL imports)\n",
+                "\n",
+                "\n",
+                "*NDJson*\n",
+                "- Checklist classification (including nested classifications)\n",
+                "- Radio classifications (including nested classifications)\n",
+                "- Free text classifications\n",
+                "- Bounding box \n",
+                "- Entities \n",
+                "- Relationships (only supported for MAL imports)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Replace with your API key\n",
+                "Guides on https://docs.labelbox.com/docs/create-an-api-key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Supported Annotations"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########## Entity ##########\n\n# Annotation Types\nentities_annotations = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_annotations_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########### Radio Classification #########\n\n# Annotation types\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############ Bounding Box ###########\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=102.771, y=135.3),  # x = left, y = top\n        end=lb_types.Point(x=518.571,\n                           y=245.143),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 135.3,\n        \"left\": 102.771,\n        \"height\": 109.843,\n        \"width\": 415.8\n    },\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# ############ global nested classifications ###########\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############## Classification Free-form text ##############\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\"name\": \"free_text\", \"answer\": \"sample text\"}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######### BBOX with nested classifications #########\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=317.271, y=226.757),  # x = left, y = top\n        end=lb_types.Point(x=566.657,\n                           y=420.986),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_annotation_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\"\n                },\n            }],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 226.757,\n        \"left\": 317.271,\n        \"height\": 194.229,\n        \"width\": 249.386,\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\"\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######### Relationships ##########\nentity_source = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_target = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nentity_source_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_source,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\n\nentity_target_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_target,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\nner_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######### BBOX with relationships #############\n# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=188.257, y=68.875),  # x = left, y = top\n        end=lb_types.Point(x=270.907,\n                           y=149.556),  # x = left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=96.424, y=66.251),\n        end=lb_types.Point(x=179.074, y=146.932),\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source_2 = str(uuid.uuid4())\nuuid_target_2 = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_source_2,\n    \"bbox\": {\n        \"top\": 68.875,\n        \"left\": 188.257,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_target_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_target_2,\n    \"bbox\": {\n        \"top\": 66.251,\n        \"left\": 96.424,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source_2,\n        \"target\": uuid_target_2,\n        \"type\": \"unidirectional\",\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annotations - putting it all together "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 1: Import data rows into Catalog "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
+                "\n",
+                "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
+                "- The document must have no more than 15 pages.\n",
+                "- The file size should not exceed 20 MB.\n",
+                "\n",
+                "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
+                "\n",
+                "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
+                "\n",
+                "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"0801.3483_doc.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 2: Create/select an Ontology for your project\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 3: Creating a labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"PDF_annotation_demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"PDF_annotation_batch\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 5. Create the annotation payload\n",
+                "Create the annotations payload using the snippets of code in Supported predictions section.\n",
+                "\n",
+                "Labelbox support NDJSON only for this data type.\n",
+                "\n",
+                "The resulting label should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Step 5.1: First, we need to populate the text selections for Entity annotations\n",
+                "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "To extract the generated text layer url we first need to export the data row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n    \"Organic charge transfer salts based on the donor\",\n    \"the experimental investigations on this issue have not\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\ntext_selections_source = []\ntext_selections_target = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_annotations_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_annotation_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[2]:\n            relationship_source = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_source = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=relationship_source, page=1)\n            text_selections_source.append(text_selection_entity_source)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entity_source_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_source,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[3]:\n            relationship_target = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_target = lb_types.DocumentTextSelection(\n                group_id=group[\"id\"], tokenIds=relationship_target, page=1)\n            text_selections_target.append(text_selection_entity_target)\n            # build text selections forthe NDJson annotations\n            update_text_selections(\n                annotation=entity_target_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_target,  # ids representing individual words from the group\n                page=1,\n            )",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Re-write the python annotations to include text selections (only required for python annotation types)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# re-write the entity annotation with text selections\nentities_annotation_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", textSelections=text_selections)\nentities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_annotation_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\", textSelections=text_selections_ner)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# re-write the entity source and target annotations withe text selectios\nentity_source_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_source)\nentity_source = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_source_doc)\n\nentity_target_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_target)\nentity_target = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_target_doc)\n\n# re-write the entity relationship with the re-created entities\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\nprint(f\"entities_annotation={entities_annotation}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_annotation_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")\nprint(f\"entity_source_ndjson={entity_source_ndjson}\")\nprint(f\"entity_target_ndjson={entity_target_ndjson}\")\nprint(f\"entity_source={entity_source}\")\nprint(f\"entity_target={entity_target}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python annotation\n",
+                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n            entity_source,\n            entity_target,\n            entity_relationship,  # Only supported for MAL imports\n            bbox_source,\n            bbox_target,\n            bbox_relationship,  # Only supported for MAL imports\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### NDJson annotations\n",
+                "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annot in [\n        entities_annotations_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        bbox_annotation_ndjson,\n        bbox_with_radio_subclass_annotation_ndjson,\n        ner_with_checklist_subclass_annotation_ndjson,\n        entity_source_ndjson,\n        entity_target_ndjson,\n        ner_relationship_annotation_ndjson,  # Only supported for MAL imports\n        bbox_source_ndjson,\n        bbox_target_ndjson,\n        bbox_relationship_annotation_ndjson,  # Only supported for MAL imports\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(annot)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Import the annotation payload\n",
+                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Option A: Upload to a labeling project as pre-labels (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Option B: Upload to a labeling project using ground truth"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Uncomment this code when excluding relationships from label import\n## Relationships are not currently supported for label import\n\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=labels) ## Remove unsupported relationships from the labels list\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/text.ipynb b/examples/annotation_import/text.ipynb
index 749320caf..74691ad78 100644
--- a/examples/annotation_import/text.ipynb
+++ b/examples/annotation_import/text.ipynb
@@ -1,616 +1,318 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/static/images/logo-v4.svg\" width=190/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Text Annotation Import\n",
-        "* This notebook will provide examples of each supported annotation type for text assets, and also cover MAL and Label Import methods.\n",
-        "\n",
-        "Supported annotations that can be uploaded through the SDK: \n",
-        "\n",
-        "* Entity\n",
-        "* Classification radio \n",
-        "* Classification checklist \n",
-        "* Classification free-form text \n",
-        "\n",
-        "\n",
-        "**Not** supported:\n",
-        "* Relationships\n",
-        "* Segmentation mask\n",
-        "* Polygon\n",
-        "* Bounding box \n",
-        "* Polyline\n",
-        "* Point \n",
-        "\n",
-        "MAL and Label Import: \n",
-        "\n",
-        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-        "\n",
-        "For information on what types of annotations are supported per data type, refer to the Import text annotations [documentation](https://docs.labelbox.com/reference/import-text-annotations)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Notes:\n",
-        "  * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
-        "  * You may need to refresh your browser in order to see the results of the import job."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Setup\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid\n",
-        "import json"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Replace with your API key\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY=\"\"\n",
-        "client = lb.Client(API_KEY)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Supported Python annotation types and NDJSON"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Entities ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "named_entity = lb_types.TextEntity(start=10, end=20)\n",
-        "named_entitity_annotation = lb_types.ObjectAnnotation(value=named_entity, name = \"named_entity\")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "entities_ndjson = {\n",
-        "    \"name\": \"named_entity\",\n",
-        "    \"location\": {\n",
-        "        \"start\": 67,\n",
-        "        \"end\": 128\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Radio (single choice ) ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer =\n",
-        "        lb_types.ClassificationAnswer(name = \"first_radio_answer\")\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_annotation_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\"}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Radio and Checklist (with subclassifications)  ##########\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "# NDJSON\n",
-        "nested_radio_annotation_ndjson= {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Checklist (Multi-choice) ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"third_checklist_answer\")\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_annotation_ndjson = {\n",
-        "  \"name\": \"checklist_question\",\n",
-        "  \"answer\": [\n",
-        "    {\"name\": \"first_checklist_answer\"},\n",
-        "    {\"name\": \"second_checklist_answer\"},\n",
-        "    {\"name\": \"third_checklist_answer\"},\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification Free-Form text  ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name = \"free_text\",\n",
-        "    value = lb_types.Text(answer=\"sample text\")\n",
-        ")\n",
-        "\n",
-        "#  NDJSON\n",
-        "text_annotation_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annoations - putting it all together "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# You can now include ohter fields like attachments, media type and metadata in the data row creation step: https://docs.labelbox.com/reference/text-file\n",
-        "global_key = \"lorem-ipsum.txt\"\n",
-        "text_asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n",
-        "    \"global_key\": global_key,\n",
-        "    \"media_type\": \"TEXT\",\n",
-        "    \"attachments\": [{\"type\": \"TEXT_URL\", \"value\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\"}]\n",
-        "    }\n",
-        "\n",
-        "dataset = client.create_dataset(\n",
-        "    name=\"text_annotation_import_demo_dataset\",\n",
-        "    iam_integration=None # Removing this argument will default to the organziation's default iam integration\n",
-        ")\n",
-        "task = dataset.create_data_rows([text_asset])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 2:  Create/select an ontology\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool and classification `name` should match the `name` field in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-        "\n",
-        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup the ontology and link the tools created above.\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",\n",
-        "      options=[lb.Option(value=\"first_radio_answer\")]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"nested_radio_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\",\n",
-        "          options=[\n",
-        "              lb.Classification(\n",
-        "                class_type=lb.Classification.Type.RADIO,\n",
-        "                name=\"sub_radio_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                ]\n",
-        "            ),\n",
-        "          ]\n",
-        "        ),\n",
-        "      ],\n",
-        "    ),\n",
-        "     lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\"),\n",
-        "        lb.Option(value=\"third_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "     lb.Classification( # Text classification given the name \"text\"\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\"\n",
-        "    )\n",
-        "  ],\n",
-        "  tools=[ # List of Tool objects\n",
-        "         lb.Tool(\n",
-        "            tool=lb.Tool.Type.NER,\n",
-        "            name=\"named_entity\"\n",
-        "          ),\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Text Annotations\", ontology_builder.asdict())\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 3: Create a labeling project \n",
-        "Connect the ontology to the labeling project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
-        "# Queue mode will be deprecated once dataset mode is deprecated\n",
-        "\n",
-        "project = client.create_project(name=\"Text Annotation Import Demo\",\n",
-        "                                    media_type=lb.MediaType.Text)\n",
-        "\n",
-        "\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 4: Send a batch of data rows to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup Batches and Ontology\n",
-        "\n",
-        "# Create a batch to send to your MAL project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-text-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 5: Create the annotations payload\n",
-        "\n",
-        "Create the annotations payload using the snippets of code above\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python annotations"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label\n",
-        "labels = []\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "        data={\"global_key\" : global_key },\n",
-        "        annotations = [\n",
-        "            named_entitity_annotation,\n",
-        "            radio_annotation,\n",
-        "            checklist_annotation,\n",
-        "            text_annotation,\n",
-        "            nested_checklist_annotation,\n",
-        "            nested_radio_annotation\n",
-        "        ]\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### NDJSON annotations"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [entities_ndjson,\n",
-        "                   radio_annotation_ndjson,\n",
-        "                   checklist_annotation_ndjson,\n",
-        "                   text_annotation_ndjson,\n",
-        "                   nested_radio_annotation_ndjson,\n",
-        "                   nested_checklist_annotation_ndjson,\n",
-        "                    ] :\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": { \"globalKey\": global_key }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or ground truth\n",
-        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python Annotation types). \n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model-Assisted Labeling (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload MAL label for this data row in project\n",
-        "upload_job_mal = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"mal_import_job\"+str(uuid.uuid4()),\n",
-        "    predictions=labels)\n",
-        "\n",
-        "upload_job_mal.wait_until_done()\n",
-        "print(\"Errors:\", upload_job_mal.errors)\n",
-        "print(\"Status of uploads: \", upload_job_mal.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload label for this data row in project\n",
-        "upload_job_label_import = lb.LabelImport.create_from_objects(\n",
-        "     client = client,\n",
-        "     project_id = project.uid,\n",
-        "     name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "     labels=labels)\n",
-        "\n",
-        "upload_job_label_import.wait_until_done()\n",
-        "print(\"Errors:\", upload_job_label_import.errors)\n",
-        "print(\"Status of uploads: \", upload_job_label_import.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Text Annotation Import\n",
+                "* This notebook will provide examples of each supported annotation type for text assets, and also cover MAL and Label Import methods.\n",
+                "\n",
+                "Supported annotations that can be uploaded through the SDK: \n",
+                "\n",
+                "* Entity\n",
+                "* Classification radio \n",
+                "* Classification checklist \n",
+                "* Classification free-form text \n",
+                "\n",
+                "\n",
+                "**Not** supported:\n",
+                "* Relationships\n",
+                "* Segmentation mask\n",
+                "* Polygon\n",
+                "* Bounding box \n",
+                "* Polyline\n",
+                "* Point \n",
+                "\n",
+                "MAL and Label Import: \n",
+                "\n",
+                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+                "\n",
+                "For information on what types of annotations are supported per data type, refer to the Import text annotations [documentation](https://docs.labelbox.com/reference/import-text-annotations)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Notes:\n",
+                "  * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+                "  * You may need to refresh your browser in order to see the results of the import job."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Setup\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Replace with your API key\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Supported Python annotation types and NDJSON"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nnamed_entitity_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                      name=\"named_entity\")\n\n# NDJSON\nentities_ndjson = {\n    \"name\": \"named_entity\",\n    \"location\": {\n        \"start\": 67,\n        \"end\": 128\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Checklist (Multi-choice) ##########\n\n# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\n#  NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annoations - putting it all together "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# You can now include ohter fields like attachments, media type and metadata in the data row creation step: https://docs.labelbox.com/reference/text-file\nglobal_key = \"lorem-ipsum.txt\"\ntext_asset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"TEXT\",\n    \"attachments\": [{\n        \"type\":\n            \"TEXT_URL\",\n        \"value\":\n            \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n    }],\n}\n\ndataset = client.create_dataset(\n    name=\"text_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([text_asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 2:  Create/select an ontology\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool and classification `name` should match the `name` field in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+                "\n",
+                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(  # Text classification given the name \"text\"\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\"),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Text Annotations\",\n                                  ontology_builder.asdict())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 3: Create a labeling project \n",
+                "Connect the ontology to the labeling project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Text Annotation Import Demo\",\n                                media_type=lb.MediaType.Text)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 4: Send a batch of data rows to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-text-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 5: Create the annotations payload\n",
+                "\n",
+                "Create the annotations payload using the snippets of code above\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python annotations"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label\nlabels = []\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            named_entitity_annotation,\n            radio_annotation,\n            checklist_annotation,\n            text_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### NDJSON annotations"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        entities_ndjson,\n        radio_annotation_ndjson,\n        checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or ground truth\n",
+                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python Annotation types). \n",
+                "\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model-Assisted Labeling (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload label for this data row in project\nupload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 69b3f5982..6a4f6fd09 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -1,1103 +1,347 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Tiled Imagery Annotation Import\n",
-        "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
-        "\n",
-        "Supported annotations that can be uploaded through the SDK: \n",
-        "  * Point \n",
-        "  * Polygon\n",
-        "  * Bounding Box \n",
-        "  * Classification radio \n",
-        "  * Classification checklist \n",
-        "  * Classification free-form text\n",
-        "\n",
-        "**Not** supported:\n",
-        "  * Segmentation mask\n",
-        "\n",
-        "\n",
-        "MAL and Label Import: \n",
-        "\n",
-        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-        "\n",
-        "For information on what types of annotations are supported per data type, refer to this documentation:\n",
-        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Notes:\n",
-        " * This notebook uses the Slippy Maps format\n",
-        " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
-        " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
-        " * You may need to refresh your browser in order to see the results of the import job."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q 'labelbox[data]'"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "import uuid\n",
-        "import numpy as np\n",
-        "import cv2\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Replace with your API key\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for tiled imagery"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Supported Python annotation types and NDJSON "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Point #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "point_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"point_geo\",\n",
-        "  value = lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "point_annotation_ndjson = {\n",
-        "    \"name\": \"point_geo\",\n",
-        "    \"point\": {\n",
-        "         \"x\": -99.20647859573366,\n",
-        "         \"y\": 19.40018029091072\n",
-        "     }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Polyline #######\n",
-        "# Coordinates\n",
-        "coords = [\n",
-        "            [\n",
-        "                -99.20842051506044,\n",
-        "                19.40032196622975\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20809864997865,\n",
-        "                19.39758963475322\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20758366584778,\n",
-        "                19.39776167179227\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20728325843811,\n",
-        "                19.3973265189299\n",
-        "            ]\n",
-        "        ]\n",
-        "\n",
-        "line_points = []\n",
-        "line_points_ndjson = []\n",
-        "\n",
-        "for sub in coords:\n",
-        "  line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n",
-        "  line_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
-        "\n",
-        "# Python Annotation\n",
-        "polyline_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline_geo\",\n",
-        "  value = lb_types.Line(points=line_points),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "polyline_annotation_ndjson = {\n",
-        "    \"name\": \"polyline_geo\",\n",
-        "    \"line\": line_points_ndjson\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Polygon #######\n",
-        "# Coordinates in the desired EPSG coordinate system\n",
-        "coords_polygon = [\n",
-        "    [\n",
-        "        -99.21042680740356,\n",
-        "        19.40036244486966\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2104160785675,\n",
-        "        19.40017017124035\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2103409767151,\n",
-        "        19.400008256428897\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21014785766603,\n",
-        "        19.400008256428897\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21019077301027,\n",
-        "        19.39983622176518\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21022295951845,\n",
-        "        19.399674306621385\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21029806137086,\n",
-        "        19.39951239131646\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2102873325348,\n",
-        "        19.399340356128437\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21025514602663,\n",
-        "        19.399117722085677\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21024441719057,\n",
-        "        19.39892544698541\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2102336883545,\n",
-        "        19.39874329141769\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21021223068239,\n",
-        "        19.398561135646027\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21018004417421,\n",
-        "        19.398399219233365\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21011567115785,\n",
-        "        19.39822718286836\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20992255210878,\n",
-        "        19.398136104719125\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20974016189577,\n",
-        "        19.398085505725305\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20957922935487,\n",
-        "        19.398004547302467\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20939683914186,\n",
-        "        19.39792358883935\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20918226242067,\n",
-        "        19.39786286996558\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20899987220764,\n",
-        "        19.397822390703805\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20891404151918,\n",
-        "        19.397994427496787\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.398176583902874\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.398368859888045\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.398540896103246\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.39872305189756\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.39890520748796\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.39907724313608\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.399259398329956\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.399431433603585\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.39961358840092\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.399785623300048\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20897841453552,\n",
-        "        19.399937418648214\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20919299125673,\n",
-        "        19.399937418648214\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2093861103058,\n",
-        "        19.39991717927664\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20956850051881,\n",
-        "        19.39996777770086\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20961141586305,\n",
-        "        19.40013981222548\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20963287353517,\n",
-        "        19.40032196622975\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20978307724,\n",
-        "        19.4004130431554\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20996546745302,\n",
-        "        19.40039280384301\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21019077301027,\n",
-        "        19.400372564528084\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21042680740356,\n",
-        "        19.40036244486966\n",
-        "    ]\n",
-        "\n",
-        "]\n",
-        "\n",
-        "polygon_points = []\n",
-        "polygon_points_ndjson = []\n",
-        "\n",
-        "for sub in coords_polygon:\n",
-        "  polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n",
-        "  polygon_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
-        "\n",
-        "# Python Annotation\n",
-        "polygon_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon_geo\",\n",
-        "  value = lb_types.Polygon(points=polygon_points),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "polygon_annotation_ndjson = {\n",
-        "    \"name\": \"polygon_geo\",\n",
-        "    \"polygon\": polygon_points_ndjson\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Bounding Box #######\n",
-        "coord_object =  {\n",
-        "    \"coordinates\" : [[\n",
-        "            [\n",
-        "                -99.20746564865112,\n",
-        "                19.39799442829336\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20746564865112,\n",
-        "                19.39925939999194\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20568466186523,\n",
-        "                19.39925939999194\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20568466186523,\n",
-        "                19.39799442829336\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20746564865112,\n",
-        "                19.39799442829336\n",
-        "            ]\n",
-        "        ]]\n",
-        "}\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "bbox_top_left = lb_types.Point(x= -99.20746564865112, y=19.39799442829336)\n",
-        "bbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"bbox_geo\",\n",
-        "  value = lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right)\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_annotation_ndjson = {\n",
-        "    \"name\" : \"bbox_geo\",\n",
-        "    \"bbox\" : {\n",
-        "        'top': coord_object[\"coordinates\"][0][1][1],\n",
-        "        'left': coord_object[\"coordinates\"][0][1][0],\n",
-        "        'height': coord_object[\"coordinates\"][0][3][1] - coord_object[\"coordinates\"][0][1][1],\n",
-        "        'width': coord_object[\"coordinates\"][0][3][0] - coord_object[\"coordinates\"][0][1][0]\n",
-        "    }\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification - radio (single choice) #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question_geo\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_radio_answer\"))\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_annotation_ndjson = {\n",
-        "    \"name\": \"radio_question_geo\",\n",
-        "    \"answer\": { \"name\": \"first_radio_answer\"}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification - Checklist (multi-choice) #######\n",
-        "\n",
-        "coord_object_checklist = {\n",
-        "    \"coordinates\": [\n",
-        "       [\n",
-        "            [\n",
-        "                -99.210266,\n",
-        "                19.39540372195134\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.210266,\n",
-        "                19.396901\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20621067903966,\n",
-        "                19.396901\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20621067903966,\n",
-        "                19.39540372195134\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.210266,\n",
-        "                19.39540372195134\n",
-        "            ]\n",
-        "      ]\n",
-        "    ]\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_checklist_geo\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=-99.210266, y=19.39540372195134), # Top left\n",
-        "        end=lb_types.Point(x=-99.20621067903966, y=19.396901), # Bottom right\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"checklist_class_name\",\n",
-        "            value=lb_types.Checklist(\n",
-        "                answer=[lb_types.ClassificationAnswer(name=\"first_checklist_answer\")]\n",
-        "            )\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_with_checklist_subclass_ndjson = {\n",
-        "    \"name\": \"bbox_checklist_geo\",\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"checklist_class_name\",\n",
-        "        \"answer\": [\n",
-        "            { \"name\":\"first_checklist_answer\" }\n",
-        "        ]\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "        'top': coord_object_checklist[\"coordinates\"][0][1][1],\n",
-        "        'left': coord_object_checklist[\"coordinates\"][0][1][0],\n",
-        "        'height': coord_object_checklist[\"coordinates\"][0][3][1] - coord_object_checklist[\"coordinates\"][0][1][1],\n",
-        "        'width': coord_object_checklist[\"coordinates\"][0][3][0] - coord_object_checklist[\"coordinates\"][0][1][0]\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification free form text with bbox #######\n",
-        "\n",
-        "coord_object_text ={\n",
-        "    \"coordinates\": [\n",
-        "      [\n",
-        "        [\n",
-        "            -99.21019613742828,\n",
-        "            19.397447957052933\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.21019613742828,\n",
-        "            19.39772119262215\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.20986354351044,\n",
-        "            19.39772119262215\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.20986354351044,\n",
-        "            19.397447957052933\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.21019613742828,\n",
-        "            19.397447957052933\n",
-        "        ]\n",
-        "      ]\n",
-        "    ]\n",
-        "}\n",
-        "# Python Annotation\n",
-        "bbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_text_geo\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=-99.21019613742828, y=19.397447957052933), # Top left\n",
-        "        end=lb_types.Point(x=-99.20986354351044, y=19.39772119262215), # Bottom right\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"free_text_geo\",\n",
-        "            value=lb_types.Text(answer=\"sample text\")\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_with_free_text_subclass_ndjson = {\n",
-        "    \"name\":\"bbox_text_geo\",\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"free_text_geo\",\n",
-        "        \"answer\": \"sample text\"\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "        'top': coord_object_text[\"coordinates\"][0][1][1],\n",
-        "        'left': coord_object_text[\"coordinates\"][0][1][0],\n",
-        "        'height': coord_object_text[\"coordinates\"][0][3][1] - coord_object_text[\"coordinates\"][0][1][1],\n",
-        "        'width': coord_object_text[\"coordinates\"][0][3][0] - coord_object_text[\"coordinates\"][0][1][0]\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification - Checklist (multi-choice) #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question_geo\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"third_checklist_answer\")\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_annotation_ndjson = {\n",
-        "  'name': 'checklist_question_geo',\n",
-        "  'answer': [\n",
-        "    {'name': 'first_checklist_answer'},\n",
-        "    {'name': 'second_checklist_answer'},\n",
-        "    {'name': 'third_checklist_answer'},\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Radio and Checklist (with subclassifications)  ##########\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "# NDJSON\n",
-        "nested_radio_annotation_ndjson= {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\"}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Upload Annotations - putting it all together\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\n",
-        "bottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n",
-        "\n",
-        "epsg = lb_types.EPSG.EPSG4326\n",
-        "bounds = lb_types.TiledBounds(epsg=epsg, bounds=[top_left_bound, bottom_right_bound])\n",
-        "global_key = \"mexico_city\"\n",
-        "\n",
-        "tile_layer = lb_types.TileLayer(\n",
-        "    url=\"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n",
-        ")\n",
-        "\n",
-        "tiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n",
-        "                                  tile_bounds=bounds,\n",
-        "                                  zoom_levels=[17, 23])\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": tiled_image_data.asdict(),\n",
-        "    \"global_key\": global_key,\n",
-        "    \"media_type\": \"TMS_GEO\"\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"geo_demo_dataset\")\n",
-        "task= dataset.create_data_rows([asset])\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 2: Create/select an ontology\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n",
-        "        lb.Tool(\n",
-        "          tool=lb.Tool.Type.BBOX,\n",
-        "          name=\"bbox_checklist_geo\",\n",
-        "          classifications=[\n",
-        "                lb.Classification(\n",
-        "                    class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                    name=\"checklist_class_name\",\n",
-        "                    options=[\n",
-        "                      lb.Option(value=\"first_checklist_answer\")\n",
-        "                    ]\n",
-        "                ),\n",
-        "            ]\n",
-        "          ),\n",
-        "        lb.Tool(\n",
-        "          tool=lb.Tool.Type.BBOX,\n",
-        "          name=\"bbox_text_geo\",\n",
-        "          classifications=[\n",
-        "                lb.Classification(\n",
-        "                    class_type=lb.Classification.Type.TEXT,\n",
-        "                    name=\"free_text_geo\"\n",
-        "                ),\n",
-        "            ]\n",
-        "          )\n",
-        "      ],\n",
-        "      classifications = [\n",
-        "          lb.Classification(\n",
-        "              class_type=lb.Classification.Type.CHECKLIST,\n",
-        "              name=\"checklist_question_geo\",\n",
-        "              options=[\n",
-        "                  lb.Option(value=\"first_checklist_answer\"),\n",
-        "                  lb.Option(value=\"second_checklist_answer\"),\n",
-        "                  lb.Option(value=\"third_checklist_answer\")\n",
-        "              ]\n",
-        "          ),\n",
-        "          lb.Classification(\n",
-        "              class_type=lb.Classification.Type.RADIO,\n",
-        "              name=\"radio_question_geo\",\n",
-        "              options=[\n",
-        "                  lb.Option(value=\"first_radio_answer\")\n",
-        "              ]\n",
-        "          ),\n",
-        "\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.RADIO,\n",
-        "          name=\"nested_radio_question\",\n",
-        "          options=[\n",
-        "            lb.Option(value=\"first_radio_answer\",\n",
-        "              options=[\n",
-        "                  lb.Classification(\n",
-        "                    class_type=lb.Classification.Type.RADIO,\n",
-        "                    name=\"sub_radio_question\",\n",
-        "                    options=[\n",
-        "                      lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                    ]\n",
-        "                ),\n",
-        "              ]\n",
-        "            ),\n",
-        "          ],\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.CHECKLIST,\n",
-        "          name=\"nested_checklist_question\",\n",
-        "          options=[\n",
-        "              lb.Option(\"first_checklist_answer\",\n",
-        "                options=[\n",
-        "                  lb.Classification(\n",
-        "                      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                      name=\"sub_checklist_question\",\n",
-        "                      options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "                  )\n",
-        "              ]\n",
-        "            )\n",
-        "          ]\n",
-        "      )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Geospatial Annotations\", ontology_builder.asdict(), media_type=lb.MediaType.Geospatial_Tile)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 3: Create a labeling project\n",
-        "Connect the ontology to the labeling project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
-        "# Queue mode will be deprecated once dataset mode is deprecated\n",
-        "\n",
-        "project = client.create_project(name=\"Geospatial Project Demo\",\n",
-        "                                    media_type=lb.MediaType.Geospatial_Tile)\n",
-        "\n",
-        "\n",
-        "project.setup_editor(ontology)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 4: Send a batch of data rows to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup Batches and Ontology\n",
-        "\n",
-        "# Create a batch to send to your MAL project\n",
-        "batch = project.create_batch(\n",
-        "  \"first-batch-geo-demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 5: Create the annotations payload \n",
-        "Create the annotations payload using the snippets of code above\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python annotations\n",
-        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        " ## Lets create another polygon annotation with python annotation tools that draws the image using cv2 python libraries\n",
-        "\n",
-        "hsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\n",
-        "mask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\n",
-        "kernel = np.ones((15, 20), np.uint8)\n",
-        "mask = cv2.erode(mask, kernel)\n",
-        "mask = cv2.dilate(mask, kernel)\n",
-        "mask_annotation = lb_types.MaskData.from_2D_arr(mask)\n",
-        "mask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\n",
-        "h, w, _ = tiled_image_data.value.shape\n",
-        "pixel_bounds = lb_types.TiledBounds(epsg=lb_types.EPSG.SIMPLEPIXEL,\n",
-        "                          bounds=[lb_types.Point(x=0, y=0),\n",
-        "                                  lb_types.Point(x=w, y=h)])\n",
-        "transformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n",
-        "    src_epsg=pixel_bounds.epsg,\n",
-        "    pixel_bounds=pixel_bounds,\n",
-        "    geo_bounds=tiled_image_data.tile_bounds,\n",
-        "    zoom=20)\n",
-        "pixel_polygons = mask_data.shapely.simplify(3)\n",
-        "list_of_polygons = [transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms]\n",
-        "polygon_annotation_two = lb_types.ObjectAnnotation(value=list_of_polygons[0], name=\"polygon_geo_2\")\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels =[]\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "        data={\n",
-        "            \"global_key\": global_key,\n",
-        "            \"tile_layer\": tile_layer,\n",
-        "            \"tile_bounds\":bounds,\n",
-        "            \"zoom_levels\": [12, 20]\n",
-        "        },\n",
-        "        annotations = [\n",
-        "            point_annotation,\n",
-        "            polyline_annotation,\n",
-        "            polygon_annotation,\n",
-        "            bbox_annotation,\n",
-        "            radio_annotation,\n",
-        "            bbox_with_checklist_subclass,\n",
-        "            bbox_with_free_text_subclass,\n",
-        "            checklist_annotation,\n",
-        "            polygon_annotation_two,\n",
-        "            nested_checklist_annotation,\n",
-        "            nested_radio_annotation\n",
-        "        ]\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### NDJSON annotations\n",
-        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "\n",
-        "for annotations in [point_annotation_ndjson,\n",
-        "                    polyline_annotation_ndjson,\n",
-        "                    polygon_annotation_ndjson,\n",
-        "                    bbox_annotation_ndjson,\n",
-        "                    radio_annotation_ndjson,\n",
-        "                    bbox_with_checklist_subclass_ndjson,\n",
-        "                    bbox_with_free_text_subclass_ndjson,\n",
-        "                    checklist_annotation_ndjson,\n",
-        "                    nested_checklist_annotation_ndjson,\n",
-        "                    nested_radio_annotation_ndjson\n",
-        "                    ]:\n",
-        "  annotations.update({\n",
-        "      'dataRow': {\n",
-        "          'globalKey': global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model-Assisted Labeling (MAL)\n",
-        "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload MAL label for this data row in project\n",
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"mal_import_job\"+str(uuid.uuid4()),\n",
-        "    predictions=labels)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload label for this data row in project\n",
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"label_geo_import_job\"+str(uuid.uuid4()),\n",
-        "    labels=labels)\n",
-        "\n",
-        "upload_job.wait_until_done();\n",
-        "print(\"Errors:\", upload_job.errors)\n",
-        "print(\"Status of uploads: \", upload_job.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Tiled Imagery Annotation Import\n",
+                "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
+                "\n",
+                "Supported annotations that can be uploaded through the SDK: \n",
+                "  * Point \n",
+                "  * Polygon\n",
+                "  * Bounding Box \n",
+                "  * Classification radio \n",
+                "  * Classification checklist \n",
+                "  * Classification free-form text\n",
+                "\n",
+                "**Not** supported:\n",
+                "  * Segmentation mask\n",
+                "\n",
+                "\n",
+                "MAL and Label Import: \n",
+                "\n",
+                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+                "\n",
+                "For information on what types of annotations are supported per data type, refer to this documentation:\n",
+                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Notes:\n",
+                " * This notebook uses the Slippy Maps format\n",
+                " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
+                " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+                " * You may need to refresh your browser in order to see the results of the import job."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q 'labelbox[data]'",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nimport numpy as np\nimport cv2\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Replace with your API key\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for tiled imagery"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Supported Python annotation types and NDJSON "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point_geo\",\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"line\": line_points_ndjson,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"polygon\": polygon_points_ndjson,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\"\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Upload Annotations - putting it all together\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 2: Create/select an ontology\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 3: Create a labeling project\n",
+                "Connect the ontology to the labeling project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Geospatial Project Demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 4: Send a batch of data rows to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-geo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 5: Create the annotations payload \n",
+                "Create the annotations payload using the snippets of code above\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python annotations\n",
+                "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=20,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_annotation_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            polygon_annotation_two,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### NDJSON annotations\n",
+                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\n\nfor annotations in [\n        point_annotation_ndjson,\n        polyline_annotation_ndjson,\n        polygon_annotation_ndjson,\n        bbox_annotation_ndjson,\n        radio_annotation_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model-Assisted Labeling (MAL)\n",
+                "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload MAL label for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_geo_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/video.ipynb b/examples/annotation_import/video.ipynb
index b3eda6b9b..266197bdf 100644
--- a/examples/annotation_import/video.ipynb
+++ b/examples/annotation_import/video.ipynb
@@ -1,1282 +1,409 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td> "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Video Annotation Import\n",
-        "\n",
-        "* Annotations must be created and uploaded using NDJSON\n",
-        "* Supported annotations that can be uploaded through the SDK:\n",
-        "    * Bounding box\n",
-        "    * Point\n",
-        "    * Polyline \n",
-        "    * Radio classifications \n",
-        "    * Checklist classifications \n",
-        "    *  Segmentation masks\n",
-        "* **NOT** supported:\n",
-        "    * Polygons \n",
-        "\n",
-        "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import uuid\n",
-        "from PIL import Image\n",
-        "import requests\n",
-        "import base64\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "from io import BytesIO\n",
-        "import pprint\n",
-        "pp = pprint.PrettyPrinter(indent=4)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Replace with your API key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY=\"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported annotations for video\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding box: (frame-based)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class\n",
-        "\n",
-        "# bbox dimensions\n",
-        "bbox_dm = {\n",
-        "  \"top\":617,\n",
-        "  \"left\":1371,\n",
-        "  \"height\":419,\n",
-        "  \"width\":505\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_annotation = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=13,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=19,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]),\n",
-        "      )\n",
-        "  )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_annotation_ndjson = {\n",
-        "    \"name\" : \"bbox_video\",\n",
-        "    \"segments\" : [{\n",
-        "        \"keyframes\" : [\n",
-        "            {\n",
-        "              \"frame\": 13,\n",
-        "              \"bbox\" : bbox_dm\n",
-        "           },\n",
-        "           {\n",
-        "              \"frame\": 19,\n",
-        "              \"bbox\" : bbox_dm\n",
-        "           }\n",
-        "        ]\n",
-        "      }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Point (frame-based)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "point_annotation = [\n",
-        "    lb_types.VideoObjectAnnotation(\n",
-        "        name = \"point_video\",\n",
-        "        keyframe=True,\n",
-        "        frame=17,\n",
-        "        value = lb_types.Point(x=660.134, y=407.926),\n",
-        "        )\n",
-        "]\n",
-        "\n",
-        "# NDJSON\n",
-        "point_annotation_ndjson = {\n",
-        "    \"name\": \"point_video\",\n",
-        "    \"segments\": [{\n",
-        "        \"keyframes\": [{\n",
-        "            \"frame\": 17,\n",
-        "            \"point\" : {\n",
-        "                \"x\": 660.134 ,\n",
-        "                \"y\": 407.926\n",
-        "            }\n",
-        "        }]\n",
-        "    }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polyline (frame-based)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Polyline ########\n",
-        "\n",
-        "# Python Annotation\n",
-        "polyline_annotation = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=5,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=20,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=24,\n",
-        "    segment_index=1,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=45,\n",
-        "    segment_index=1,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  )\n",
-        "\n",
-        "]\n",
-        "\n",
-        "# NDJSON\n",
-        "polyline_frame_annotation_ndjson = {\n",
-        "  \"name\": \"line_video_frame\",\n",
-        "  \"segments\": [\n",
-        "      {\n",
-        "        \"keyframes\": [\n",
-        "          {\n",
-        "            \"frame\": 5,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 680,\n",
-        "              \"y\": 100\n",
-        "            },{\n",
-        "              \"x\": 100,\n",
-        "              \"y\": 190\n",
-        "            },{\n",
-        "              \"x\": 190,\n",
-        "              \"y\": 220\n",
-        "            }]\n",
-        "          },\n",
-        "          {\n",
-        "            \"frame\": 20,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 680,\n",
-        "              \"y\": 180\n",
-        "            },{\n",
-        "              \"x\": 100,\n",
-        "              \"y\": 200\n",
-        "            },{\n",
-        "              \"x\": 200,\n",
-        "              \"y\": 260\n",
-        "            }]\n",
-        "          }\n",
-        "        ]\n",
-        "      },\n",
-        "      {\n",
-        "        \"keyframes\": [\n",
-        "          {\n",
-        "            \"frame\": 24,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 300,\n",
-        "              \"y\": 310\n",
-        "            },{\n",
-        "              \"x\": 330,\n",
-        "              \"y\": 430\n",
-        "            }]\n",
-        "          },\n",
-        "          {\n",
-        "            \"frame\": 45,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 600,\n",
-        "              \"y\": 810\n",
-        "            },{\n",
-        "              \"x\": 900,\n",
-        "              \"y\": 930\n",
-        "            }]\n",
-        "          }\n",
-        "        ]\n",
-        "      }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Radio and checklist (frame-based)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "radio_annotation = [\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"radio_class\",\n",
-        "        frame=9,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        "    ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"radio_class\",\n",
-        "        frame=15,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "## NDJSON\n",
-        "frame_radio_classification_ndjson = {\n",
-        "    \"name\": \"radio_class\",\n",
-        "    \"answer\": { \"name\": \"first_radio_answer\", \"frames\": [{\"start\": 9, \"end\": 15}]}\n",
-        "}\n",
-        "\n",
-        "#Python annotation\n",
-        "checklist_annotation= [\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=29,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"first_checklist_answer\"\n",
-        "                ),\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"second_checklist_answer\"\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=35,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"first_checklist_answer\"\n",
-        "                ),\n",
-        "                 lb_types.ClassificationAnswer(\n",
-        "                    name = \"second_checklist_answer\"\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "## NDJSON\n",
-        "frame_checklist_classification_ndjson = {\n",
-        "    \"name\": \"checklist_class\",\n",
-        "    \"answer\": [\n",
-        "        { \"name\": \"first_checklist_answer\" , \"frames\": [{\"start\": 29, \"end\": 35 }]},\n",
-        "        { \"name\": \"second_checklist_answer\" , \"frames\": [{\"start\": 29, \"end\": 35 }]}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Checklist and radio (global)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Global Classifications #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "## For global classifications use ClassificationAnnotation\n",
-        "global_radio_annotation = [lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_class_global\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        ")]\n",
-        "\n",
-        "# NDJSON\n",
-        "global_radio_classification_ndjson = {\n",
-        "    \"name\": \"radio_class_global\",\n",
-        "    \"answer\": { \"name\": \"first_radio_answer\"}\n",
-        "}\n",
-        "\n",
-        "# Python annotation\n",
-        "global_checklist_annotation=[lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_class_global\",\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        " )]\n",
-        "\n",
-        "# NDJSON\n",
-        "global_checklist_classification_ndjson = {\n",
-        "    \"name\": \"checklist_class_global\",\n",
-        "    \"answer\": [\n",
-        "        { \"name\": \"first_checklist_answer\" },\n",
-        "        { \"name\": \"second_checklist_answer\"}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Nested radio and checklist (global)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Nested Global Classification ###########\n",
-        "\n",
-        "# Python Annotation\n",
-        "nested_radio_annotation =[lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")]\n",
-        "\n",
-        "# NDJSON\n",
-        "nested_radio_annotation_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\",\n",
-        "  \"classifications\" : [\n",
-        "    {\"name\": \"sub_radio_question\", \"answer\": {\"name\": \"first_sub_radio_answer\"}}\n",
-        "   ]\n",
-        "  }\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "nested_checklist_annotation = [lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")]\n",
-        "\n",
-        "# NDJSON\n",
-        "nested_checklist_annotation_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######### Free text classification ###########\n",
-        "text_annotation = [lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",  # must match your ontology feature\"s name\n",
-        "  value=lb_types.Text(answer=\"sample text\")\n",
-        ")]\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding box with sub-classifications (frame-based)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n",
-        "\n",
-        "# bounding box dimensions\n",
-        "bbox_dm2 = {\n",
-        "  \"top\": 146.0,\n",
-        "  \"left\": 98.0,\n",
-        "  \"height\": 382.0,\n",
-        "  \"width\": 341.0\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "frame_bbox_with_checklist_subclass_annotation = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=10,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=11,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]),\n",
-        "      ),\n",
-        "    classifications=[\n",
-        "                lb_types.ClassificationAnnotation(\n",
-        "                    name=\"checklist_class\",\n",
-        "                    value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(\n",
-        "                        name=\"first_checklist_answer\")])\n",
-        "                )\n",
-        "            ]\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=13,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]),\n",
-        "      ),\n",
-        "    classifications=[\n",
-        "              lb_types.ClassificationAnnotation(\n",
-        "                  name=\"checklist_class\",\n",
-        "                  value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(\n",
-        "                      name=\"second_checklist_answer\")])\n",
-        "              )\n",
-        "          ]\n",
-        "  )\n",
-        "]\n",
-        "\n",
-        "frame_bbox_with_checklist_subclass_annotation_ndjson = {\n",
-        "    \"name\": \"bbox_class\",\n",
-        "    \"segments\": [{\n",
-        "        \"keyframes\": [\n",
-        "            {\n",
-        "            \"frame\": 10,\n",
-        "            \"bbox\": bbox_dm2\n",
-        "          },\n",
-        "          {\n",
-        "          \"frame\": 11,\n",
-        "            \"bbox\": bbox_dm2,\n",
-        "            \"classifications\": [\n",
-        "              {\n",
-        "                \"name\": \"checklist_class\",\n",
-        "                \"answer\": [{\"name\": \"first_checklist_answer\"}]\n",
-        "              }\n",
-        "            ]\n",
-        "          },\n",
-        "          {\n",
-        "          \"frame\": 13,\n",
-        "            \"bbox\": bbox_dm2,\n",
-        "            \"classifications\": [\n",
-        "              {\n",
-        "                \"name\": \"checklist_class\",\n",
-        "                \"answer\": [{\"name\": \"second_checklist_answer\"}]\n",
-        "              }\n",
-        "            ]\n",
-        "          }\n",
-        "        ]\n",
-        "      }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Masks (frame-based)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "def extract_rgb_colors_from_url(image_url):\n",
-        "    response = requests.get(image_url)\n",
-        "    img = Image.open(BytesIO(response.content))\n",
-        "\n",
-        "    colors = set()\n",
-        "    for x in range(img.width):\n",
-        "        for y in range(img.height):\n",
-        "            pixel = img.getpixel((x, y))\n",
-        "            if pixel[:3] != (0,0,0):\n",
-        "                colors.add(pixel[:3])  # Get only the RGB values\n",
-        "\n",
-        "    return colors"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Raster Segmentation (Byte string array)\n",
-        "##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation\n",
-        "\n",
-        "\n",
-        "# Single mask\n",
-        "url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\n",
-        "response = requests.get(url)\n",
-        "img_bytes = base64.b64encode(response.content).decode('utf-8')\n",
-        "\n",
-        "# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain\n",
-        "# all frames and instances\n",
-        "frames_mask_single=[\n",
-        "    lb_types.MaskFrame(\n",
-        "        index=20,\n",
-        "        im_bytes=response.content # Instead of bytes you could also pass an instance URI : instance_uri=url\n",
-        "    )\n",
-        "]\n",
-        "instances_mask_single=[\n",
-        "    lb_types.MaskInstance(color_rgb=(76, 104, 177), name= \"video_mask\")\n",
-        "]\n",
-        "\n",
-        "\n",
-        "## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame\n",
-        "frames_cp_mask_url = [\n",
-        "    {\"1\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png\"},\n",
-        "    {\"24\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"},\n",
-        "    {\"26\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png\" }\n",
-        "]\n",
-        "\n",
-        "rgb_mask_tool = [(227, 135, 126) ,(169, 248, 152),(83, 152, 103)]\n",
-        "cp_masks = []\n",
-        "unique_colors = set()\n",
-        "\n",
-        "\n",
-        "lb_frames = []\n",
-        "lb_instances = []\n",
-        "counter = 0\n",
-        "\n",
-        "for d in frames_cp_mask_url:\n",
-        "    for frame_no, v in d.items():\n",
-        "        response = requests.get(v)\n",
-        "        colors = extract_rgb_colors_from_url(v)\n",
-        "        for color in colors:\n",
-        "            if not color in unique_colors:\n",
-        "                unique_colors.add(color)\n",
-        "                name = \"video_mask\" if color in rgb_mask_tool else \"mask_with_text_subclass\"\n",
-        "                lb_instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n",
-        "                counter += 1\n",
-        "        lb_frames.append(\n",
-        "            lb_types.MaskFrame(\n",
-        "                index=frame_no,\n",
-        "                im_bytes=response.content\n",
-        "            )\n",
-        "        )\n",
-        "cp_masks.append(lb_types.VideoMaskAnnotation(\n",
-        "    frames=lb_frames + frames_mask_single,\n",
-        "    instances=lb_instances + instances_mask_single\n",
-        "))\n",
-        "\n",
-        "pp.pprint(lb_frames)\n",
-        "pp.pprint(cp_masks)\n",
-        "\n",
-        "\n",
-        "\n",
-        "# NDJSON - single tool\n",
-        "video_mask_ndjson_bytes_2 = {\n",
-        "    'masks': {\n",
-        "      'frames': [\n",
-        "          {\n",
-        "              \"index\" : 31,\n",
-        "              \"imBytes\": img_bytes,\n",
-        "          },\n",
-        "          {\n",
-        "              \"index\" : 34,\n",
-        "              \"imBytes\": img_bytes,\n",
-        "          }\n",
-        "      ],\n",
-        "      'instances': [\n",
-        "          {\n",
-        "              \"colorRGB\" : [76, 104, 177],\n",
-        "              \"name\" : \"video_mask\"\n",
-        "          }\n",
-        "      ]\n",
-        "    }\n",
-        " }"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Multiple instances of bounding box annotations in the same frame"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Fist instance of bounding box ranging from frame 22 to 27\n",
-        "bbox_annotation_1 = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=22,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=27,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]),\n",
-        "      )\n",
-        "  )\n",
-        "]\n",
-        "# NDJSON example:\n",
-        "bbox_frame_annotation_ndjson = {\n",
-        "    \"name\": \"bbox_video\",\n",
-        "    \"segments\": [{\n",
-        "        \"keyframes\": [\n",
-        "          {\n",
-        "            \"frame\": 22,\n",
-        "            \"bbox\":  bbox_dm\n",
-        "          },\n",
-        "          {\n",
-        "            \"frame\": 27,\n",
-        "            \"bbox\": bbox_dm2\n",
-        "          }\n",
-        "\n",
-        "        ]\n",
-        "      }]\n",
-        "}\n",
-        "\n",
-        "# Second instance of bounding box ranging from frame 22 to 27\n",
-        "bbox_annotation_2 = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=22,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]),\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=27,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]),\n",
-        "      )\n",
-        "  )\n",
-        "]\n",
-        "# NDJSON\n",
-        "bbox_frame_annotation_ndjson2 = {\n",
-        "      \"name\": \"bbox_video\",\n",
-        "      \"segments\": [{\n",
-        "          \"keyframes\": [\n",
-        "            {\n",
-        "              \"frame\": 22,\n",
-        "              \"bbox\": bbox_dm\n",
-        "            },\n",
-        "            {\n",
-        "              \"frame\": 27,\n",
-        "              \"bbox\": bbox_dm2\n",
-        "            }\n",
-        "          ]\n",
-        "        }]\n",
-        "    }\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## End-to-end example: Import pre-labels or ground truth"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"sample-video-jellyfish.mp4\"\n",
-        "asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n",
-        "    \"global_key\": global_key,\n",
-        "    \"media_type\": \"VIDEO\"\n",
-        " }\n",
-        "\n",
-        "dataset = client.create_dataset(\n",
-        "    name=\"video_demo_dataset\",\n",
-        "    iam_integration=None # If this argument is removed, labelbox will use the default integration for your organization.\n",
-        ")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 2: Create/select an ontology\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the bounding box annotation above, we provided the `name` as `bbox_video`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_video`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-        "\n",
-        "\n",
-        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n",
-        "        lb.Tool(\n",
-        "          tool=lb.Tool.Type.BBOX, name=\"bbox_class\",\n",
-        "          classifications=[\n",
-        "            lb.Classification(\n",
-        "              class_type=lb.Classification.Type.RADIO,\n",
-        "              name=\"checklist_class\",\n",
-        "              scope = lb.Classification.Scope.INDEX,\n",
-        "              options=[\n",
-        "                lb.Option(value=\"first_checklist_answer\"),\n",
-        "                lb.Option(value=\"second_checklist_answer\")\n",
-        "              ]\n",
-        "            )\n",
-        "          ]\n",
-        "        ),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION,\n",
-        "                name=\"mask_with_text_subclass\",\n",
-        "                classifications=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.TEXT,\n",
-        "                        name=\"sub_free_text\")\n",
-        "                    ]\n",
-        "                )\n",
-        "    ],\n",
-        "    classifications=[\n",
-        "        lb.Classification(\n",
-        "            class_type=lb.Classification.Type.CHECKLIST,\n",
-        "            name=\"checklist_class\",\n",
-        "            scope = lb.Classification.Scope.INDEX, ## Need to defined scope for frame classifications\n",
-        "            options=[\n",
-        "                lb.Option(value=\"first_checklist_answer\"),\n",
-        "                lb.Option(value=\"second_checklist_answer\")\n",
-        "            ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "            class_type=lb.Classification.Type.RADIO,\n",
-        "            name=\"radio_class\",\n",
-        "            scope = lb.Classification.Scope.INDEX,\n",
-        "            options=[\n",
-        "                lb.Option(value=\"first_radio_answer\"),\n",
-        "                lb.Option(value=\"second_radio_answer\")\n",
-        "            ]\n",
-        "        ),\n",
-        "         lb.Classification(\n",
-        "              class_type=lb.Classification.Type.RADIO,\n",
-        "              name=\"nested_radio_question\",\n",
-        "              options=[\n",
-        "                  lb.Option(\"first_radio_answer\",\n",
-        "                        options=[\n",
-        "                            lb.Classification(\n",
-        "                                class_type=lb.Classification.Type.RADIO,\n",
-        "                                name=\"sub_radio_question\",\n",
-        "                                options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                            )\n",
-        "                        ]\n",
-        "                  )\n",
-        "              ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.CHECKLIST,\n",
-        "          name=\"nested_checklist_question\",\n",
-        "          options=[\n",
-        "              lb.Option(\"first_checklist_answer\",\n",
-        "                options=[\n",
-        "                  lb.Classification(\n",
-        "                      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                      name=\"sub_checklist_question\",\n",
-        "                      options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "                  )\n",
-        "              ]\n",
-        "            )\n",
-        "          ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.RADIO,\n",
-        "          name=\"radio_class_global\",\n",
-        "          options=[\n",
-        "                lb.Option(value=\"first_radio_answer\"),\n",
-        "                lb.Option(value=\"second_radio_answer\")\n",
-        "            ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.CHECKLIST,\n",
-        "          name=\"checklist_class_global\",\n",
-        "          options=[\n",
-        "                lb.Option(value=\"first_checklist_answer\"),\n",
-        "                lb.Option(value=\"second_checklist_answer\")\n",
-        "          ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.TEXT,\n",
-        "          name=\"free_text\"\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Video Annotation Import Demo Ontology\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Video)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 3: Create a labeling project \n",
-        "Connect the ontology to the labeling project."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(name=\"Video Annotation Import Demo\",\n",
-        "                                    media_type=lb.MediaType.Video)\n",
-        "\n",
-        "## connect ontology to your project\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 4: Send a batch of data rows to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "batch = project.create_batch(\n",
-        "  \"first-batch-video-demo2\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # A paginated collection of data row objects, a list of data rows or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(\"Batch: \", batch)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 5: Create the annotations payload \n",
-        "Create the annotations payload using the snippets of code above.\n",
-        "\n",
-        "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python Annotation Types"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "annotations_list = [\n",
-        "          checklist_annotation,\n",
-        "          radio_annotation,\n",
-        "          bbox_annotation,\n",
-        "          frame_bbox_with_checklist_subclass_annotation,\n",
-        "          bbox_annotation_1,\n",
-        "          bbox_annotation_2,\n",
-        "          point_annotation,\n",
-        "          polyline_annotation,\n",
-        "          global_checklist_annotation,\n",
-        "          global_radio_annotation,\n",
-        "          nested_checklist_annotation,\n",
-        "          nested_radio_annotation,\n",
-        "          text_annotation,\n",
-        "          cp_masks\n",
-        "      ]\n",
-        "\n",
-        "for annotation in annotations_list:\n",
-        "    label.append(\n",
-        "        lb_types.Label(\n",
-        "            data= {\"global_key\": global_key},\n",
-        "            annotations = annotation\n",
-        "        )\n",
-        "    )\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### NDJSON annotations\n",
-        "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "First, let\"s update the bbox with nested classifications with the corresponding featureSchemaId"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "\n",
-        "annotations_list_ndjson = [\n",
-        "    point_annotation_ndjson,\n",
-        "    bbox_annotation_ndjson,\n",
-        "    polyline_frame_annotation_ndjson,\n",
-        "    frame_checklist_classification_ndjson,\n",
-        "    frame_radio_classification_ndjson,\n",
-        "    nested_radio_annotation_ndjson,\n",
-        "    nested_checklist_annotation_ndjson,\n",
-        "    frame_bbox_with_checklist_subclass_annotation_ndjson,\n",
-        "    global_radio_classification_ndjson,\n",
-        "    global_checklist_classification_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    bbox_frame_annotation_ndjson,\n",
-        "    bbox_frame_annotation_ndjson2,\n",
-        "    video_mask_ndjson_bytes_2\n",
-        "]\n",
-        "\n",
-        "for annotation in annotations_list_ndjson:\n",
-        "  annotation.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotation)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
-        "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model-Assisted Labeling (MAL)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload MAL label for this data row in project\n",
-        "upload_job_mal = lb.MALPredictionImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"mal_import_job-\" + str(uuid.uuid4()),\n",
-        "    predictions=label)\n",
-        "\n",
-        "upload_job_mal.wait_until_done()\n",
-        "print(\"Errors:\", upload_job_mal.errors)\n",
-        "print(\"Status of uploads: \", upload_job_mal.statuses)\n",
-        "print(\"   \")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# For this demo either run MAL or Ground truth import, not both.\n",
-        "\n",
-        "# upload_job_label_import = lb.LabelImport.create_from_objects(\n",
-        "#     client = client,\n",
-        "#     project_id = project.uid,\n",
-        "#     name = \"label_import_job-\" + str(uuid.uuid4()),\n",
-        "#     labels=label\n",
-        "# )\n",
-        "\n",
-        "# upload_job_label_import.wait_until_done()\n",
-        "# print(\"Errors:\", upload_job_label_import.errors)\n",
-        "# print(\"Status of uploads: \", upload_job_label_import.statuses)\n",
-        "# print(\"   \")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Optional deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Delete Project\n",
-        "# project.delete()\n",
-        "#dataset.delete()\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Video Annotation Import\n",
+                "\n",
+                "* Annotations must be created and uploaded using NDJSON\n",
+                "* Supported annotations that can be uploaded through the SDK:\n",
+                "    * Bounding box\n",
+                "    * Point\n",
+                "    * Polyline \n",
+                "    * Radio classifications \n",
+                "    * Checklist classifications \n",
+                "    *  Segmentation masks\n",
+                "* **NOT** supported:\n",
+                "    * Polygons \n",
+                "\n",
+                "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO\nimport pprint\n\npp = pprint.PrettyPrinter(indent=4)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Replace with your API key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported annotations for video\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding box: (frame-based)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Point (frame-based)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polyline (frame-based)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_frame_annotation_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Radio and checklist (frame-based)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n# Python annotation\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n]\n\n## NDJSON\nframe_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Checklist and radio (global)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "##### Global Classifications #######\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}\n\n# Python annotation\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Nested radio and checklist (global)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "######### Free text classification ###########\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature\"s name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding box with sub-classifications (frame-based)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\"\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\"\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Masks (frame-based)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "def extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "### Raster Segmentation (Byte string array)\n##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation\n\n# Single mask\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\nresponse = requests.get(url)\nimg_bytes = base64.b64encode(response.content).decode(\"utf-8\")\n\n# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain\n# all frames and instances\nframes_mask_single = [\n    lb_types.MaskFrame(\n        index=20,\n        im_bytes=response.\n        content,  # Instead of bytes you could also pass an instance URI : instance_uri=url\n    )\n]\ninstances_mask_single = [\n    lb_types.MaskInstance(color_rgb=(76, 104, 177), name=\"video_mask\")\n]\n\n## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame\nframes_cp_mask_url = [\n    {\n        \"1\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png\"\n    },\n    {\n        \"24\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\n    },\n    {\n        \"26\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png\"\n    },\n]\n\nrgb_mask_tool = [(227, 135, 126), (169, 248, 152), (83, 152, 103)]\ncp_masks = []\nunique_colors = set()\n\nlb_frames = []\nlb_instances = []\ncounter = 0\n\nfor d in frames_cp_mask_url:\n    for frame_no, v in d.items():\n        response = requests.get(v)\n        colors = extract_rgb_colors_from_url(v)\n        for color in colors:\n            if not color in unique_colors:\n                unique_colors.add(color)\n                name = (\"video_mask\" if color in rgb_mask_tool else\n                        \"mask_with_text_subclass\")\n                lb_instances.append(\n                    lb_types.MaskInstance(color_rgb=color, name=name))\n                counter += 1\n        lb_frames.append(\n            lb_types.MaskFrame(index=frame_no, im_bytes=response.content))\ncp_masks.append(\n    lb_types.VideoMaskAnnotation(\n        frames=lb_frames + frames_mask_single,\n        instances=lb_instances + instances_mask_single,\n    ))\n\npp.pprint(lb_frames)\npp.pprint(cp_masks)\n\n# NDJSON - single tool\nvideo_mask_ndjson_bytes_2 = {\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\": 31,\n                \"imBytes\": img_bytes,\n            },\n            {\n                \"index\": 34,\n                \"imBytes\": img_bytes,\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": [76, 104, 177],\n            \"name\": \"video_mask\"\n        }],\n    }\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Multiple instances of bounding box annotations in the same frame"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Fist instance of bounding box ranging from frame 22 to 27\nbbox_annotation_1 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON example:\nbbox_frame_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}\n\n# Second instance of bounding box ranging from frame 22 to 27\nbbox_annotation_2 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON\nbbox_frame_annotation_ndjson2 = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## End-to-end example: Import pre-labels or ground truth"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"sample-video-jellyfish.mp4\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"VIDEO\",\n}\n\ndataset = client.create_dataset(\n    name=\"video_demo_dataset\",\n    iam_integration=\n    None,  # If this argument is removed, labelbox will use the default integration for your organization.\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 2: Create/select an ontology\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+                "\n",
+                "For example, when we create the bounding box annotation above, we provided the `name` as `bbox_video`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_video`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+                "\n",
+                "\n",
+                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.INDEX,\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## Need to defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Video Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 3: Create a labeling project \n",
+                "Connect the ontology to the labeling project."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(name=\"Video Annotation Import Demo\",\n                                media_type=lb.MediaType.Video)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 4: Send a batch of data rows to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "batch = project.create_batch(\n    \"first-batch-video-demo2\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # A paginated collection of data row objects, a list of data rows or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 5: Create the annotations payload \n",
+                "Create the annotations payload using the snippets of code above.\n",
+                "\n",
+                "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python Annotation Types"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass_annotation,\n    bbox_annotation_1,\n    bbox_annotation_2,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n    cp_masks,\n]\n\nfor annotation in annotations_list:\n    label.append(\n        lb_types.Label(data={\"global_key\": global_key}, annotations=annotation))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### NDJSON annotations\n",
+                "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "First, let\"s update the bbox with nested classifications with the corresponding featureSchemaId"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\n\nannotations_list_ndjson = [\n    point_annotation_ndjson,\n    bbox_annotation_ndjson,\n    polyline_frame_annotation_ndjson,\n    frame_checklist_classification_ndjson,\n    frame_radio_classification_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    frame_bbox_with_checklist_subclass_annotation_ndjson,\n    global_radio_classification_ndjson,\n    global_checklist_classification_ndjson,\n    text_annotation_ndjson,\n    bbox_frame_annotation_ndjson,\n    bbox_frame_annotation_ndjson2,\n    video_mask_ndjson_bytes_2,\n]\n\nfor annotation in annotations_list_ndjson:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
+                "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model-Assisted Labeling (MAL)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=label,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# For this demo either run MAL or Ground truth import, not both.\n\n# upload_job_label_import = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name = \"label_import_job-\" + str(uuid.uuid4()),\n#     labels=label\n# )\n\n# upload_job_label_import.wait_until_done()\n# print(\"Errors:\", upload_job_label_import.errors)\n# print(\"Status of uploads: \", upload_job_label_import.statuses)\n# print(\"   \")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Optional deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index 6ed7b6403..fb42578a2 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -1,265 +1,187 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=190/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Basic project/datasets overview\n",
-        "\n",
-        "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import random"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY=None\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# For the purpose of this demo get a single project/dataset id from your organization\n",
-        "\n",
-        "# Get a single Project id\n",
-        "# get_projects returns a PaginatedCollection object, which is iterable.\n",
-        "project = next(client.get_projects())\n",
-        "project_id=project.uid\n",
-        "project_name=project.name\n",
-        "print(\"Project ID: \", project_id)\n",
-        "print(\"Project Name:\", project_name)\n",
-        "\n",
-        "print(\"-\" * 40)\n",
-        "\n",
-        "# Get a single dataset id\n",
-        "# get_datasets returns a PaginatedCollection object, which is iterable.\n",
-        "dataset = next(client.get_datasets())\n",
-        "dataset_id = dataset.uid\n",
-        "dataset_name = dataset.name\n",
-        "print(\"Dataset ID: \", dataset_id)\n",
-        "print(\"Dataset Name:\" , dataset_name)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Fetch the project and dataset by using the IDs fetched in the previous cell\n",
-        "project = client.get_project(project_id)\n",
-        "dataset = client.get_dataset(dataset_id)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(\"Project: \", project)\n",
-        "print(\"Dataset: \", dataset)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Fields\n",
-        "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
-        "* These fields are attributes of the object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(project.name)\n",
-        "print(dataset.name)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.update(description=\"new description field\")\n",
-        "print(project.description)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Pagination\n",
-        "* Queries that return a list of database objects are return as a PaginatedCollection\n",
-        "* Limits the data that is being returned for better performance"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels_paginated_collection = project.labels()\n",
-        "print(\"Type of collection: \", type(labels_paginated_collection))\n",
-        "\n",
-        "# A paginated collection can be parsed by using list()\n",
-        "# list(paginated...) should be avoided for queries that could return more than a dozen results\n",
-        "print(\"Number of labels :\", len(list(labels_paginated_collection)))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n",
-        "# Iterate over the paginated collection\n",
-        "try:\n",
-        "  single_label = next(project.labels())\n",
-        "  print(single_label)\n",
-        "except StopIteration:\n",
-        "  print(\"Project has no labels !\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Query parameters\n",
-        "* Query with the following conventions:\n",
-        "    * `DbObject.Field`"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n",
-        "\n",
-        "projects = client.get_projects(\n",
-        "    where=((lb.Project.name == project_name) &\n",
-        "           (lb.Project.description == \"new description field\")))\n",
-        "\n",
-        "# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n",
-        "# So even if there is one element returned it is in a paginatedCollection.\n",
-        "print(projects)\n",
-        "print(next(projects, None))\n",
-        "print(next(projects, None))\n",
-        "print(next(projects, None))\n",
-        "# We can see there is only one."
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Querying Limitations\n",
-        "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
-        "* The below query is not valid since get_project returns a project not a dataset\n",
-        ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Relationships between projects and batches/datasets\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "sample_project_batches = project.batches()\n",
-        "\n",
-        "list(sample_project_batches)\n",
-        "\n",
-        "for b in sample_project_batches:\n",
-        "  print(f\" Name of project : {b.project().name}\")\n",
-        "  print(f\" Name of batches in project: {b.name}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Basic project/datasets overview\n",
+                "\n",
+                "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport random",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Fields\n",
+                "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
+                "* These fields are attributes of the object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "print(project.name)\nprint(dataset.name)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.update(description=\"new description field\")\nprint(project.description)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Pagination\n",
+                "* Queries that return a list of database objects are return as a PaginatedCollection\n",
+                "* Limits the data that is being returned for better performance"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Query parameters\n",
+                "* Query with the following conventions:\n",
+                "    * `DbObject.Field`"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Querying Limitations\n",
+                "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
+                "* The below query is not valid since get_project returns a project not a dataset\n",
+                ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Relationships between projects and batches/datasets\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index 4424bb68e..7645f498e 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -1,487 +1,301 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=190/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Batches\n",
-        "https://docs.labelbox.com/docs/batches"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* A batch is collection of data rows.\n",
-        "* A data row cannot be part of more than one batch in a given project.\n",
-        "* Batches work for all data types, but there can only be one data type per project.\n",
-        "* Batches can not be shared between projects.\n",
-        "* Batches may have data rows from multiple datasets.\n",
-        "* Currently, only benchmarks quality settings is supported in batch projects\n",
-        "* You can set the priority for each batch."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install \"labelbox[data]\" -q"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import random\n",
-        "import uuid\n",
-        "import json"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## API key and client\n",
-        "Provide a valid API key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create a dataset and data rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a dataset\n",
-        "dataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n",
-        "\n",
-        "uploads = []\n",
-        "# Generate data rows\n",
-        "for i in range(1,9):\n",
-        "    uploads.append({\n",
-        "        'row_data':  f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\": \"TEST-ID-%id\" % uuid.uuid1(),\n",
-        "    })\n",
-        "\n",
-        "data_rows = dataset.create_data_rows(uploads)\n",
-        "data_rows.wait_till_done()\n",
-        "print(\"ERRORS: \" , data_rows.errors)\n",
-        "print(\"RESULT URL: \", data_rows.result_url)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup batch project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(\n",
-        "  name=\"Demo-Batches-Project\",\n",
-        "  media_type=lb.MediaType.Image\n",
-        ")\n",
-        "print(\"Project Name: \", project.name, \"Project ID: \", project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create batches"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Select all data rows from the dataset\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client.enable_experimental = True\n",
-        "\n",
-        "export_task = dataset.export()\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "data_rows = []\n",
-        "\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  data_row = json.loads(output.json_str)\n",
-        "  data_rows.append(data_row)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\n",
-        "print(\"Number of global keys:\", len(global_keys))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Select a random sample\n",
-        "This method is useful if you have large datasets and only want to work with a handful of data rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "sample = random.sample(global_keys, 4)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a batch\n",
-        "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "batch = project.create_batch(\n",
-        "  name=\"Demo-First-Batch\", # Each batch in a project must have a unique name\n",
-        "  global_keys=sample, # A list of data rows or data row ids\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")\n",
-        "# number of data rows in the batch\n",
-        "print(\"Number of data rows in batch: \", batch.size)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create multiple batches\n",
-        "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
-        "\n",
-        "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
-        "\n",
-        "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
-        "\n",
-        "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# First, we must create a second project so that we can re-use the data rows we already created.\n",
-        "second_project = client.create_project(\n",
-        "  name=\"Second-Demo-Batches-Project\",\n",
-        "  media_type=lb.MediaType.Image\n",
-        ")\n",
-        "print(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n",
-        "\n",
-        "# Then, use the method that will create multiple batches if necessary.\n",
-        "task = second_project.create_batches(\n",
-        "  name_prefix=\"demo-create-batches-\",\n",
-        "  global_keys=global_keys,\n",
-        "  priority=5\n",
-        ")\n",
-        "\n",
-        "print(\"Errors: \", task.errors())\n",
-        "print(\"Result: \", task.result())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create batches from a dataset\n",
-        "\n",
-        "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
-        "\n",
-        "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# First, we must create a third project so that we can re-use the data rows we already created.\n",
-        "third_project = client.create_project(\n",
-        "  name=\"Third-Demo-Batches-Project\",\n",
-        "  media_type=lb.MediaType.Image\n",
-        ")\n",
-        "print(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n",
-        "\n",
-        "# Then, use the method to create batches from a dataset.\n",
-        "task = third_project.create_batches_from_dataset(\n",
-        "    name_prefix=\"demo-batches-from-dataset-\",\n",
-        "    dataset_id=dataset.uid,\n",
-        "    priority=5\n",
-        ")\n",
-        "\n",
-        "print(\"Errors: \", task.errors())\n",
-        "print(\"Result: \", task.result())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Manage Batches\n",
-        "Note: You can view your batch data through the **Data Rows** tab."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Export Batches"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Create and Attach Ontology to Project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "classification_features = [\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.CHECKLIST,\n",
-        "        name=\"Quality Issues\",\n",
-        "        options=[\n",
-        "            lb.Option(value=\"blurry\", label=\"Blurry\"),\n",
-        "            lb.Option(value=\"distorted\", label=\"Distorted\")\n",
-        "        ]\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[],\n",
-        "    classifications=classification_features\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\n",
-        "  \"Ontology from new features\",\n",
-        "  ontology_builder.asdict(),\n",
-        "  media_type=lb.MediaType.Image\n",
-        ")\n",
-        "\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export from Project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client.enable_experimental = True\n",
-        "\n",
-        "export_params = {\n",
-        " \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"batch_ids\" : [batch.uid] # Include batch ids if you only want to export specific batches, otherwise,\n",
-        "  #you can export all the data without using this parameter\n",
-        "}\n",
-        "filters = {}\n",
-        "\n",
-        "# A task is returned, this provides additional information about the status of your task, such as\n",
-        "# any errors encountered\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_rows = []\n",
-        "\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  data_row = json.loads(output.json_str)\n",
-        "  data_rows.append(data_row)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export the data row iDs\n",
-        "data_rows = [dr for dr in data_rows]\n",
-        "print(\"Data rows in batch: \", data_rows)\n",
-        "\n",
-        "## List the batches in your project\n",
-        "for batch in project.batches():\n",
-        "    print(\"Batch name: \", batch.name , \"  Batch ID:\", batch.uid)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Archive a batch"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Archiving a batch removes all queued data rows in the batch from the project\n",
-        "batch.remove_queued_data_rows()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Clean up \n",
-        "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Delete Batch\n",
-        "#batch.delete()\n",
-        "\n",
-        "# Delete Project\n",
-        "#project.delete()\n",
-        "\n",
-        "# Delete DataSet\n",
-        "#dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Batches\n",
+                "https://docs.labelbox.com/docs/batches"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* A batch is collection of data rows.\n",
+                "* A data row cannot be part of more than one batch in a given project.\n",
+                "* Batches work for all data types, but there can only be one data type per project.\n",
+                "* Batches can not be shared between projects.\n",
+                "* Batches may have data rows from multiple datasets.\n",
+                "* Currently, only benchmarks quality settings is supported in batch projects\n",
+                "* You can set the priority for each batch."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install \"labelbox[data]\" -q",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API key and client\n",
+                "Provide a valid API key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create a dataset and data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup batch project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create batches"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Select all data rows from the dataset\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Select a random sample\n",
+                "This method is useful if you have large datasets and only want to work with a handful of data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "sample = random.sample(global_keys, 4)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a batch\n",
+                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create multiple batches\n",
+                "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
+                "\n",
+                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
+                "\n",
+                "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
+                "\n",
+                "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create batches from a dataset\n",
+                "\n",
+                "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
+                "\n",
+                "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Manage Batches\n",
+                "Note: You can view your batch data through the **Data Rows** tab."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Export Batches"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create and Attach Ontology to Project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export from Project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Archive a batch"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Clean up \n",
+                "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/custom_embeddings.ipynb b/examples/basics/custom_embeddings.ipynb
index 20221a80a..65f4952d6 100644
--- a/examples/basics/custom_embeddings.ipynb
+++ b/examples/basics/custom_embeddings.ipynb
@@ -1,375 +1,288 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings_sdk.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings_sdk.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Custom Embeddings\n",
-        "\n",
-        "You can improve your data exploration and similarity search experience by adding your own custom embeddings. Labelbox allows you to upload up to 10 different custom embeddings per workspace on any kind of data. You can experiment with different embeddings to power your data selection."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set up "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip3 install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import numpy as np\n",
-        "import json\n",
-        "import uuid\n",
-        "import random"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace with your API key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Select data rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "- Get images from a Labelbox dataset\n",
-        "- To improve similarity search, you need to upload custom embeddings to at least 1,000 data rows.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "DATASET_ID = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset = client.get_dataset(dataset_id=DATASET_ID)\n",
-        "export_task = dataset.export()\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_rows = []\n",
-        "\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  data_row = json.loads(output.json_str)\n",
-        "  data_rows.append(data_row)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_row_dict = [{\"data_row_id\": dr[\"data_row\"][\"id\"]} for dr in data_rows]\n",
-        "data_row_dict = data_row_dict[:1000] # keep the first 1000 examples for the sake of this demo"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create custom embedding payload "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Generate random vectors for embeddings (max : 2048 dimensions)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "nb_data_rows = len(data_row_dict)\n",
-        "print(\"Number of data rows: \", nb_data_rows)\n",
-        "# Labelbox supports custom embedding vectors of dimension up to 2048\n",
-        "custom_embeddings = [list(np.random.random(2048)) for _ in range(nb_data_rows)]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "List all custom embeddings available in your Labelbox workspace"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "embeddings = client.get_embeddings()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Choose an existing embedding type or create a new one"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Name of the custom embedding must be unique\n",
-        "embedding = client.create_embedding(\"my_custom_embedding_2048_dimensions\", 2048)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Create payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "The payload should encompass the `key` (data row id  or global key) and the new embedding vector data. Note that the `dataset.upsert_data_rows()` operation will only update the values you pass in the payload; all other existing row data will not be modified."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "payload = []\n",
-        "for data_row_dict, custom_embedding in zip(data_row_dict,custom_embeddings):\n",
-        "  payload.append({\"key\": lb.UniqueId(data_row_dict['data_row_id']), \"embeddings\": [{\"embedding_id\": embedding.id, \"vector\": custom_embedding}]})\n",
-        "\n",
-        "print('payload', len(payload),payload[:1])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Upsert data rows with custom embeddings"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "task = dataset.upsert_data_rows(payload)\n",
-        "task.wait_till_done()\n",
-        "print(task.errors)\n",
-        "print(task.status)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Get the count of imported vectors for a custom embedding"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Count how many data rows have a specific custom embedding (this can take a couple of minutes)\n",
-        "count = embedding.get_imported_vector_count()\n",
-        "print(count)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Delete custom embedding type"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#embedding.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload custom embeddings during data row creation"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Create a dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a dataset\n",
-        "dataset_new = client.create_dataset(name=\"data_rows_with_embeddings\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Fetch an embedding (2048 dimension)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "embedding = client.get_embedding_by_name(\"my_custom_embedding_2048_dimensions\")\n",
-        "vector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Upload data rows with embeddings"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "uploads = []\n",
-        "# Generate data rows\n",
-        "for i in range(1,9):\n",
-        "    uploads.append({\n",
-        "        \"row_data\":  f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\": \"TEST-ID-%id\" % uuid.uuid1(),\n",
-        "        \"embeddings\": [{\n",
-        "                    \"embedding_id\": embedding.id,\n",
-        "                    \"vector\": vector\n",
-        "                }]\n",
-        "    })\n",
-        "\n",
-        "task1 = dataset_new.create_data_rows(uploads)\n",
-        "task1.wait_till_done()\n",
-        "print(\"ERRORS: \" , task1.errors)\n",
-        "print(\"RESULTS:\" , task1.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Custom Embeddings\n",
+                "\n",
+                "You can improve your data exploration and similarity search experience by adding your own custom embeddings. Labelbox allows you to upload up to 10 different custom embeddings per workspace on any kind of data. You can experiment with different embeddings to power your data selection."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Set up "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip3 install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport numpy as np\nimport json\nimport uuid\nimport random",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Replace with your API key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Select data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "- Get images from a Labelbox dataset\n",
+                "- To improve similarity search, you need to upload custom embeddings to at least 1,000 data rows.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "DATASET_ID = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "dataset = client.get_dataset(dataset_id=DATASET_ID)\nexport_task = dataset.export()\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "data_row_dict = [{\"data_row_id\": dr[\"data_row\"][\"id\"]} for dr in data_rows]\ndata_row_dict = data_row_dict[:\n                              1000]  # keep the first 1000 examples for the sake of this demo",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Create custom embedding payload "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Generate random vectors for embeddings (max : 2048 dimensions)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "nb_data_rows = len(data_row_dict)\nprint(\"Number of data rows: \", nb_data_rows)\n# Labelbox supports custom embedding vectors of dimension up to 2048\ncustom_embeddings = [list(np.random.random(2048)) for _ in range(nb_data_rows)]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "List all custom embeddings available in your Labelbox workspace"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "embeddings = client.get_embeddings()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Choose an existing embedding type or create a new one"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Name of the custom embedding must be unique\nembedding = client.create_embedding(\"my_custom_embedding_2048_dimensions\", 2048)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Create payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "The payload should encompass the `key` (data row id  or global key) and the new embedding vector data. Note that the `dataset.upsert_data_rows()` operation will only update the values you pass in the payload; all other existing row data will not be modified."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "payload = []\nfor data_row_dict, custom_embedding in zip(data_row_dict, custom_embeddings):\n    payload.append({\n        \"key\":\n            lb.UniqueId(data_row_dict[\"data_row_id\"]),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": custom_embedding\n        }],\n    })\n\nprint(\"payload\", len(payload), payload[:1])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Upload payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Upsert data rows with custom embeddings"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "task = dataset.upsert_data_rows(payload)\ntask.wait_till_done()\nprint(task.errors)\nprint(task.status)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Get the count of imported vectors for a custom embedding"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Count how many data rows have a specific custom embedding (this can take a couple of minutes)\ncount = embedding.get_imported_vector_count()\nprint(count)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Delete custom embedding type"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# embedding.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Upload custom embeddings during data row creation"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Create a dataset"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a dataset\ndataset_new = client.create_dataset(name=\"data_rows_with_embeddings\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Fetch an embedding (2048 dimension)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "embedding = client.get_embedding_by_name(\"my_custom_embedding_2048_dimensions\")\nvector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Upload data rows with embeddings"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": vector\n        }],\n    })\n\ntask1 = dataset_new.create_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/data_row_metadata.ipynb b/examples/basics/data_row_metadata.ipynb
index 8bbee9be9..9c9f64987 100644
--- a/examples/basics/data_row_metadata.ipynb
+++ b/examples/basics/data_row_metadata.ipynb
@@ -1,460 +1,302 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Data Row Metadata\n",
-        "\n",
-        "Metadata is useful to better understand data on the platform to help with labeling review, model diagnostics, and data selection. This **should not be confused with attachments**. Attachments provide additional context for labelers but is not searchable within Catalog."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Metadata ontology\n",
-        "\n",
-        "We use a similar system for managing metadata as we do feature schemas. Metadata schemas are strongly typed to ensure we can provide the best experience in the App. Each metadata field can be uniquely accessed by id. Names are unique within the kind of metadata, reserved or custom. A DataRow can have a maximum of 5 metadata fields at a time.\n",
-        "\n",
-        "### Metadata kinds\n",
-        "\n",
-        "* **Enum**: A classification with options, only one option can be selected at a time\n",
-        "* **DateTime**: A utc ISO datetime \n",
-        "* **String**: A string of less than 500 characters\n",
-        "\n",
-        "### Reserved fields\n",
-        "\n",
-        "* **tag**: a free text field\n",
-        "* **split**: enum of train-valid-test\n",
-        "* **captureDateTime**: ISO 8601 datetime field. All times must be in UTC\n",
-        "\n",
-        "### Custom fields\n",
-        "\n",
-        "* **Embedding**: 128 float 32 vector used for similarity. To upload custom embeddings use the following [tutorial](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/basics/custom_embeddings.ipynb)\n",
-        "* Any metadata kind can be customized"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from datetime import datetime\n",
-        "from pprint import pprint\n",
-        "from labelbox.schema.data_row_metadata import DataRowMetadataKind\n",
-        "from uuid import uuid4"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get the current metadata ontology "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "mdo = client.get_data_row_metadata_ontology()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# list all your metadata ontology as a dictionary accessable by id\n",
-        "metadata_ontologies = mdo.fields_by_id\n",
-        "pprint(metadata_ontologies, indent=2)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Access metadata by name"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "split_field = mdo.reserved_by_name[\"split\"]\n",
-        "split_field"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "tag_field = mdo.reserved_by_name[\"tag\"]\n",
-        "tag_field"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "train_field = mdo.reserved_by_name[\"split\"][\"train\"]\n",
-        "train_field"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Construct metadata fields for existing metadata schemas\n",
-        "\n",
-        "To construct a metadata field you must provide the name for the metadata field and the value that will be uploaded. You can either construct a DataRowMetadataField object or specify the name and value in a dictionary format.\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Option 1: Specify metadata with a list of `DataRowMetadataField` objects. This is the recommended option since it comes with validation for metadata fields."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Construct a metadata field of string kind\n",
-        "tag_metadata_field = lb.DataRowMetadataField(\n",
-        "    name=\"tag\",\n",
-        "    value=\"tag_string\",\n",
-        ")\n",
-        "\n",
-        "# Construct an metadata field of datetime kind\n",
-        "capture_datetime_field = lb.DataRowMetadataField(\n",
-        "    name=\"captureDateTime\",\n",
-        "    value=datetime.utcnow(),\n",
-        ")\n",
-        "\n",
-        "# Construct a metadata field of Enums options\n",
-        "split_metadata_field = lb.DataRowMetadataField(\n",
-        "    name=\"split\",\n",
-        "    value=\"train\",\n",
-        ")\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Option 2: You can also specify the metadata fields with dictionary format without declaring the `DataRowMetadataField` objects.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Construct a dictionary of string metadata\n",
-        "tag_metadata_field_dict = {\n",
-        "    \"name\": \"tag\",\n",
-        "    \"value\": \"tag_string\",\n",
-        "}\n",
-        "\n",
-        "# Construct a dictionary of datetime metadata\n",
-        "capture_datetime_field_dict = {\n",
-        "    \"name\": \"captureDateTime\",\n",
-        "    \"value\": datetime.utcnow(),\n",
-        "}\n",
-        "\n",
-        "# Construct a dictionary of Enums options metadata\n",
-        "split_metadata_field_dict = {\n",
-        "    \"name\": \"split\",\n",
-        "    \"value\": \"train\",\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create a custom metadata schema with their corresponding fields\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Final\n",
-        "custom_metadata_fields = []\n",
-        "\n",
-        "# Create the schema for the metadata\n",
-        "number_schema = mdo.create_schema(\n",
-        "  name=\"numberMetadataCustom\",\n",
-        "  kind=DataRowMetadataKind.number\n",
-        ")\n",
-        "\n",
-        "# Add fields to the metadata schema\n",
-        "data_row_metadata_fields_number = lb.DataRowMetadataField(\n",
-        "    name=number_schema.name,\n",
-        "    value=5.0\n",
-        ")\n",
-        "\n",
-        "custom_metadata_fields.append(data_row_metadata_fields_number)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create the schema for an enum metadata\n",
-        "custom_metadata_fields = []\n",
-        "\n",
-        "enum_schema = mdo.create_schema(\n",
-        "  name=\"enumMetadata\",\n",
-        "  kind=DataRowMetadataKind.enum,\n",
-        "  options=[\"option1\", \"option2\"]\n",
-        ")\n",
-        "\n",
-        "# Add fields to the metadata schema\n",
-        "data_row_metadata_fields_enum_1 = lb.DataRowMetadataField(\n",
-        "  name=enum_schema.name,\n",
-        "  value=\"option1\"\n",
-        ")\n",
-        "custom_metadata_fields.append(data_row_metadata_fields_enum_1)\n",
-        "\n",
-        "\n",
-        "data_row_metadata_fields_enum_2 =  lb.DataRowMetadataField(\n",
-        "  name=enum_schema.name,\n",
-        "  value=\"option2\"\n",
-        ")\n",
-        "custom_metadata_fields.append(data_row_metadata_fields_enum_2)\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Inspect the newly created metadata schemas\n",
-        "metadata_ontologies = mdo.fields_by_id\n",
-        "pprint(metadata_ontologies, indent=2)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create data rows with metadata\n",
-        "\n",
-        "See our [documentation](https://docs.labelbox.com/docs/limits) for information on limits for uploading data rows in a single API operation."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# A simple example of uploading data rows with metadata\n",
-        "dataset = client.create_dataset(name=\"Simple Data Rows import with metadata example\")\n",
-        "global_key = \"s_basic.jpg\"\n",
-        "data_row = {\"row_data\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/basic.jpg\", \"global_key\": global_key}\n",
-        "# This line works with dictionaries as well as schemas and fields created with DataRowMetadataField\n",
-        "data_row['metadata_fields'] =  custom_metadata_fields + [ split_metadata_field , capture_datetime_field_dict, tag_metadata_field ]\n",
-        "\n",
-        "\n",
-        "task = dataset.create_data_rows([data_row])\n",
-        "task.wait_till_done()\n",
-        "result_task = task.result\n",
-        "print(result_task)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Update data row metadata"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get the data row that was uploaded in the previous cell\n",
-        "num_schema = mdo.get_by_name(\"numberMetadataCustom\")\n",
-        "\n",
-        "# Update the metadata\n",
-        "updated_metadata = lb.DataRowMetadataField(\n",
-        "  schema_id=num_schema.uid,\n",
-        "  value=10.2\n",
-        ")\n",
-        "\n",
-        "# Create data row payload\n",
-        "data_row_payload = lb.DataRowMetadata(\n",
-        "  global_key=global_key,\n",
-        "  fields=[updated_metadata]\n",
-        ")\n",
-        "\n",
-        "# Upsert the fields with the update metadata for number-metadata\n",
-        "mdo.bulk_upsert([data_row_payload])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Update metadata schema"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# update a name\n",
-        "number_schema = mdo.update_schema(name=\"numberMetadataCustom\", new_name=\"numberMetadataCustomNew\")\n",
-        "\n",
-        "# update an Enum metadata schema option's name, this only applies to Enum metadata schema.\n",
-        "enum_schema = mdo.update_enum_option(\n",
-        "  name=\"enumMetadata\",\n",
-        "  option=\"option1\",\n",
-        "  new_option=\"option3\"\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Accessing metadata\n",
-        "\n",
-        "You can examine an individual data row, including its metadata."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_row = next(dataset.data_rows())\n",
-        "for metadata_field in data_row.metadata_fields:\n",
-        "  print(metadata_field['name'], \":\", metadata_field['value'])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "You can bulk export metadata using data row IDs."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_rows_metadata = mdo.bulk_export([data_row.uid])\n",
-        "len(data_rows_metadata)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Delete custom metadata schema \n",
-        "You can delete custom metadata schema by name. If you wish to delete a metadata schema, uncomment the line below and insert the desired name."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#status = mdo.delete_schema(name=\"<metadata schema name>\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Data Row Metadata\n",
+                "\n",
+                "Metadata is useful to better understand data on the platform to help with labeling review, model diagnostics, and data selection. This **should not be confused with attachments**. Attachments provide additional context for labelers but is not searchable within Catalog."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Metadata ontology\n",
+                "\n",
+                "We use a similar system for managing metadata as we do feature schemas. Metadata schemas are strongly typed to ensure we can provide the best experience in the App. Each metadata field can be uniquely accessed by id. Names are unique within the kind of metadata, reserved or custom. A DataRow can have a maximum of 5 metadata fields at a time.\n",
+                "\n",
+                "### Metadata kinds\n",
+                "\n",
+                "* **Enum**: A classification with options, only one option can be selected at a time\n",
+                "* **DateTime**: A utc ISO datetime \n",
+                "* **String**: A string of less than 500 characters\n",
+                "\n",
+                "### Reserved fields\n",
+                "\n",
+                "* **tag**: a free text field\n",
+                "* **split**: enum of train-valid-test\n",
+                "* **captureDateTime**: ISO 8601 datetime field. All times must be in UTC\n",
+                "\n",
+                "### Custom fields\n",
+                "\n",
+                "* **Embedding**: 128 float 32 vector used for similarity. To upload custom embeddings use the following [tutorial](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/basics/custom_embeddings.ipynb)\n",
+                "* Any metadata kind can be customized"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom datetime import datetime\nfrom pprint import pprint\nfrom labelbox.schema.data_row_metadata import DataRowMetadataKind\nfrom uuid import uuid4",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Get the current metadata ontology "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "mdo = client.get_data_row_metadata_ontology()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# list all your metadata ontology as a dictionary accessable by id\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Access metadata by name"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "split_field = mdo.reserved_by_name[\"split\"]\nsplit_field",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "tag_field = mdo.reserved_by_name[\"tag\"]\ntag_field",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "train_field = mdo.reserved_by_name[\"split\"][\"train\"]\ntrain_field",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Construct metadata fields for existing metadata schemas\n",
+                "\n",
+                "To construct a metadata field you must provide the name for the metadata field and the value that will be uploaded. You can either construct a DataRowMetadataField object or specify the name and value in a dictionary format.\n",
+                "\n",
+                "\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Option 1: Specify metadata with a list of `DataRowMetadataField` objects. This is the recommended option since it comes with validation for metadata fields."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Construct a metadata field of string kind\ntag_metadata_field = lb.DataRowMetadataField(\n    name=\"tag\",\n    value=\"tag_string\",\n)\n\n# Construct an metadata field of datetime kind\ncapture_datetime_field = lb.DataRowMetadataField(\n    name=\"captureDateTime\",\n    value=datetime.utcnow(),\n)\n\n# Construct a metadata field of Enums options\nsplit_metadata_field = lb.DataRowMetadataField(\n    name=\"split\",\n    value=\"train\",\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Option 2: You can also specify the metadata fields with dictionary format without declaring the `DataRowMetadataField` objects.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Construct a dictionary of string metadata\ntag_metadata_field_dict = {\n    \"name\": \"tag\",\n    \"value\": \"tag_string\",\n}\n\n# Construct a dictionary of datetime metadata\ncapture_datetime_field_dict = {\n    \"name\": \"captureDateTime\",\n    \"value\": datetime.utcnow(),\n}\n\n# Construct a dictionary of Enums options metadata\nsplit_metadata_field_dict = {\n    \"name\": \"split\",\n    \"value\": \"train\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create a custom metadata schema with their corresponding fields\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Final\ncustom_metadata_fields = []\n\n# Create the schema for the metadata\nnumber_schema = mdo.create_schema(name=\"numberMetadataCustom\",\n                                  kind=DataRowMetadataKind.number)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_number = lb.DataRowMetadataField(\n    name=number_schema.name, value=5.0)\n\ncustom_metadata_fields.append(data_row_metadata_fields_number)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create the schema for an enum metadata\ncustom_metadata_fields = []\n\nenum_schema = mdo.create_schema(\n    name=\"enumMetadata\",\n    kind=DataRowMetadataKind.enum,\n    options=[\"option1\", \"option2\"],\n)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_enum_1 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option1\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_1)\n\ndata_row_metadata_fields_enum_2 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option2\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_2)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Inspect the newly created metadata schemas\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create data rows with metadata\n",
+                "\n",
+                "See our [documentation](https://docs.labelbox.com/docs/limits) for information on limits for uploading data rows in a single API operation."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# A simple example of uploading data rows with metadata\ndataset = client.create_dataset(\n    name=\"Simple Data Rows import with metadata example\")\nglobal_key = \"s_basic.jpg\"\ndata_row = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/basic.jpg\",\n    \"global_key\":\n        global_key,\n}\n# This line works with dictionaries as well as schemas and fields created with DataRowMetadataField\ndata_row[\"metadata_fields\"] = custom_metadata_fields + [\n    split_metadata_field,\n    capture_datetime_field_dict,\n    tag_metadata_field,\n]\n\ntask = dataset.create_data_rows([data_row])\ntask.wait_till_done()\nresult_task = task.result\nprint(result_task)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Update data row metadata"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get the data row that was uploaded in the previous cell\nnum_schema = mdo.get_by_name(\"numberMetadataCustom\")\n\n# Update the metadata\nupdated_metadata = lb.DataRowMetadataField(schema_id=num_schema.uid, value=10.2)\n\n# Create data row payload\ndata_row_payload = lb.DataRowMetadata(global_key=global_key,\n                                      fields=[updated_metadata])\n\n# Upsert the fields with the update metadata for number-metadata\nmdo.bulk_upsert([data_row_payload])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Update metadata schema"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# update a name\nnumber_schema = mdo.update_schema(name=\"numberMetadataCustom\",\n                                  new_name=\"numberMetadataCustomNew\")\n\n# update an Enum metadata schema option's name, this only applies to Enum metadata schema.\nenum_schema = mdo.update_enum_option(name=\"enumMetadata\",\n                                     option=\"option1\",\n                                     new_option=\"option3\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Accessing metadata\n",
+                "\n",
+                "You can examine an individual data row, including its metadata."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "data_row = next(dataset.data_rows())\nfor metadata_field in data_row.metadata_fields:\n    print(metadata_field[\"name\"], \":\", metadata_field[\"value\"])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "You can bulk export metadata using data row IDs."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "data_rows_metadata = mdo.bulk_export([data_row.uid])\nlen(data_rows_metadata)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Delete custom metadata schema \n",
+                "You can delete custom metadata schema by name. If you wish to delete a metadata schema, uncomment the line below and insert the desired name."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# status = mdo.delete_schema(name=\"<metadata schema name>\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/data_rows.ipynb b/examples/basics/data_rows.ipynb
index 3de7b6933..c240b0d30 100644
--- a/examples/basics/data_rows.ipynb
+++ b/examples/basics/data_rows.ipynb
@@ -1,541 +1,317 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Data rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Data rows are the assets that are being labeled. We currently support the following asset types:\n",
-        "    * Image\n",
-        "    * Text\n",
-        "    * Video\n",
-        "    * Geospatial / Tiled Imagery\n",
-        "    * Audio\n",
-        "    * Documents \n",
-        "    * HTML \n",
-        "    * DICOM \n",
-        "    * Conversational\n",
-        "* A data row cannot exist without belonging to a dataset.\n",
-        "* Data rows are added to labeling tasks by first attaching them to datasets and then creating batches in projects"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox -q"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid\n",
-        "import json"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get data rows from projects"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Pick a project with batches that have data rows with global keys\n",
-        "PROJECT_ID = \"<PROJECT-ID>\"\n",
-        "project = client.get_project(PROJECT_ID)\n",
-        "batches = list(project.batches())\n",
-        "print(batches)\n",
-        "# This is the same as\n",
-        "# -> dataset = client.get_dataset(dataset_id)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Fetch data rows from project's batches\n",
-        "\n",
-        "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client.enable_experimental = True\n",
-        "\n",
-        "batch_ids = [batch.uid for batch in batches]\n",
-        "\n",
-        "export_params = {\n",
-        " \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"batch_ids\" : batch_ids # Include batch ids if you only want to export specific batches, otherwise,\n",
-        "  #you can export all the data without using this parameter\n",
-        "}\n",
-        "filters = {}\n",
-        "\n",
-        "# A task is returned, this provides additional information about the status of your task, such as\n",
-        "# any errors encountered\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_rows = []\n",
-        "\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  data_row = json.loads(output.json_str)\n",
-        "  data_rows.append(data_row)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get single data row\n",
-        "data_row = data_rows[0]\n",
-        "print(data_row)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get labels from the data row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(\"Associated label(s)\", data_row[\"projects\"][project.uid][\"labels\"])\n",
-        "print(\"Global key\", data_row[\"data_row\"][\"global_key\"])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get data row ids by using global keys"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"<ENTER GLOBAL KEY>\"\n",
-        "task = client.get_data_row_ids_for_global_keys([global_key])\n",
-        "print(f\"Data row id: {task['results']}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create\n",
-        "We recommend the following methods to create data rows : `dataset.upsert_data_rows()`, and `dataset.create_data_rows()`, "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create data rows via `dataset.upsert_data_rows()`"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a dataset\n",
-        "dataset = client.create_dataset(name=\"data_rows_demo_dataset_6\")\n",
-        "# You can also upload metadata along with your data row\n",
-        "mdo = client.get_data_row_metadata_ontology()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "uploads = []\n",
-        "# Generate data rows\n",
-        "for i in range(1,8):\n",
-        "    uploads.append({\n",
-        "        \"row_data\":  f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\": \"TEST-ID-%id\" % uuid.uuid1(),\n",
-        "        ## add metadata (optional)\n",
-        "        \"metadata_fields\": [\n",
-        "            lb.DataRowMetadataField(\n",
-        "                schema_id=mdo.reserved_by_name[\"tag\"].uid,  # specify the schema id\n",
-        "                value=\"tag_string\", # typed inputs\n",
-        "            ),\n",
-        "        ],\n",
-        "        \"attachments\": [\n",
-        "            {\n",
-        "                \"type\": \"IMAGE_OVERLAY\",\n",
-        "                \"value\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\"\n",
-        "            },\n",
-        "            {\n",
-        "                \"type\": \"RAW_TEXT\",\n",
-        "                \"value\": \"IOWA, Zone 2232, June 2022 [Text string]\"\n",
-        "            },\n",
-        "            {\n",
-        "                \"type\": \"TEXT_URL\",\n",
-        "                \"value\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\"\n",
-        "            },\n",
-        "            {\n",
-        "                \"type\": \"IMAGE\",\n",
-        "                \"value\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\"\n",
-        "            },\n",
-        "            {\n",
-        "                \"type\": \"VIDEO\",\n",
-        "                \"value\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4\"\n",
-        "            },\n",
-        "            {\n",
-        "                \"type\": \"HTML\",\n",
-        "                \"value\": \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/windy.html\"\n",
-        "            },\n",
-        "            {\n",
-        "                \"type\": \"PDF_URL\",\n",
-        "                \"value\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n",
-        "            }\n",
-        "        ]\n",
-        "    })\n",
-        "\n",
-        "task1 = dataset.upsert_data_rows(uploads)\n",
-        "task1.wait_till_done()\n",
-        "print(\"ERRORS: \" , task1.errors)\n",
-        "print(\"RESULTS:\" , task1.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Create data rows from data in your local path "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "from PIL import Image\n",
-        "\n",
-        "# Create dummy empty jpeg file\n",
-        "width = 400\n",
-        "height = 300\n",
-        "color = (255, 255, 255)  # White color\n",
-        "image = Image.new(\"RGB\", (width, height), color)\n",
-        "\n",
-        "# Save the image as a JPEG file\n",
-        "image.save(\"dummy.jpg\")\n",
-        "\n",
-        "local_data_path = \"dummy.jpg\"\n",
-        "\n",
-        "data = {\n",
-        "  \"row_data\" : local_data_path,\n",
-        "  \"global_key\": str(uuid.uuid4())\n",
-        "}\n",
-        "\n",
-        "task3 = dataset.upsert_data_rows([data])\n",
-        "task3.wait_till_done()\n",
-        "print(\"ERRORS: \" , task3.errors)\n",
-        "print(\"RESULTS:\" , task3.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# You can mix local files with urls when creating data rows\n",
-        "task4 = dataset.upsert_data_rows([{\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_0009.jpeg\",\n",
-        "    \"global_key\": str(uuid.uuid4())\n",
-        "    }, {\n",
-        "    \"row_data\": local_data_path,\n",
-        "    \"global_key\": str(uuid.uuid4())\n",
-        "    }])\n",
-        "task4.wait_till_done()\n",
-        "print(\"ERRORS: \" , task4.errors)\n",
-        "print(\"RESULTS:\" , task4.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create data rows via `dataset.create_data_rows()`\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset_2 = client.create_dataset(name=\"data_rows_demo_dataset_3\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "uploads = []\n",
-        "# Generate data rows\n",
-        "for i in range(1,9):\n",
-        "    uploads.append({\n",
-        "        \"row_data\":  f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\": \"TEST-ID-%id\" % uuid.uuid1(),\n",
-        "        ## add metadata (optional)\n",
-        "        \"metadata_fields\": [\n",
-        "            lb.DataRowMetadataField(\n",
-        "                schema_id=mdo.reserved_by_name[\"tag\"].uid,  # specify the schema id\n",
-        "                value=\"tag_string\", # typed inputs\n",
-        "            ),\n",
-        "        ]\n",
-        "    })\n",
-        "\n",
-        "task1_2 = dataset_2.create_data_rows(uploads)\n",
-        "task1_2.wait_till_done()\n",
-        "print(\"ERRORS: \" , task1_2.errors)\n",
-        "print(\"RESULTS:\" , task1_2.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Update\n",
-        "`dataset.upsert_data_rows()` can also be use to update data rows\n",
-        "\n",
-        "To update data rows using this method, you need to pass a `key`, which can reference either a global key or a data row ID. Additionally, include any fields that you wish to update along with their new values.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Fetch a data row from the first dataset example\n",
-        "ts = dataset.export()\n",
-        "ts.wait_till_done()\n",
-        "DATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()][0]['data_row']['id']\n",
-        "GLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()][0]['data_row']['global_key']\n",
-        "\n",
-        "print(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Update the global key assodicated with the DATAROW_ID or GLOBAL_KEY, and include a additional metadata\n",
-        "data = {\n",
-        "    \"key\": lb.UniqueId(DATA_ROW_ID),\n",
-        "    \"global_key\": \"NEW-ID-%id\" % uuid.uuid1(),\n",
-        "    \"metadata_fields\": [\n",
-        "        # New metadata\n",
-        "        lb.DataRowMetadataField(\n",
-        "            schema_id=mdo.reserved_by_name['captureDateTime'].uid,\n",
-        "            value=\"2000-01-01 00:00:00\"\n",
-        "        ),\n",
-        "        # Include original metadata otherwise it will be removed\n",
-        "        lb.DataRowMetadataField(\n",
-        "            schema_id=mdo.reserved_by_name[\"tag\"].uid,\n",
-        "            value=\"tag_string\",\n",
-        "        ),\n",
-        "    ]\n",
-        "}\n",
-        "\n",
-        "task5 = dataset_2.upsert_data_rows([data])\n",
-        "task5.wait_till_done()\n",
-        "print(\"ERRORS: \" , task5.errors)\n",
-        "print(\"RESULTS:\" , task5.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a single attachment on an existing data row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# You can only create one attachment at the time.\n",
-        "DATA_ROW_ID = \"<DATA-ROW-ID>\"\n",
-        "data_row = client.get_data_row(DATA_ROW_ID)\n",
-        "attachment = data_row.create_attachment(attachment_type=\"RAW_TEXT\",\n",
-        "                           attachment_value=\"LABELERS WILL SEE THIS\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Update a recently created attachment "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "attachment.update(type= \"RAW_TEXT\", value=\"NEW RAW TEXT\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Delete"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Delete a single data row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "DATAROW_ID_TO_DELETE = \"<DATA-ROW-ID>\"\n",
-        "data_row = client.get_data_row(DATAROW_ID_TO_DELETE)\n",
-        "data_row.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Bulk delete data row objects"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Bulk delete a list of data_rows ( limit: 4K data rows per call)\n",
-        "lb.DataRow.bulk_delete(list(dataset.data_rows()))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Data rows are the assets that are being labeled. We currently support the following asset types:\n",
+                "    * Image\n",
+                "    * Text\n",
+                "    * Video\n",
+                "    * Geospatial / Tiled Imagery\n",
+                "    * Audio\n",
+                "    * Documents \n",
+                "    * HTML \n",
+                "    * DICOM \n",
+                "    * Conversational\n",
+                "* A data row cannot exist without belonging to a dataset.\n",
+                "* Data rows are added to labeling tasks by first attaching them to datasets and then creating batches in projects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox -q",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Get data rows from projects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Pick a project with batches that have data rows with global keys\nPROJECT_ID = \"<PROJECT-ID>\"\nproject = client.get_project(PROJECT_ID)\nbatches = list(project.batches())\nprint(batches)\n# This is the same as\n# -> dataset = client.get_dataset(dataset_id)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Fetch data rows from project's batches\n",
+                "\n",
+                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\n\nbatch_ids = [batch.uid for batch in batches]\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\":\n        batch_ids,  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Get single data row\ndata_row = data_rows[0]\nprint(data_row)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Get labels from the data row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "print(\"Associated label(s)\", data_row[\"projects\"][project.uid][\"labels\"])\nprint(\"Global key\", data_row[\"data_row\"][\"global_key\"])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Get data row ids by using global keys"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"<ENTER GLOBAL KEY>\"\ntask = client.get_data_row_ids_for_global_keys([global_key])\nprint(f\"Data row id: {task['results']}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create\n",
+                "We recommend the following methods to create data rows : `dataset.upsert_data_rows()`, and `dataset.create_data_rows()`, "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create data rows via `dataset.upsert_data_rows()`"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"data_rows_demo_dataset_6\")\n# You can also upload metadata along with your data row\nmdo = client.get_data_row_metadata_ontology()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 8):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n        \"attachments\": [\n            {\n                \"type\":\n                    \"IMAGE_OVERLAY\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\": \"RAW_TEXT\",\n                \"value\": \"IOWA, Zone 2232, June 2022 [Text string]\",\n            },\n            {\n                \"type\":\n                    \"TEXT_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n            },\n            {\n                \"type\":\n                    \"IMAGE\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\":\n                    \"VIDEO\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4\",\n            },\n            {\n                \"type\":\n                    \"HTML\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/windy.html\",\n            },\n            {\n                \"type\":\n                    \"PDF_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n            },\n        ],\n    })\n\ntask1 = dataset.upsert_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Create data rows from data in your local path "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "from PIL import Image\n\n# Create dummy empty jpeg file\nwidth = 400\nheight = 300\ncolor = (255, 255, 255)  # White color\nimage = Image.new(\"RGB\", (width, height), color)\n\n# Save the image as a JPEG file\nimage.save(\"dummy.jpg\")\n\nlocal_data_path = \"dummy.jpg\"\n\ndata = {\"row_data\": local_data_path, \"global_key\": str(uuid.uuid4())}\n\ntask3 = dataset.upsert_data_rows([data])\ntask3.wait_till_done()\nprint(\"ERRORS: \", task3.errors)\nprint(\"RESULTS:\", task3.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# You can mix local files with urls when creating data rows\ntask4 = dataset.upsert_data_rows([\n    {\n        \"row_data\":\n            \"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_0009.jpeg\",\n        \"global_key\":\n            str(uuid.uuid4()),\n    },\n    {\n        \"row_data\": local_data_path,\n        \"global_key\": str(uuid.uuid4())\n    },\n])\ntask4.wait_till_done()\nprint(\"ERRORS: \", task4.errors)\nprint(\"RESULTS:\", task4.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create data rows via `dataset.create_data_rows()`\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "dataset_2 = client.create_dataset(name=\"data_rows_demo_dataset_3\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n    })\n\ntask1_2 = dataset_2.create_data_rows(uploads)\ntask1_2.wait_till_done()\nprint(\"ERRORS: \", task1_2.errors)\nprint(\"RESULTS:\", task1_2.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Update\n",
+                "`dataset.upsert_data_rows()` can also be use to update data rows\n",
+                "\n",
+                "To update data rows using this method, you need to pass a `key`, which can reference either a global key or a data row ID. Additionally, include any fields that you wish to update along with their new values.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Update the global key assodicated with the DATAROW_ID or GLOBAL_KEY, and include a additional metadata\ndata = {\n    \"key\":\n        lb.UniqueId(DATA_ROW_ID),\n    \"global_key\":\n        \"NEW-ID-%id\" % uuid.uuid1(),\n    \"metadata_fields\": [\n        # New metadata\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"captureDateTime\"].uid,\n            value=\"2000-01-01 00:00:00\",\n        ),\n        # Include original metadata otherwise it will be removed\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"tag\"].uid,\n            value=\"tag_string\",\n        ),\n    ],\n}\n\ntask5 = dataset_2.upsert_data_rows([data])\ntask5.wait_till_done()\nprint(\"ERRORS: \", task5.errors)\nprint(\"RESULTS:\", task5.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a single attachment on an existing data row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# You can only create one attachment at the time.\nDATA_ROW_ID = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATA_ROW_ID)\nattachment = data_row.create_attachment(\n    attachment_type=\"RAW_TEXT\", attachment_value=\"LABELERS WILL SEE THIS\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Update a recently created attachment "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "attachment.update(type=\"RAW_TEXT\", value=\"NEW RAW TEXT\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Delete"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Delete a single data row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "DATAROW_ID_TO_DELETE = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATAROW_ID_TO_DELETE)\ndata_row.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Bulk delete data row objects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Bulk delete a list of data_rows ( limit: 4K data rows per call)\nlb.DataRow.bulk_delete(list(dataset.data_rows()))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/export_v1_migration_support.ipynb b/examples/basics/export_v1_migration_support.ipynb
index 3ad1062ea..12100fad1 100644
--- a/examples/basics/export_v1_migration_support.ipynb
+++ b/examples/basics/export_v1_migration_support.ipynb
@@ -1,1031 +1,593 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/Export_V1_to_V2_migration_support.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/Export_V1_to_V2_migration_support.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Export V1 to V2 migration "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "**Export V1 will no longer be available in any version of the SDK starting on April 2024**. We recommend users to plan accordingly. \n",
-        "\n",
-        "This notebook is designed to help users identify alternative V2 export methods that can serve as replacements for V1 methods."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Key changes included in export V2 methods ( ``export()`` and ``export_v2()``):\n",
-        "1. Added flexibility to only export the data that is needed. The new methods include parameters and filters to give you more granular control over your exports.\n",
-        "2. Added functionality to stream an **unlimited** number of data rows using ``export()`` (available on SDK >=3.56). Upgrading to `export()` is recommended as it is a more scalable solution.\n",
-        "\n",
-        "For complete details on how to use export V2 methods please see the [Export V2 methods](https://docs.labelbox.com/reference/label-export#export-v2-methods) documentation."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "###  Export V1 deprecated methods: \n",
-        "Project methods : \n",
-        "1. ```project.export_labels()```\n",
-        "2. ```project.label_generator()```\n",
-        "3. ```project.export_queued_data_rows()```\n",
-        "\n",
-        "Dataset methods: \n",
-        "1. ```dataset.export_data_rows()```\n",
-        "\n",
-        "Batch methods: \n",
-        "1. ```batch.export_data_rows()```\n",
-        "\n",
-        "Model methods :\n",
-        "1. ```model_run.export_labels()```\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Imports"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import pprint\n",
-        "pp = pprint.PrettyPrinter(width=30, compact=True)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## API Key and Client\n",
-        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)\n",
-        "client.enable_experimental = True ## This is required if using the export() streamable method"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Exports V1 to V2 guidelines\n",
-        "The follow sections will demonstrate how to use the export V2 methods to fetch data from your projects, datasets, batches and model runs. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export labels from a project\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "PROJECT_ID = \"\"\n",
-        "project = client.get_project(PROJECT_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Export V1 (deprecated)  \n",
-        "1. ```project.export_labels()``` \n",
-        "    - Parameters:  \n",
-        "        - ```download: bool = False```\n",
-        "        - ```timeout_seconds: int = 1800```\n",
-        "    - Output : (str | List[Dict[Any, Any]] | None)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "2. ```project.label_generator()```\n",
-        "    - Parameters: \n",
-        "        - ```timeout_seconds: int = 600```\n",
-        "    - Output: LabelGenerator"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Single entry from the output of project.label_generator() (deprecated)\n",
-        "# Label objects will not be deprecated.\n",
-        "single_output_from_generator = \"\"\"\n",
-        "\n",
-        "Label(\n",
-        "    uid='clrf5csho2ihx07ilffgp2fzj',\n",
-        "    data=ImageData(\n",
-        "        im_bytes=None,\n",
-        "        file_path=None,\n",
-        "        url='https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg',\n",
-        "        arr=None\n",
-        "    ),\n",
-        "    annotations=[\n",
-        "        ObjectAnnotation(\n",
-        "            confidence=None,\n",
-        "            name='bounding_box',\n",
-        "            feature_schema_id='clrf5ck4a0b9b071paa9ncu15',\n",
-        "            extra={\n",
-        "                'instanceURI': 'https://api.labelbox.com/masks/feature/clrf5csvi6ofm07lsf9pygwvi?token=<token>'\n",
-        "                'color': '#ff0000',\n",
-        "                'feature_id': 'clrf5csvi6ofm07lsf9pygwvi',\n",
-        "                'value': 'bounding_box',\n",
-        "                'page': None,\n",
-        "                'unit': None\n",
-        "            },\n",
-        "            value=Rectangle(\n",
-        "                extra={},\n",
-        "                start=Point(extra={}, x=2096.0, y=1264.0),\n",
-        "                end=Point(extra={}, x=2240.0, y=1689.0)\n",
-        "            ),\n",
-        "            classifications=[]\n",
-        "        ),\n",
-        "        # Add more annotations as needed\n",
-        "        # ...\n",
-        "    ],\n",
-        "    extra={\n",
-        "        'Created By': 'aovalle@labelbox.com',\n",
-        "        'Project Name': 'Image Annotation Import Demo',\n",
-        "        'Created At': '2024-01-15T16:35:59.000Z',\n",
-        "        'Updated At': '2024-01-15T16:51:56.000Z',\n",
-        "        'Seconds to Label': 66.0,\n",
-        "        'Agreement': -1.0,\n",
-        "        'Benchmark Agreement': -1.0,\n",
-        "        'Benchmark ID': None,\n",
-        "        'Dataset Name': 'image-demo-dataset',\n",
-        "        'Reviews': [],\n",
-        "        'View Label': 'https://editor.labelbox.com?project=clrf5ckex09m9070x1te223u5&label=clrf5csho2ihx07ilffgp2fzj',\n",
-        "        'Has Open Issues': 0.0,\n",
-        "        'Skipped': False,\n",
-        "        'media_type': 'image',\n",
-        "        'Data Split': None,\n",
-        "        'Global Key': '2560px-Kitano_Street_Kobe01s5s41102.jpeg'\n",
-        "    }\n",
-        ")\n",
-        "\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Export V2 \n",
-        "\n",
-        "For complete details on the supported filters abd parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-        "\n",
-        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-        "\n",
-        "    - Parameters:  \n",
-        "        - ```\"label_details\": True```\n",
-        "        - ```\"attachments\": True```\n",
-        "        - ```\"data_row_details\": True```\n",
-        "        - ```\"project_details\": True```\n",
-        "        - ```\"label_details\": True```\n",
-        "        - ```\"performance_details\": True```\n",
-        "    - Output: \n",
-        "       - ```ExportTask```\n",
-        "            - `ExportTask.has_result()` return type:  bool \n",
-        "            - `ExportTask.has_errors()` return type: bool\n",
-        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Set the export parameters to only export labels\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True\n",
-        "}\n",
-        "# You also have the option to include additional filtering to narrow down the list of labels\n",
-        "filters = {}\n",
-        "\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export queued (\"To Label\") data rows from a project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Export V1 (deprecated):  \n",
-        "1. ``project.export_queued_data_rows()`` :\n",
-        "    - Parameters: \n",
-        "        - ``timeout_seconds: int = 120``\n",
-        "        - ``include_metadata: bool = False``\n",
-        "    - Output: List[Dict[str, str]]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Single entry from the output of project.export_queued_data_rows() (deprecated)\n",
-        "single_output_example = \"\"\"\n",
-        "[\n",
-        "  {'id': 'clpouak6nap2g0783ajd1d6pf',\n",
-        " 'createdAt': '2023-12-03T02:04:34.062Z',\n",
-        " 'updatedAt': '2023-12-03T02:05:33.797Z',\n",
-        " 'externalId': None,\n",
-        " 'globalKey': 'b57c9ab2-304f-4c17-ba5f-c536f39a6a46',\n",
-        " 'metadataFields': [],\n",
-        " 'customMetadata': [],\n",
-        " 'rowData': 'https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg',\n",
-        " 'mediaAttributes': {'assetType': 'image',\n",
-        "  'contentLength': 305973,\n",
-        "  'height': 1333,\n",
-        "  'mimeType': 'image/jpeg',\n",
-        "  'subType': 'jpeg',\n",
-        "  'superType': 'image',\n",
-        "  'width': 2000}}\n",
-        "]\n",
-        "\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Export V2\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-        "\n",
-        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-        "\n",
-        "    - Parameters (Minimum required parameters):  \n",
-        "      - ```\"data_row_details\": True```\n",
-        "      - ```\"project_details\": True```\n",
-        "    - Required filters: \n",
-        "      - ``` \"workflow_status\": \"ToLabel\"```\n",
-        "    - Output: \n",
-        "       - ```ExportTask```\n",
-        "            - `ExportTask.has_result()` return type:  bool \n",
-        "            - `ExportTask.has_errors()` return type: bool\n",
-        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params= {\n",
-        " \"attachments\": True, # Set to true if you want to export attachments\n",
-        "  \"metadata_fields\": True, # Set to true if you want to export metadata\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True\n",
-        "}\n",
-        "filters = {\n",
-        "  \"workflow_status\": \"ToLabel\" ## Using this filter will only export queued data rows\n",
-        "}\n",
-        "\n",
-        "# An ExportTask is returned, this provides additional information about the status of your task, such as\n",
-        "# any errors encountered and includes additional methods to stream your data\n",
-        "\n",
-        "\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a Dataset "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "DATASET_ID = \"\"\n",
-        "dataset = client.get_dataset(DATASET_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V1 (deprecated): \n",
-        "\n",
-        "1. ```dataset.export_data_rows()```\n",
-        "  - Parameters:  \n",
-        "    - ``timeout_seconds=120``\n",
-        "    - ``include_metadata: bool = True``\n",
-        "  - Output: \n",
-        "    - Data row object generator\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Single entry from the output of dataset.export_data_rows() (deprecated)\n",
-        "# Data row objects will not be deprecated.\n",
-        "\n",
-        "single_output_from_data_row_generator = \"\"\"\n",
-        "<DataRow {\n",
-        "    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n",
-        "    \"external_id\": null,\n",
-        "    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n",
-        "    \"media_attributes\": {\n",
-        "        \"assetType\": \"image\",\n",
-        "        \"contentLength\": 305973,\n",
-        "        \"height\": 1333,\n",
-        "        \"mimeType\": \"image/jpeg\",\n",
-        "        \"subType\": \"jpeg\",\n",
-        "        \"superType\": \"image\",\n",
-        "        \"width\": 2000\n",
-        "    },\n",
-        "    \"metadata\": [],\n",
-        "    \"metadata_fields\": [],\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n",
-        "    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n",
-        "    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n",
-        "}>\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-        "\n",
-        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-        "\n",
-        "    - Parameters (minimum required parameters):  \n",
-        "      - ``\"data_row_details\": True``\n",
-        "    - Output: \n",
-        "       - ```ExportTask```\n",
-        "            - `ExportTask.has_result()` return type:  bool \n",
-        "            - `ExportTask.has_errors()` return type: bool\n",
-        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params = {\n",
-        " \"attachments\": True, # Set to true if you want to export attachments\n",
-        "  \"metadata_fields\": True, # Set to true if you want to export metadata\n",
-        "  \"data_row_details\": True,\n",
-        "}\n",
-        "filters = {}\n",
-        "\n",
-        "# A task is returned, this provides additional information about the status of your task, such as\n",
-        "# any errors encountered\n",
-        "export_task = dataset.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a batch"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V1 (deprecated): \n",
-        "1. ```batch.export_data_rows()```\n",
-        "  - Parameters:  \n",
-        "    - ``timeout_seconds=120``\n",
-        "    - ``include_metadata: bool = True``\n",
-        "  - Output: \n",
-        "    - Data row object generator"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Single output from batch.export_data_rows() method (deprecated)\n",
-        "# Data row objects will not be deprecated\n",
-        "\n",
-        "single_output_from_data_row_generator = \"\"\"\n",
-        "<DataRow {\n",
-        "    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n",
-        "    \"external_id\": null,\n",
-        "    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n",
-        "    \"media_attributes\": {\n",
-        "        \"assetType\": \"image\",\n",
-        "        \"contentLength\": 305973,\n",
-        "        \"height\": 1333,\n",
-        "        \"mimeType\": \"image/jpeg\",\n",
-        "        \"subType\": \"jpeg\",\n",
-        "        \"superType\": \"image\",\n",
-        "        \"width\": 2000\n",
-        "    },\n",
-        "    \"metadata\": [],\n",
-        "    \"metadata_fields\": [],\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n",
-        "    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n",
-        "    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n",
-        "}>\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-        "\n",
-        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-        "\n",
-        "    - Required parameters:  \n",
-        "      - ```\"data_row_details\": True```,\n",
-        "      - ```\"batch_ids\": [<batch_id>] ```\n",
-        "    - Output: \n",
-        "       - ```ExportTask```\n",
-        "            - `ExportTask.has_result()` return type:  bool \n",
-        "            - `ExportTask.has_errors()` return type: bool\n",
-        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Find the batch ID by navigating to \"Batches\" -->  \"Manage batches\" --> \"Copy Batch ID\"\n",
-        "BATCH_ID = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params = {\n",
-        " \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"batch_ids\" : [BATCH_ID] # Include batch ids if you only want to export specific batches, otherwise,\n",
-        "  #you can export all the data without using this parameter\n",
-        "}\n",
-        "filters = {}\n",
-        "\n",
-        "# A task is returned, this provides additional information about the status of your task, such as\n",
-        "# any errors encountered\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a Model "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V1 (deprecated): \n",
-        "1. ```model_run.export_labels(downlaod=True)```\n",
-        "    - Parameters:  \n",
-        "        - ```download: bool = False```\n",
-        "        - ```timeout_seconds: int = 1800```\n",
-        "    - Output : (str | List[Dict[Any, Any]] | None)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Single output from model_run.export_labels()\n",
-        "single_output_example = \"\"\"\n",
-        "[\n",
-        "   {'ID': '1c48a7a0-3016-48e0-b0e3-47430f974869',\n",
-        "   'Data Split': 'training',\n",
-        "   'DataRow ID': 'clpqdyf650xd40712pycshy6a',\n",
-        "   'External ID': './resume/BANKING/99124477.pdf',\n",
-        "   'Labeled Data': 'https://storage.labelbox.com/cl5bn8qvq1av907xtb3bp8q60%2F8c6afc38-42a4-b2e1-a2e3-1e3b0c2998fc-99124477.pdf?Expires=1706637969726&KeyName=labelbox-assets-key-3&Signature=2nVt3sJ21CbjGS9I64yFquUELRw',\n",
-        "   'Media Attributes': {'assetType': 'pdf',\n",
-        "      'contentLength': 42535,\n",
-        "      'mimeType': 'application/pdf',\n",
-        "      'pageCount': 3,\n",
-        "      'subType': 'pdf',\n",
-        "      'superType': 'application'},\n",
-        "   'Label': {'objects': [{'featureId': 'b9f3b584-0f45-050a-88d4-39c2a169c8e1',\n",
-        "      'schemaId': 'clq1ckwbd08jp07z91q9mch5j',\n",
-        "      'title': 'Test',\n",
-        "      'value': 'test',\n",
-        "      'color': '#1CE6FF',\n",
-        "      'data': {'location': [{'text-bbox': {'page': 1,\n",
-        "            'top': 158.44,\n",
-        "            'left': 58.765,\n",
-        "            'height': 13.691,\n",
-        "            'width': 78.261}}],\n",
-        "         'unit': 'POINTS'}}],\n",
-        "      'classifications': [],\n",
-        "      'relationships': []}}\n",
-        "   ]\n",
-        "   \"\"\"\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-        "\n",
-        "1. ```model_run.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-        "\n",
-        "    - Required parameters:  \n",
-        "      - ```\"data_row_details\": True```\n",
-        "      - ```\"project_details\": True```\n",
-        "      - ```\"label_details\": True```\n",
-        "    - Required filters: \n",
-        "      - N/A -> Filters not supported\n",
-        "    - Output: \n",
-        "       - ```ExportTask```\n",
-        "            - `ExportTask.has_result()` return type:  bool \n",
-        "            - `ExportTask.has_errors()` return type: bool\n",
-        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "MODEL_RUN_ID = \"\"\n",
-        "model_run = client.get_model_run(MODEL_RUN_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params = {\n",
-        " \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"performance_details\": True\n",
-        "}\n",
-        "\n",
-        "export_task = model_run.export(params=export_params)\n",
-        "export_task.wait_till_done()\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a video project\n",
-        "Video projects include additional fields. Please refer to the example below to extract specific fields from video exports.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Export V1 (deprecated)  \n",
-        "1. ```project.export_labels()``` \n",
-        "    - Parameters:  \n",
-        "        - ```download: bool = False```\n",
-        "        - ```timeout_seconds: int = 1800```\n",
-        "    - Output : (str | List[Dict[Any, Any]] | None)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "2. ```project.video_label_generator()```\n",
-        "    - Parameters: \n",
-        "        - ```timeout_seconds: int = 600```\n",
-        "    - Output: LabelGenerator"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Export V2\n",
-        "\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-        "\n",
-        "1. ```project.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-        "\n",
-        "    - Required parameters:  \n",
-        "      - ```\"attachments\": True```\n",
-        "      - ```\"data_row_details\": True```\n",
-        "      - ```\"project_details\": True```\n",
-        "      - ```\"label_details\": True```\n",
-        "      - ```\"performance_details\": True```\n",
-        "    - Output: \n",
-        "       - ```ExportTask```\n",
-        "            - `ExportTask.has_result()` return type:  bool \n",
-        "            - `ExportTask.has_errors()` return type: bool\n",
-        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "VIDEO_PROJECT_ID = \"\"\n",
-        "project = client.get_project(VIDEO_PROJECT_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params = {\n",
-        " \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"interpolated_frames\": True # For additional information on interpolated frames please visit our documentation https://docs.labelbox.com/docs/video-annotations#video-editor-components\n",
-        "}\n",
-        "filters = {}\n",
-        "\n",
-        "# A task is returned, this provides additional information about the status of your task, such as\n",
-        "# any errors encountered\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Fetch frame specific objects and frame or global classifications"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import json\n",
-        "import pprint as pp  # Assuming pp is imported from pprint module\n",
-        "\n",
-        "frames_objects_class_list = []\n",
-        "global_class_list = []\n",
-        "\n",
-        "stream = export_task.get_stream()\n",
-        "for output in stream:\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n",
-        "        frames_data = dr[\"annotations\"][\"frames\"]\n",
-        "        for k, v in frames_data.items():\n",
-        "            frames_objects_class_list.append({k: v})\n",
-        "        global_class_list.extend(dr[\"annotations\"][\"classifications\"])\n",
-        "\n",
-        "    print(\"------- Frame specific classifications and objects -------\")\n",
-        "    pp.pprint(frames_objects_class_list)\n",
-        "\n",
-        "    print(\"------ Global classifications -------\")\n",
-        "    pp.pprint(global_class_list)\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Fetch key frame feature map "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "keyframe_map = []\n",
-        "\n",
-        "stream = export_task.get_stream()\n",
-        "for output in stream:\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n",
-        "    for label in labels:\n",
-        "        annotations = label[\"annotations\"][\"key_frame_feature_map\"]\n",
-        "        for key, value in annotations.items():\n",
-        "            keyframe_map.append({key: value})\n",
-        "\n",
-        "print(\"----- Keyframe Feature Map -----\")\n",
-        "pp.pprint(keyframe_map)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Fetch segments "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "segments_map = []\n",
-        "stream = export_task.get_stream()\n",
-        "for output in stream:\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n",
-        "    for label in labels:\n",
-        "        annotations = label[\"annotations\"][\"segments\"]\n",
-        "        for key, value in annotations.items():\n",
-        "            segments_map.append({key: value})\n",
-        "\n",
-        "print(\"----- Segments Feature Map -----\")\n",
-        "pp.pprint(segments_map)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_migration_support.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_migration_support.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Export V1 to V2 migration "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "**Export V1 will no longer be available in any version of the SDK starting on April 2024**. We recommend users to plan accordingly. \n",
+                "\n",
+                "This notebook is designed to help users identify alternative V2 export methods that can serve as replacements for V1 methods."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Key changes included in export V2 methods ( ``export()`` and ``export_v2()``):\n",
+                "1. Added flexibility to only export the data that is needed. The new methods include parameters and filters to give you more granular control over your exports.\n",
+                "2. Added functionality to stream an **unlimited** number of data rows using ``export()`` (available on SDK >=3.56). Upgrading to `export()` is recommended as it is a more scalable solution.\n",
+                "\n",
+                "For complete details on how to use export V2 methods please see the [Export V2 methods](https://docs.labelbox.com/reference/label-export#export-v2-methods) documentation."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "###  Export V1 deprecated methods: \n",
+                "Project methods : \n",
+                "1. ```project.export_labels()```\n",
+                "2. ```project.label_generator()```\n",
+                "3. ```project.export_queued_data_rows()```\n",
+                "\n",
+                "Dataset methods: \n",
+                "1. ```dataset.export_data_rows()```\n",
+                "\n",
+                "Batch methods: \n",
+                "1. ```batch.export_data_rows()```\n",
+                "\n",
+                "Model methods :\n",
+                "1. ```model_run.export_labels()```\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Imports"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport pprint\n\npp = pprint.PrettyPrinter(width=30, compact=True)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API Key and Client\n",
+                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Exports V1 to V2 guidelines\n",
+                "The follow sections will demonstrate how to use the export V2 methods to fetch data from your projects, datasets, batches and model runs. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export labels from a project\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "PROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Export V1 (deprecated)  \n",
+                "1. ```project.export_labels()``` \n",
+                "    - Parameters:  \n",
+                "        - ```download: bool = False```\n",
+                "        - ```timeout_seconds: int = 1800```\n",
+                "    - Output : (str | List[Dict[Any, Any]] | None)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "2. ```project.label_generator()```\n",
+                "    - Parameters: \n",
+                "        - ```timeout_seconds: int = 600```\n",
+                "    - Output: LabelGenerator"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Single entry from the output of project.label_generator() (deprecated)\n# Label objects will not be deprecated.\nsingle_output_from_generator = \"\"\"\n\nLabel(\n    uid='clrf5csho2ihx07ilffgp2fzj',\n    data=ImageData(\n        im_bytes=None,\n        file_path=None,\n        url='https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg',\n        arr=None\n    ),\n    annotations=[\n        ObjectAnnotation(\n            confidence=None,\n            name='bounding_box',\n            feature_schema_id='clrf5ck4a0b9b071paa9ncu15',\n            extra={\n                'instanceURI': 'https://api.labelbox.com/masks/feature/clrf5csvi6ofm07lsf9pygwvi?token=<token>'\n                'color': '#ff0000',\n                'feature_id': 'clrf5csvi6ofm07lsf9pygwvi',\n                'value': 'bounding_box',\n                'page': None,\n                'unit': None\n            },\n            value=Rectangle(\n                extra={},\n                start=Point(extra={}, x=2096.0, y=1264.0),\n                end=Point(extra={}, x=2240.0, y=1689.0)\n            ),\n            classifications=[]\n        ),\n        # Add more annotations as needed\n        # ...\n    ],\n    extra={\n        'Created By': 'aovalle@labelbox.com',\n        'Project Name': 'Image Annotation Import Demo',\n        'Created At': '2024-01-15T16:35:59.000Z',\n        'Updated At': '2024-01-15T16:51:56.000Z',\n        'Seconds to Label': 66.0,\n        'Agreement': -1.0,\n        'Benchmark Agreement': -1.0,\n        'Benchmark ID': None,\n        'Dataset Name': 'image-demo-dataset',\n        'Reviews': [],\n        'View Label': 'https://editor.labelbox.com?project=clrf5ckex09m9070x1te223u5&label=clrf5csho2ihx07ilffgp2fzj',\n        'Has Open Issues': 0.0,\n        'Skipped': False,\n        'media_type': 'image',\n        'Data Split': None,\n        'Global Key': '2560px-Kitano_Street_Kobe01s5s41102.jpeg'\n    }\n)\n\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Export V2 \n",
+                "\n",
+                "For complete details on the supported filters abd parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+                "\n",
+                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+                "\n",
+                "    - Parameters:  \n",
+                "        - ```\"label_details\": True```\n",
+                "        - ```\"attachments\": True```\n",
+                "        - ```\"data_row_details\": True```\n",
+                "        - ```\"project_details\": True```\n",
+                "        - ```\"label_details\": True```\n",
+                "        - ```\"performance_details\": True```\n",
+                "    - Output: \n",
+                "       - ```ExportTask```\n",
+                "            - `ExportTask.has_result()` return type:  bool \n",
+                "            - `ExportTask.has_errors()` return type: bool\n",
+                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Set the export parameters to only export labels\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n}\n# You also have the option to include additional filtering to narrow down the list of labels\nfilters = {}\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export queued (\"To Label\") data rows from a project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Export V1 (deprecated):  \n",
+                "1. ``project.export_queued_data_rows()`` :\n",
+                "    - Parameters: \n",
+                "        - ``timeout_seconds: int = 120``\n",
+                "        - ``include_metadata: bool = False``\n",
+                "    - Output: List[Dict[str, str]]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Single entry from the output of project.export_queued_data_rows() (deprecated)\nsingle_output_example = \"\"\"\n[\n  {'id': 'clpouak6nap2g0783ajd1d6pf',\n 'createdAt': '2023-12-03T02:04:34.062Z',\n 'updatedAt': '2023-12-03T02:05:33.797Z',\n 'externalId': None,\n 'globalKey': 'b57c9ab2-304f-4c17-ba5f-c536f39a6a46',\n 'metadataFields': [],\n 'customMetadata': [],\n 'rowData': 'https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg',\n 'mediaAttributes': {'assetType': 'image',\n  'contentLength': 305973,\n  'height': 1333,\n  'mimeType': 'image/jpeg',\n  'subType': 'jpeg',\n  'superType': 'image',\n  'width': 2000}}\n]\n\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Export V2\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+                "\n",
+                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+                "\n",
+                "    - Parameters (Minimum required parameters):  \n",
+                "      - ```\"data_row_details\": True```\n",
+                "      - ```\"project_details\": True```\n",
+                "    - Required filters: \n",
+                "      - ``` \"workflow_status\": \"ToLabel\"```\n",
+                "    - Output: \n",
+                "       - ```ExportTask```\n",
+                "            - `ExportTask.has_result()` return type:  bool \n",
+                "            - `ExportTask.has_errors()` return type: bool\n",
+                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n    \"project_details\": True,\n}\nfilters = {\n    \"workflow_status\":\n        \"ToLabel\"  ## Using this filter will only export queued data rows\n}\n\n# An ExportTask is returned, this provides additional information about the status of your task, such as\n# any errors encountered and includes additional methods to stream your data\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a Dataset "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "DATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V1 (deprecated): \n",
+                "\n",
+                "1. ```dataset.export_data_rows()```\n",
+                "  - Parameters:  \n",
+                "    - ``timeout_seconds=120``\n",
+                "    - ``include_metadata: bool = True``\n",
+                "  - Output: \n",
+                "    - Data row object generator\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Single entry from the output of dataset.export_data_rows() (deprecated)\n# Data row objects will not be deprecated.\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+                "\n",
+                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+                "\n",
+                "    - Parameters (minimum required parameters):  \n",
+                "      - ``\"data_row_details\": True``\n",
+                "    - Output: \n",
+                "       - ```ExportTask```\n",
+                "            - `ExportTask.has_result()` return type:  bool \n",
+                "            - `ExportTask.has_errors()` return type: bool\n",
+                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a batch"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V1 (deprecated): \n",
+                "1. ```batch.export_data_rows()```\n",
+                "  - Parameters:  \n",
+                "    - ``timeout_seconds=120``\n",
+                "    - ``include_metadata: bool = True``\n",
+                "  - Output: \n",
+                "    - Data row object generator"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Single output from batch.export_data_rows() method (deprecated)\n# Data row objects will not be deprecated\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+                "\n",
+                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+                "\n",
+                "    - Required parameters:  \n",
+                "      - ```\"data_row_details\": True```,\n",
+                "      - ```\"batch_ids\": [<batch_id>] ```\n",
+                "    - Output: \n",
+                "       - ```ExportTask```\n",
+                "            - `ExportTask.has_result()` return type:  bool \n",
+                "            - `ExportTask.has_errors()` return type: bool\n",
+                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Find the batch ID by navigating to \"Batches\" -->  \"Manage batches\" --> \"Copy Batch ID\"\nBATCH_ID = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        BATCH_ID\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a Model "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V1 (deprecated): \n",
+                "1. ```model_run.export_labels(downlaod=True)```\n",
+                "    - Parameters:  \n",
+                "        - ```download: bool = False```\n",
+                "        - ```timeout_seconds: int = 1800```\n",
+                "    - Output : (str | List[Dict[Any, Any]] | None)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Single output from model_run.export_labels()\nsingle_output_example = \"\"\"\n[\n   {'ID': '1c48a7a0-3016-48e0-b0e3-47430f974869',\n   'Data Split': 'training',\n   'DataRow ID': 'clpqdyf650xd40712pycshy6a',\n   'External ID': './resume/BANKING/99124477.pdf',\n   'Labeled Data': 'https://storage.labelbox.com/cl5bn8qvq1av907xtb3bp8q60%2F8c6afc38-42a4-b2e1-a2e3-1e3b0c2998fc-99124477.pdf?Expires=1706637969726&KeyName=labelbox-assets-key-3&Signature=2nVt3sJ21CbjGS9I64yFquUELRw',\n   'Media Attributes': {'assetType': 'pdf',\n      'contentLength': 42535,\n      'mimeType': 'application/pdf',\n      'pageCount': 3,\n      'subType': 'pdf',\n      'superType': 'application'},\n   'Label': {'objects': [{'featureId': 'b9f3b584-0f45-050a-88d4-39c2a169c8e1',\n      'schemaId': 'clq1ckwbd08jp07z91q9mch5j',\n      'title': 'Test',\n      'value': 'test',\n      'color': '#1CE6FF',\n      'data': {'location': [{'text-bbox': {'page': 1,\n            'top': 158.44,\n            'left': 58.765,\n            'height': 13.691,\n            'width': 78.261}}],\n         'unit': 'POINTS'}}],\n      'classifications': [],\n      'relationships': []}}\n   ]\n   \"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+                "\n",
+                "1. ```model_run.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+                "\n",
+                "    - Required parameters:  \n",
+                "      - ```\"data_row_details\": True```\n",
+                "      - ```\"project_details\": True```\n",
+                "      - ```\"label_details\": True```\n",
+                "    - Required filters: \n",
+                "      - N/A -> Filters not supported\n",
+                "    - Output: \n",
+                "       - ```ExportTask```\n",
+                "            - `ExportTask.has_result()` return type:  bool \n",
+                "            - `ExportTask.has_errors()` return type: bool\n",
+                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "MODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n}\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a video project\n",
+                "Video projects include additional fields. Please refer to the example below to extract specific fields from video exports.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Export V1 (deprecated)  \n",
+                "1. ```project.export_labels()``` \n",
+                "    - Parameters:  \n",
+                "        - ```download: bool = False```\n",
+                "        - ```timeout_seconds: int = 1800```\n",
+                "    - Output : (str | List[Dict[Any, Any]] | None)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "2. ```project.video_label_generator()```\n",
+                "    - Parameters: \n",
+                "        - ```timeout_seconds: int = 600```\n",
+                "    - Output: LabelGenerator"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### Export V2\n",
+                "\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+                "\n",
+                "1. ```project.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+                "\n",
+                "    - Required parameters:  \n",
+                "      - ```\"attachments\": True```\n",
+                "      - ```\"data_row_details\": True```\n",
+                "      - ```\"project_details\": True```\n",
+                "      - ```\"label_details\": True```\n",
+                "      - ```\"performance_details\": True```\n",
+                "    - Output: \n",
+                "       - ```ExportTask```\n",
+                "            - `ExportTask.has_result()` return type:  bool \n",
+                "            - `ExportTask.has_errors()` return type: bool\n",
+                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "VIDEO_PROJECT_ID = \"\"\nproject = client.get_project(VIDEO_PROJECT_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"label_details\": True,\n    \"interpolated_frames\":\n        True,  # For additional information on interpolated frames please visit our documentation https://docs.labelbox.com/docs/video-annotations#video-editor-components\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Fetch frame specific objects and frame or global classifications"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import json\nimport pprint as pp  # Assuming pp is imported from pprint module\n\nframes_objects_class_list = []\nglobal_class_list = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n        for k, v in frames_data.items():\n            frames_objects_class_list.append({k: v})\n        global_class_list.extend(dr[\"annotations\"][\"classifications\"])\n\n    print(\"------- Frame specific classifications and objects -------\")\n    pp.pprint(frames_objects_class_list)\n\n    print(\"------ Global classifications -------\")\n    pp.pprint(global_class_list)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Fetch key frame feature map "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "keyframe_map = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"key_frame_feature_map\"]\n        for key, value in annotations.items():\n            keyframe_map.append({key: value})\n\nprint(\"----- Keyframe Feature Map -----\")\npp.pprint(keyframe_map)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Fetch segments "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "segments_map = []\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"segments\"]\n        for key, value in annotations.items():\n            segments_map.append({key: value})\n\nprint(\"----- Segments Feature Map -----\")\npp.pprint(segments_map)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/ontologies.ipynb b/examples/basics/ontologies.ipynb
index c4ed8806e..6937bbe9c 100644
--- a/examples/basics/ontologies.ipynb
+++ b/examples/basics/ontologies.ipynb
@@ -1,459 +1,304 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Ontologies\n",
-        "* An ontology is a collection different tools and classifications that can be used within a project's editor. Each tool or classification is called a \"Feature Schema\". \n",
-        "* Feature Schemas contain information about the tool such as the kind, the name, all subclasses, and other information related to a tool. Feature Schemas can be shared between ontologies. \n",
-        "\n",
-        "* Helpful Links:\n",
-        "    * [Ontology documentation](https://docs.labelbox.com/docs/labelbox-ontology)\n",
-        "    * [Project Setup Using Ontologies](https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/project_setup.ipynb)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox -q"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import json"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Ontology From Normalized Data\n",
-        "* Users can create ontologies from a json definition of the ontology.\n",
-        "* See below `OntologyBuilder` section for more details on constructing the normalized ontology.\n",
-        "* Each tool type requires a specific value be passed:\n",
-        "\n",
-        "| Tool    | Value |\n",
-        "| :----------- | :----------- |\n",
-        "| Bounding box      | rectangle    |\n",
-        "| Polygon           | polygon      |\n",
-        "| Polyline          | line         |\n",
-        "| Point             | point        |\n",
-        "| Segmentation mask | raster-segmentation  |\n",
-        "| Entity            | named-entity |\n",
-        "| Relationship      | edge         |"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# This will automatically create new feature schema\n",
-        "ontology_name = \"sdk-ontology\"\n",
-        "feature_schema_cat_normalized = {\n",
-        "    'tool': 'polygon',\n",
-        "    'name': 'cat',\n",
-        "    'color': 'black'\n",
-        "}\n",
-        "\n",
-        "ontology_normalized_json = {\n",
-        "    \"tools\": [feature_schema_cat_normalized],\n",
-        "    \"classifications\": []\n",
-        "}\n",
-        "ontology = client.create_ontology(name=ontology_name,\n",
-        "                                  normalized=ontology_normalized_json)\n",
-        "print(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Ontology From Existing Feature Schemas\n",
-        "* It is often useful to support the same features in multiple ontologies. \n",
-        "* Labelbox supports this workflow by allowing users to create ontologies using existing feature schemas."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# First create the feature schema\n",
-        "feature_schema_cat = client.create_feature_schema(feature_schema_cat_normalized)\n",
-        "# When we create the ontology it will not re-create the feature schema\n",
-        "print(feature_schema_cat.uid)\n",
-        "ontology = client.create_ontology_from_feature_schemas(ontology_name,\n",
-        "                                                       [feature_schema_cat.uid])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Ontology From a Mix of New and Existing Feature Schemas\n",
-        "* If we want to create a new ontology that expands upon a previous ontology it is helpful to be able to share a portion of the features.\n",
-        "* To do this we will create the new schema ids that we want. Then we will create an ontology from the new list of ids.\n",
-        "* Note that for additional customization you can also combine the normalized json and use the create_ontology() method (not covered here)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create new dog schema id\n",
-        "feature_schema_dog_normalized = {\n",
-        "    'tool': 'polygon',\n",
-        "    'name': 'dog',\n",
-        "    'color': 'black',\n",
-        "    'classifications': [],\n",
-        "}\n",
-        "feature_schema_dog = client.create_feature_schema(feature_schema_dog_normalized)\n",
-        "# The cat is shared between this new ontology and the one we created previously\n",
-        "# (ie. the cat feature schema will not be re-created)\n",
-        "ontology = client.create_ontology_from_feature_schemas(\n",
-        "    ontology_name, [feature_schema_cat.uid, feature_schema_dog.uid])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Read\n",
-        "* We can directly query by id for ontologies and feature schemas\n",
-        "* We also can search for both by name"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Fetch by ID\n",
-        "feature_schema = client.get_feature_schema(feature_schema_cat.uid)\n",
-        "ontology = client.get_ontology(ontology.uid)\n",
-        "print(feature_schema)\n",
-        "print(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Search by name\n",
-        "feature_schema = next(client.get_feature_schemas(\"cat\"))\n",
-        "ontology = next(client.get_ontologies(ontology_name))\n",
-        "print(feature_schema)\n",
-        "print(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Update and Delete"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Check if feature is archived\n",
-        "feature_schema = next(client.get_feature_schemas(\"cat\"))\n",
-        "client.is_feature_schema_archived(ontology_id=ontology.uid, feature_schema_id=feature_schema.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Update a feature's title\n",
-        "client.update_feature_schema_title(feature_schema_id=feature_schema.uid, title=\"cat-2\")\n",
-        "feature = client.get_feature_schema(feature_schema_id=feature_schema.uid)\n",
-        "print(\"Feature: \", feature)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace a feature\n",
-        "tool = lb.Tool(feature_schema_id=feature_schema.uid, name=\"tool-cat-upserted\", tool=lb.Tool.Type.BBOX, color=\"#FF0000\")\n",
-        "upserted_feature_schema_id = client.upsert_feature_schema(tool.asdict()).uid\n",
-        "feature = client.get_feature_schema(feature_schema_id=upserted_feature_schema_id)\n",
-        "print(\"Updated feature: \", feature)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert a new feature\n",
-        "tool = lb.Tool(name=\"tool-cat-2\", tool=lb.Tool.Type.RASTER_SEGMENTATION)\n",
-        "feature_schema_id_new = client.create_feature_schema(tool.asdict()).uid\n",
-        "client.insert_feature_schema_into_ontology(feature_schema_id=feature_schema_id_new, ontology_id=ontology.uid , position=2)\n",
-        "print(\"Updated ontology: \", client.get_ontology(ontology_id=ontology.uid))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Delete or Archived a feature:\n",
-        "\n",
-        "If the feature schema is a root level node with associated labels, it will be archived.\n",
-        "If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
-        "If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client.delete_feature_schema_from_ontology(ontology_id=ontology.uid, feature_schema_id=feature_schema_id_new)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Only features with annotations will be archived, features without annotations will be deleted.\n",
-        "feature_schema_id_with_annotations = \"\"\n",
-        "ontology_id = \"\"\n",
-        "client.unarchive_feature_schema_node(ontology_id=ontology_id, root_feature_schema_id=feature_schema_id_with_annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Ontology Builder\n",
-        "* The ontology builder is a tool for creating and modifying normalized json"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create normalized json with a bounding box and segmentation tool\n",
-        "ontology_builder = lb.OntologyBuilder(tools=[\n",
-        "    lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n",
-        "    lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\"),\n",
-        "])\n",
-        "# Creating an ontology from this is easy\n",
-        "ontology = client.create_ontology(\"ontology-builder-ontology\",\n",
-        "                                  ontology_builder.asdict())\n",
-        "print(json.dumps(ontology.normalized, indent=2))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Alternative syntax for defining the ontology via the OntologyBuilder"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create\n",
-        "ontology_builder = lb.OntologyBuilder()\n",
-        "# Append tools\n",
-        "tool_dog = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\")\n",
-        "tool_cat = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\")\n",
-        "ontology_builder.add_tool(tool_dog)\n",
-        "ontology_builder.add_tool(tool_cat)\n",
-        "ontology = client.create_ontology(\"ontology-builder-ontology\",\n",
-        "                                  ontology_builder.asdict())\n",
-        "print(json.dumps(ontology.normalized, indent=2))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Classifications are supported too (Both for top level and as subclassifications)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION,\n",
-        "             name=\"cat\",\n",
-        "             classifications=[\n",
-        "                 lb.Classification(class_type=lb.Classification.Type.TEXT,\n",
-        "                                name=\"name\")\n",
-        "             ])\n",
-        "    ],\n",
-        "    classifications=[\n",
-        "        lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
-        "                       name=\"image_quality\",\n",
-        "                       options=[lb.Option(value=\"clear\"),\n",
-        "                                lb.Option(value=\"blurry\")])\n",
-        "    ])\n",
-        "print(json.dumps(ontology_builder.asdict(), indent=2))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Example of how to add sub-classfication within an option"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder()\n",
-        "radio_classification = lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
-        "                                      name=\"Global classification\",\n",
-        "                                      options=[lb.Option(\"1st option\", options=[lb.Classification(class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                                                                                            name=\"Inside 1st option\",\n",
-        "                                                                                            options=[lb.Option(\"Option A\"), lb.Option(\"Option B\")])]), lb.Option(\"2nd option\", options=[lb.Classification(class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                                                                                                                                                                                              name=\"Inside 2nd option\",\n",
-        "                                                                                                                                                                                              options=[lb.Option(\"Option A\"), lb.Option(\"Option B\")])])])\n",
-        "\n",
-        "ontology_builder.add_classification(radio_classification)\n",
-        "\n",
-        "ontology = client.create_ontology(\"example of nested classification\",\n",
-        "                                  ontology_builder.asdict())\n",
-        "print(json.dumps(ontology.normalized, indent=2))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* All Tool objects are constructed the same way:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "bbox_tool = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog_box\")\n",
-        "poly_tool = lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"dog_poly\")\n",
-        "seg_tool = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"dog_seg\")\n",
-        "point_tool = lb.Tool(tool=lb.Tool.Type.POINT, name=\"dog_center\")\n",
-        "line_tool = lb.Tool(tool=lb.Tool.Type.LINE, name=\"dog_orientation\")\n",
-        "ner_tool = lb.Tool(tool=lb.Tool.Type.NER, name=\"dog_reference\")\n",
-        "relationship_tool = lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Classifications are all constructed the same way (except text which doesn't require options)\n",
-        "* Classifications can be global or subclasses to a tool (ie dog bounding box, with a breed classification)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n",
-        "                                     name=\"dog_name\")\n",
-        "radio_classification = lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
-        "                                      name=\"dog_breed\",\n",
-        "                                      options=[lb.Option(\"poodle\")])\n",
-        "checklist_classification = lb.Classification(\n",
-        "    class_type=lb.Classification.Type.CHECKLIST,\n",
-        "    name=\"background\",\n",
-        "    options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Ontologies\n",
+                "* An ontology is a collection different tools and classifications that can be used within a project's editor. Each tool or classification is called a \"Feature Schema\". \n",
+                "* Feature Schemas contain information about the tool such as the kind, the name, all subclasses, and other information related to a tool. Feature Schemas can be shared between ontologies. \n",
+                "\n",
+                "* Helpful Links:\n",
+                "    * [Ontology documentation](https://docs.labelbox.com/docs/labelbox-ontology)\n",
+                "    * [Project Setup Using Ontologies](https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/project_setup.ipynb)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox -q",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Ontology From Normalized Data\n",
+                "* Users can create ontologies from a json definition of the ontology.\n",
+                "* See below `OntologyBuilder` section for more details on constructing the normalized ontology.\n",
+                "* Each tool type requires a specific value be passed:\n",
+                "\n",
+                "| Tool    | Value |\n",
+                "| :----------- | :----------- |\n",
+                "| Bounding box      | rectangle    |\n",
+                "| Polygon           | polygon      |\n",
+                "| Polyline          | line         |\n",
+                "| Point             | point        |\n",
+                "| Segmentation mask | raster-segmentation  |\n",
+                "| Entity            | named-entity |\n",
+                "| Relationship      | edge         |"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# This will automatically create new feature schema\nontology_name = \"sdk-ontology\"\nfeature_schema_cat_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"cat\",\n    \"color\": \"black\",\n}\n\nontology_normalized_json = {\n    \"tools\": [feature_schema_cat_normalized],\n    \"classifications\": [],\n}\nontology = client.create_ontology(name=ontology_name,\n                                  normalized=ontology_normalized_json)\nprint(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Ontology From Existing Feature Schemas\n",
+                "* It is often useful to support the same features in multiple ontologies. \n",
+                "* Labelbox supports this workflow by allowing users to create ontologies using existing feature schemas."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First create the feature schema\nfeature_schema_cat = client.create_feature_schema(feature_schema_cat_normalized)\n# When we create the ontology it will not re-create the feature schema\nprint(feature_schema_cat.uid)\nontology = client.create_ontology_from_feature_schemas(ontology_name,\n                                                       [feature_schema_cat.uid])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Ontology From a Mix of New and Existing Feature Schemas\n",
+                "* If we want to create a new ontology that expands upon a previous ontology it is helpful to be able to share a portion of the features.\n",
+                "* To do this we will create the new schema ids that we want. Then we will create an ontology from the new list of ids.\n",
+                "* Note that for additional customization you can also combine the normalized json and use the create_ontology() method (not covered here)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create new dog schema id\nfeature_schema_dog_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"dog\",\n    \"color\": \"black\",\n    \"classifications\": [],\n}\nfeature_schema_dog = client.create_feature_schema(feature_schema_dog_normalized)\n# The cat is shared between this new ontology and the one we created previously\n# (ie. the cat feature schema will not be re-created)\nontology = client.create_ontology_from_feature_schemas(\n    ontology_name, [feature_schema_cat.uid, feature_schema_dog.uid])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Read\n",
+                "* We can directly query by id for ontologies and feature schemas\n",
+                "* We also can search for both by name"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "#### Fetch by ID\nfeature_schema = client.get_feature_schema(feature_schema_cat.uid)\nontology = client.get_ontology(ontology.uid)\nprint(feature_schema)\nprint(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "#### Search by name\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nontology = next(client.get_ontologies(ontology_name))\nprint(feature_schema)\nprint(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Update and Delete"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Check if feature is archived\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nclient.is_feature_schema_archived(ontology_id=ontology.uid,\n                                  feature_schema_id=feature_schema.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Update a feature's title\nclient.update_feature_schema_title(feature_schema_id=feature_schema.uid,\n                                   title=\"cat-2\")\nfeature = client.get_feature_schema(feature_schema_id=feature_schema.uid)\nprint(\"Feature: \", feature)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Replace a feature\ntool = lb.Tool(\n    feature_schema_id=feature_schema.uid,\n    name=\"tool-cat-upserted\",\n    tool=lb.Tool.Type.BBOX,\n    color=\"#FF0000\",\n)\nupserted_feature_schema_id = client.upsert_feature_schema(tool.asdict()).uid\nfeature = client.get_feature_schema(\n    feature_schema_id=upserted_feature_schema_id)\nprint(\"Updated feature: \", feature)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Insert a new feature\ntool = lb.Tool(name=\"tool-cat-2\", tool=lb.Tool.Type.RASTER_SEGMENTATION)\nfeature_schema_id_new = client.create_feature_schema(tool.asdict()).uid\nclient.insert_feature_schema_into_ontology(\n    feature_schema_id=feature_schema_id_new,\n    ontology_id=ontology.uid,\n    position=2,\n)\nprint(\"Updated ontology: \", client.get_ontology(ontology_id=ontology.uid))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Delete or Archived a feature:\n",
+                "\n",
+                "If the feature schema is a root level node with associated labels, it will be archived.\n",
+                "If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
+                "If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.delete_feature_schema_from_ontology(\n    ontology_id=ontology.uid, feature_schema_id=feature_schema_id_new)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Only features with annotations will be archived, features without annotations will be deleted.\nfeature_schema_id_with_annotations = \"\"\nontology_id = \"\"\nclient.unarchive_feature_schema_node(\n    ontology_id=ontology_id,\n    root_feature_schema_id=feature_schema_id_with_annotations,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Ontology Builder\n",
+                "* The ontology builder is a tool for creating and modifying normalized json"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create normalized json with a bounding box and segmentation tool\nontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n    lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\"),\n])\n# Creating an ontology from this is easy\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Alternative syntax for defining the ontology via the OntologyBuilder"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create\nontology_builder = lb.OntologyBuilder()\n# Append tools\ntool_dog = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\")\ntool_cat = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\")\nontology_builder.add_tool(tool_dog)\nontology_builder.add_tool(tool_cat)\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Classifications are supported too (Both for top level and as subclassifications)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"cat\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"name\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"image_quality\",\n            options=[lb.Option(value=\"clear\"),\n                     lb.Option(value=\"blurry\")],\n        )\n    ],\n)\nprint(json.dumps(ontology_builder.asdict(), indent=2))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Example of how to add sub-classfication within an option"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder()\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"Global classification\",\n    options=[\n        lb.Option(\n            \"1st option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 1st option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n        lb.Option(\n            \"2nd option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 2nd option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n    ],\n)\n\nontology_builder.add_classification(radio_classification)\n\nontology = client.create_ontology(\"example of nested classification\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* All Tool objects are constructed the same way:"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "bbox_tool = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog_box\")\npoly_tool = lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"dog_poly\")\nseg_tool = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"dog_seg\")\npoint_tool = lb.Tool(tool=lb.Tool.Type.POINT, name=\"dog_center\")\nline_tool = lb.Tool(tool=lb.Tool.Type.LINE, name=\"dog_orientation\")\nner_tool = lb.Tool(tool=lb.Tool.Type.NER, name=\"dog_reference\")\nrelationship_tool = lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Classifications are all constructed the same way (except text which doesn't require options)\n",
+                "* Classifications can be global or subclasses to a tool (ie dog bounding box, with a breed classification)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                        name=\"dog_name\")\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"dog_breed\",\n    options=[lb.Option(\"poodle\")],\n)\nchecklist_classification = lb.Classification(\n    class_type=lb.Classification.Type.CHECKLIST,\n    name=\"background\",\n    options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")],\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index d188399a0..53dbb3bc0 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -1,515 +1,343 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Projects"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* A project can be thought of as a specific labeling task on a set of labels\n",
-        "* That set of labels is defined by the data rows attached to the project\n",
-        "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
-        "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
-        "* Also note that deprecated functions are not explained here."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "from labelbox.schema.conflict_resolution_strategy import ConflictResolutionStrategy\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a project\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Creates an empty project\n",
-        "project = client.create_project(name=\"my-test-project\",\n",
-        "                                description=\"a description\",\n",
-        "                                media_type=lb.MediaType.Image)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a dataset with data rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset = client.create_dataset(name=\"project-demo-dataset\")\n",
-        "global_keys = []\n",
-        "uploads = []\n",
-        "# Generate data rows\n",
-        "for i in range(1,9):\n",
-        "    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n",
-        "    uploads.append({\n",
-        "        'row_data':  f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-        "        \"global_key\": gb_key,\n",
-        "    })\n",
-        "    global_keys.append(gb_key)\n",
-        "\n",
-        "task = dataset.create_data_rows(uploads)\n",
-        "task.wait_till_done()\n",
-        "print(\"ERRORS: \" , task.errors)\n",
-        "print(\"RESULT URL: \", task.result_url)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Add data rows to a project \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"project-demo\", # each batch in a project must have a unique name\n",
-        "  global_keys=global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=1 # priority between 1(highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create tags and assign them to a project\n",
-        "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Create a tag"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get the organization\n",
-        "organization = client.get_organization()\n",
-        "\n",
-        "tag = organization.create_resource_tag(\n",
-        "  {\n",
-        "    \"text\": \"new-tag-name\",\n",
-        "    \"color\": \"4ed2f9\"\n",
-        "  }\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Assign the tag to a project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "tags = project.update_project_resource_tags([tag.uid])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Get project tags"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "tags = project.get_resource_tags()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Attach ontology and label data rows\n",
-        "\n",
-        "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Create your ontology"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create normalized json with a radio classification\n",
-        "ontology_builder = lb.OntologyBuilder(classifications=[  # List of Classification objects\n",
-        "        lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
-        "                          name=\"radio_question\",\n",
-        "                          options=[\n",
-        "                              lb.Option(value=\"first_radio_answer\"),\n",
-        "                              lb.Option(value=\"second_radio_answer\")\n",
-        "                          ]),\n",
-        "])\n",
-        "# Creating an ontology\n",
-        "ontology = client.create_ontology(\"test-ontology\",\n",
-        "                                  ontology_builder.asdict())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Attach ontology to project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create labels and upload them to project as ground truths"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create labels\n",
-        "labels = []\n",
-        "for global_key in global_keys:\n",
-        "    labels.append(lb_types.Label(data={\"global_key\":global_key},\n",
-        "                   annotations=[\n",
-        "                        # Create radio classification annotation for labels\n",
-        "                        lb_types.ClassificationAnnotation(\n",
-        "                         name=\"radio_question\",\n",
-        "                         value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "                         name=\"second_radio_answer\")))\n",
-        "                   ]))\n",
-        "\n",
-        "# Upload labels for the data rows in project\n",
-        "upload_job = lb.LabelImport.create_from_objects(\n",
-        "     client = client,\n",
-        "     project_id = project.uid,\n",
-        "     name=\"label_import_job\"+str(uuid.uuid4()),\n",
-        "     labels=labels)\n",
-        "\n",
-        "upload_job.wait_until_done()\n",
-        "\n",
-        "print(f\"Errors: {upload_job.errors}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Move data rows in project to different task queues"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get list of task queues for project\n",
-        "task_queues = project.task_queues()\n",
-        "\n",
-        "for task_queue in task_queues:\n",
-        "    print(task_queue)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.move_data_rows_to_task_queue(data_row_ids=lb.GlobalKeys(global_keys), #Provide a list of global keys\n",
-        "                                     task_queue_id=task_queues[2].uid #Passing None moves data rows to \"Done\" task queue\n",
-        "                                    )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Fetch project configuration"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Note the project is not fully setup many of the fields will be empty.\n",
-        "print(\"Project is not setup yet:\", project.setup_complete is None)\n",
-        "print(\"Project name:\", project.name)\n",
-        "print(\"Project description:\", project.description)\n",
-        "print(\"Media Type:\", project.media_type)\n",
-        "batches = [b for b in project.batches()]\n",
-        "print(\"Project Batches\", batches)\n",
-        "print(\"Ontology:\", project.ontology())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Return number of labeled data rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(\"Number of labels:\",  project.get_label_count())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Copy labels and data rows from one project to a different project\n",
-        "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create an empty destination project\n",
-        "destination_project = client.create_project(name=\"destination-test-project\",\n",
-        "                                description=\"a description\",\n",
-        "                                media_type=lb.MediaType.Image)\n",
-        "\n",
-        "# Create ontology and attach to destination project\n",
-        "destination_ontology_builder = lb.OntologyBuilder(classifications=[  # List of Classification objects\n",
-        "        lb.Classification(class_type=lb.Classification.Type.RADIO,\n",
-        "                          name=\"destination_radio_question\",\n",
-        "                          options=[\n",
-        "                              lb.Option(value=\"destination_first_radio_answer\"),\n",
-        "                              lb.Option(value=\"destination_second_radio_answer\")\n",
-        "                          ]),\n",
-        "])\n",
-        "\n",
-        "destination_ontology = client.create_ontology(\"dest-test-ontology\",\n",
-        "                                  ontology_builder.asdict())\n",
-        "\n",
-        "destination_project.setup_editor(destination_ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Copy data rows and labels\n",
-        "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
-        "\n",
-        "##### Parameters\n",
-        "\n",
-        "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
-        "\n",
-        "* `source_project_id`\n",
-        "    - The id of the project were our data rows with labels will originate.\n",
-        "* `annotation_ontology_mapping`\n",
-        "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
-        "* `exclude_data_rows_in_project`\n",
-        "    - Excludes data rows that are already in the project. \n",
-        "* `override_existing_annotations_rule` \n",
-        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
-        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-        "        * Options include:\n",
-        "            * ConflictResolutionStrategy.KeepExisting\n",
-        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-        "* `param batch_priority`\n",
-        "    - The priority of the batch."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get ontology dictionary to obtain featureSchemaIds\n",
-        "source_ontology_normalized = ontology.normalized\n",
-        "destination_ontology_normalized = destination_ontology.normalized\n",
-        "\n",
-        "ANNOTATION_ONTOLOGY_MAPPING = {\n",
-        "    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:destination_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"], # Classification featureSchemaID\n",
-        "    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:destination_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"], # Different Classification Answer featureSchemaIDs\n",
-        "    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:destination_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "send_to_annotate_params = {\n",
-        "    \"source_project_id\": project.uid,\n",
-        "    \"annotations_ontology_mapping\": ANNOTATION_ONTOLOGY_MAPPING,\n",
-        "    \"exclude_data_rows_in_project\": False,\n",
-        "    \"override_existing_annotations_rule\": ConflictResolutionStrategy.OverrideWithPredictions,\n",
-        "    \"batch_priority\": 5,\n",
-        "}\n",
-        "\n",
-        "# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels. \n",
-        "queue_id = [queue.uid for queue in destination_project.task_queues() if queue.queue_type == \"MANUAL_REVIEW_QUEUE\" ][0]\n",
-        "\n",
-        "task = client.send_to_annotate_from_catalog(\n",
-        "    destination_project_id=destination_project.uid,\n",
-        "    task_queue_id=queue_id, # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n",
-        "    batch_name=\"Prediction Import Demo Batch\",\n",
-        "    data_rows=lb.GlobalKeys(\n",
-        "        global_keys # Provide a list of global keys from source project\n",
-        "    ),\n",
-        "    params=send_to_annotate_params\n",
-        "    )\n",
-        "\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Clean Up"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# destination_project.delete()\n",
-        "# dataset.delete()\n",
-        "# client.delete_unused_ontology(destination_ontology.uid)\n",
-        "# client.delete_unused_ontology(ontology.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Projects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* A project can be thought of as a specific labeling task on a set of labels\n",
+                "* That set of labels is defined by the data rows attached to the project\n",
+                "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
+                "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
+                "* Also note that deprecated functions are not explained here."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a project\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a dataset with data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Add data rows to a project \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create tags and assign them to a project\n",
+                "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create a tag"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Assign the tag to a project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "tags = project.update_project_resource_tags([tag.uid])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Get project tags"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "tags = project.get_resource_tags()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Attach ontology and label data rows\n",
+                "\n",
+                "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create your ontology"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Attach ontology to project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create labels and upload them to project as ground truths"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Move data rows in project to different task queues"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Fetch project configuration"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Return number of labeled data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "print(\"Number of labels:\", project.get_label_count())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Copy labels and data rows from one project to a different project\n",
+                "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Copy data rows and labels\n",
+                "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
+                "\n",
+                "##### Parameters\n",
+                "\n",
+                "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
+                "\n",
+                "* `source_project_id`\n",
+                "    - The id of the project were our data rows with labels will originate.\n",
+                "* `annotation_ontology_mapping`\n",
+                "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
+                "* `exclude_data_rows_in_project`\n",
+                "    - Excludes data rows that are already in the project. \n",
+                "* `override_existing_annotations_rule` \n",
+                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
+                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+                "        * Options include:\n",
+                "            * ConflictResolutionStrategy.KeepExisting\n",
+                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+                "* `param batch_priority`\n",
+                "    - The priority of the batch."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Clean Up"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index 4772c1529..40d95cfee 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -1,302 +1,248 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# User Management\n",
-        "* This notebook covers the following:\n",
-        "    * create invites\n",
-        "    * query for remaining allowed invites to an organization\n",
-        "    * set and update organization roles\n",
-        "    * assign users to projects\n",
-        "        * set / update / revoke project role\n",
-        "    * delete users from org"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import os"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* You have to specifically enable experimental features to use this functionality. Notice the \n",
-        "`enable_experimental = True`\n",
-        "    * enables users to send invites and checking the number of seats available via the sdk"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = None\n",
-        "client = lb.Client(api_key=API_KEY, enable_experimental=True)\n",
-        "organization = client.get_organization()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Please provide a dummy email here:\n",
-        "# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\n",
-        "DUMMY_EMAIL = \"SET THIS\"\n",
-        "# This should be set to an account that you wan't to change the permissions for.\n",
-        "# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\n",
-        "DUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Roles\n",
-        "* When inviting a new user to an organization, there are various roles to select from.\n",
-        "* All available roles to your org can be accessed via `client.get_roles()`"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "roles = client.get_roles()\n",
-        "for name, role in roles.items():\n",
-        "    print(role.name, \":\", role.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Above we printed out all of the roles available to the current org.\n",
-        "* Notice the `NONE`. That is for project level roles"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create\n",
-        "* Users are created by sending an invite\n",
-        "* An email will be sent to them and they will be asked to join your organization"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Organization Level Permissions\n",
-        "* Invite a new labeler with labeling permissions on all projects"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# First make sure that you have enough seats:\n",
-        "organization.invite_limit()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(invite.created_at)\n",
-        "print(invite.organization_role_name)\n",
-        "print(invite.email)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Project Level Permissions\n",
-        "* Invite a new labeler with labeling permissions specific to a set of projects\n",
-        "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(name=\"test_user_management\", media_type=lb.MediaType.Image)\n",
-        "project_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\n",
-        "invite = organization.invite_user(DUMMY_EMAIL,\n",
-        "                                  roles[\"NONE\"],\n",
-        "                                  project_roles=[project_role])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Read\n",
-        "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
-        "* You are able to query for members once they have joined."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "users = list(organization.users())\n",
-        "print(users[0])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Update\n",
-        "* There is no update on invites. Instead you must delete and resend them\n",
-        "* You can update User roles"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n",
-        "\n",
-        "# Give the user organization level permissions\n",
-        "user.update_org_role(roles[\"LABELER\"])\n",
-        "print(user.org_role())\n",
-        "# Restore project level permissions\n",
-        "user.update_org_role(roles[\"NONE\"])\n",
-        "print(user.org_role())\n",
-        "# Make the user a labeler for the current project\n",
-        "user.upsert_project_role(project, roles[\"LABELER\"])\n",
-        "print(user.org_role())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\n",
-        "user.remove_from_project(project)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Delete"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Invites can only be deleted from the ui at this time. \n",
-        "* Deleting invites can be done in the members tab of the web app."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Delete the User\n",
-        "* Make sure you want to remove the user from the org:\n",
-        "* `>>> organization.remove_user(user)`"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Cleanup\n",
-        "* We created an extra project. Let's delete it"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# User Management\n",
+                "* This notebook covers the following:\n",
+                "    * create invites\n",
+                "    * query for remaining allowed invites to an organization\n",
+                "    * set and update organization roles\n",
+                "    * assign users to projects\n",
+                "        * set / update / revoke project role\n",
+                "    * delete users from org"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport os",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* You have to specifically enable experimental features to use this functionality. Notice the \n",
+                "`enable_experimental = True`\n",
+                "    * enables users to send invites and checking the number of seats available via the sdk"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Roles\n",
+                "* When inviting a new user to an organization, there are various roles to select from.\n",
+                "* All available roles to your org can be accessed via `client.get_roles()`"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Above we printed out all of the roles available to the current org.\n",
+                "* Notice the `NONE`. That is for project level roles"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create\n",
+                "* Users are created by sending an invite\n",
+                "* An email will be sent to them and they will be asked to join your organization"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Organization Level Permissions\n",
+                "* Invite a new labeler with labeling permissions on all projects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Project Level Permissions\n",
+                "* Invite a new labeler with labeling permissions specific to a set of projects\n",
+                "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Read\n",
+                "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
+                "* You are able to query for members once they have joined."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "users = list(organization.users())\nprint(users[0])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Update\n",
+                "* There is no update on invites. Instead you must delete and resend them\n",
+                "* You can update User roles"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Delete"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Invites can only be deleted from the ui at this time. \n",
+                "* Deleting invites can be done in the members tab of the web app."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Delete the User\n",
+                "* Make sure you want to remove the user from the org:\n",
+                "* `>>> organization.remove_user(user)`"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Cleanup\n",
+                "* We created an extra project. Let's delete it"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/exports/composite_mask_export.ipynb b/examples/exports/composite_mask_export.ipynb
index 7d70a3922..9b37c4305 100644
--- a/examples/exports/composite_mask_export.ipynb
+++ b/examples/exports/composite_mask_export.ipynb
@@ -1,380 +1,257 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Export composite masks \n",
-        "##### **Composite masks are only available on raster segmentation projects**\n",
-        "\n",
-        "Composite masks are a combination of mask instances grouped in a single mask URL. \n",
-        "\n",
-        "The purpose of this demo is to demonstrate how to transition from exporting single masks to exporting composite masks. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Imports"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "%pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import urllib.request\n",
-        "from PIL import Image\n",
-        "import json\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## API Key and Client\n",
-        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)\n",
-        "client.enable_experimental = True ## This is required if using the export() streamable method"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Key differences between single mask instance and composite mask."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Composite masks\n",
-        "**IMPORTANT :** The URL for the ```composite_mask``` from exports older than 30 days will no longer be accessible. To obtain a functional URL after this period, please generate a new export for the mask.\n",
-        "1. A single mask url contains all mask instances from a single label. For videos a composite mask contains all mask instances for the frame in each label. \n",
-        "2. The export and mask URL adhere to the following convention:\n",
-        "  - ***Image example***\n",
-        "```json \n",
-        "  {\n",
-        "    \"composite_mask\": {\n",
-        "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
-        "      \"color_rgb\": [\n",
-        "          142,\n",
-        "          220,\n",
-        "          196\n",
-        "      ]\n",
-        "    }\n",
-        "  }\n",
-        "```\n",
-        "  - ***Video example*** :\n",
-        "  The export will adhere to the following URL convention by default.\n",
-        "```json\n",
-        "  {\n",
-        "    \"composite_mask\": {\n",
-        "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/{frame_number}\",\n",
-        "      \"color_rgb\": [\n",
-        "        224,\n",
-        "        17,\n",
-        "        103\n",
-        "      ]\n",
-        "    }\n",
-        "  }\n",
-        "```\n",
-        "3. A unique RGB color is assigned to each mask instance. The example below shows a composite mask of a label, and while it contains all mask instances, only the RGB color associated with this particular annotation will be filled in under the ```color_rgb``` field."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Example on how to fetch a composite mask\n",
-        "# The mask here shows all the mask instances associated with a label\n",
-        "task_id = \"\"\n",
-        "composite_mask_id = \"\"\n",
-        "\n",
-        "mask_url = f'https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1'\n",
-        "req = urllib.request.Request(mask_url, headers=client.headers)\n",
-        "image = Image.open(urllib.request.urlopen(req))\n",
-        "w, h = image.size\n",
-        "new_w = w // 4\n",
-        "new_h = h // 4\n",
-        "\n",
-        "image.resize((new_w, new_h), Image.BICUBIC)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Here's an example of an entry featuring a composite mask (see image above) containing the mask instance's RGB color uniquely associated with the annotation.\n",
-        "\n",
-        "```json\n",
-        "    {\n",
-        "      \"feature_id\": \"clpk3ow9u006f14vs2w5qa9l3\",\n",
-        "      \"feature_schema_id\": \"clpk3nvrv05bh08ua8fwqavng\",\n",
-        "      \"name\": \"mask\",\n",
-        "      \"value\": \"mask\",\n",
-        "      \"annotation_kind\": \"ImageSegmentationMask\",\n",
-        "      \"classifications\": [],\n",
-        "      \"composite_mask\": {\n",
-        "          \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
-        "          \"color_rgb\": [\n",
-        "              123,\n",
-        "              103,\n",
-        "              152\n",
-        "          ]\n",
-        "      }\n",
-        "    }\n",
-        "```\n",
-        "- rgb(123,103,152) = Purple\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "---"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Single mask instance:\n",
-        "We are planning on removing single mask instances, but for now they will be displayed adjecent to the composite mask.\n",
-        "1. A single mask instance and mask url is generated for each individual annotation for each label.\n",
-        "2. The export and mask URL adhere to the following convention:  \n",
-        "```json\n",
-        "      {\n",
-        "        \"mask\": {\n",
-        "              \"url\": \"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
-        "          }\n",
-        "      }\n",
-        "\n",
-        "```\n",
-        "3. RGB color is not present"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create an export from an Image project with mask annotations\n",
-        "To better showcase composite masks, make sure you have different mask tools and mask annotations in your project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert the project ID of the project from which you wish to export data rows.\n",
-        "PROJECT_ID = \"\"\n",
-        "project = client.get_project(PROJECT_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True\n",
-        "}\n",
-        "\n",
-        "filters= {}\n",
-        "\n",
-        "# export() is the streamable option of exports V2, for more information please visit our documentation:\n",
-        "# https://docs.labelbox.com/reference/label-export#export-v2-methods\n",
-        "\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  print(export_task.result)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "stream = export_task.get_stream()\n",
-        "\n",
-        "mask_tool_rgb_mapping = {}\n",
-        "\n",
-        "for output in stream:\n",
-        "    # Parse the JSON string from the output\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "\n",
-        "    # Get the labels for the specified project ID or an empty list if the project ID is not found\n",
-        "    project_labels = output_json['projects'].get(PROJECT_ID, {}).get('labels', [])\n",
-        "\n",
-        "    # Iterate through each label\n",
-        "    for label in project_labels:\n",
-        "        # Get the list of annotations (objects) for the label\n",
-        "        annotations = label['annotations'].get('objects', [])\n",
-        "\n",
-        "        # Iterate through each annotation\n",
-        "        for annotation in annotations:\n",
-        "            # Check if the annotation is of type \"ImageSegmentationMask\"\n",
-        "            if annotation.get('annotation_kind') == \"ImageSegmentationMask\":\n",
-        "                # Add the color RGB information to the mapping dictionary\n",
-        "                mask_tool_rgb_mapping.setdefault(annotation['name'], []).append(annotation['composite_mask']['color_rgb'])\n",
-        "\n",
-        "print(mask_tool_rgb_mapping)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create an export from a Video project with mask annotations "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "VIDEO_PROJECT_ID = \"\"\n",
-        "project_video = client.get_project(VIDEO_PROJECT_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True\n",
-        "}\n",
-        "\n",
-        "filters= {}\n",
-        "\n",
-        "# export() is the streamable option of exports V2, for more information please visit our documentation:\n",
-        "# https://docs.labelbox.com/reference/label-export#export-v2-methods\n",
-        "\n",
-        "export_task_video = project_video.export(params=export_params, filters=filters)\n",
-        "export_task_video.wait_till_done()\n",
-        "\n",
-        "if export_task_video.has_result():\n",
-        "  print(export_task_video.result)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool from each frame"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "tools_frames_color = {}\n",
-        "stream = export_task_video.get_stream()\n",
-        "\n",
-        "# Iterate over each output in the stream\n",
-        "for output in stream:\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "\n",
-        "    # Iterate over the labels in the specific project\n",
-        "    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n",
-        "        frames_data = dr[\"annotations\"][\"frames\"]\n",
-        "\n",
-        "        # Iterate over each frame in the frames data\n",
-        "        for frame_key, frame_value in frames_data.items():\n",
-        "\n",
-        "            # Iterate over each annotation in the frame\n",
-        "            for annotation_key, annotation_value in frame_value.items():\n",
-        "                if 'objects' in annotation_key and annotation_value.values():\n",
-        "\n",
-        "                    # Iterate over each object in the annotation\n",
-        "                    for object_key, object_value in annotation_value.items():\n",
-        "                        if object_value['annotation_kind'] == 'VideoSegmentationMask':\n",
-        "                            # Update tools_frames_color with object information\n",
-        "                            tools_frames_color.setdefault(object_value['name'], []).append({frame_key: object_value['composite_mask']['color_rgb']})\n",
-        "\n",
-        "print(tools_frames_color)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Export composite masks \n",
+                "##### **Composite masks are only available on raster segmentation projects**\n",
+                "\n",
+                "Composite masks are a combination of mask instances grouped in a single mask URL. \n",
+                "\n",
+                "The purpose of this demo is to demonstrate how to transition from exporting single masks to exporting composite masks. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Imports"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API Key and Client\n",
+                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Key differences between single mask instance and composite mask."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Composite masks\n",
+                "**IMPORTANT :** The URL for the ```composite_mask``` from exports older than 30 days will no longer be accessible. To obtain a functional URL after this period, please generate a new export for the mask.\n",
+                "1. A single mask url contains all mask instances from a single label. For videos a composite mask contains all mask instances for the frame in each label. \n",
+                "2. The export and mask URL adhere to the following convention:\n",
+                "  - ***Image example***\n",
+                "```json \n",
+                "  {\n",
+                "    \"composite_mask\": {\n",
+                "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
+                "      \"color_rgb\": [\n",
+                "          142,\n",
+                "          220,\n",
+                "          196\n",
+                "      ]\n",
+                "    }\n",
+                "  }\n",
+                "```\n",
+                "  - ***Video example*** :\n",
+                "  The export will adhere to the following URL convention by default.\n",
+                "```json\n",
+                "  {\n",
+                "    \"composite_mask\": {\n",
+                "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/{frame_number}\",\n",
+                "      \"color_rgb\": [\n",
+                "        224,\n",
+                "        17,\n",
+                "        103\n",
+                "      ]\n",
+                "    }\n",
+                "  }\n",
+                "```\n",
+                "3. A unique RGB color is assigned to each mask instance. The example below shows a composite mask of a label, and while it contains all mask instances, only the RGB color associated with this particular annotation will be filled in under the ```color_rgb``` field."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Example on how to fetch a composite mask\n# The mask here shows all the mask instances associated with a label\ntask_id = \"\"\ncomposite_mask_id = \"\"\n\nmask_url = f\"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\"\nreq = urllib.request.Request(mask_url, headers=client.headers)\nimage = Image.open(urllib.request.urlopen(req))\nw, h = image.size\nnew_w = w // 4\nnew_h = h // 4\n\nimage.resize((new_w, new_h), Image.BICUBIC)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Here's an example of an entry featuring a composite mask (see image above) containing the mask instance's RGB color uniquely associated with the annotation.\n",
+                "\n",
+                "```json\n",
+                "    {\n",
+                "      \"feature_id\": \"clpk3ow9u006f14vs2w5qa9l3\",\n",
+                "      \"feature_schema_id\": \"clpk3nvrv05bh08ua8fwqavng\",\n",
+                "      \"name\": \"mask\",\n",
+                "      \"value\": \"mask\",\n",
+                "      \"annotation_kind\": \"ImageSegmentationMask\",\n",
+                "      \"classifications\": [],\n",
+                "      \"composite_mask\": {\n",
+                "          \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
+                "          \"color_rgb\": [\n",
+                "              123,\n",
+                "              103,\n",
+                "              152\n",
+                "          ]\n",
+                "      }\n",
+                "    }\n",
+                "```\n",
+                "- rgb(123,103,152) = Purple\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "---"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Single mask instance:\n",
+                "We are planning on removing single mask instances, but for now they will be displayed adjecent to the composite mask.\n",
+                "1. A single mask instance and mask url is generated for each individual annotation for each label.\n",
+                "2. The export and mask URL adhere to the following convention:  \n",
+                "```json\n",
+                "      {\n",
+                "        \"mask\": {\n",
+                "              \"url\": \"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
+                "          }\n",
+                "      }\n",
+                "\n",
+                "```\n",
+                "3. RGB color is not present"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create an export from an Image project with mask annotations\n",
+                "To better showcase composite masks, make sure you have different mask tools and mask annotations in your project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.has_result():\n    print(export_task.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "stream = export_task.get_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json_str)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create an export from a Video project with mask annotations "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "VIDEO_PROJECT_ID = \"\"\nproject_video = client.get_project(VIDEO_PROJECT_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task_video = project_video.export(params=export_params, filters=filters)\nexport_task_video.wait_till_done()\n\nif export_task_video.has_result():\n    print(export_task_video.result)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool from each frame"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "tools_frames_color = {}\nstream = export_task_video.get_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json_str)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/exports/export_data.ipynb b/examples/exports/export_data.ipynb
index 0d9f39b55..0d897709c 100644
--- a/examples/exports/export_data.ipynb
+++ b/examples/exports/export_data.ipynb
@@ -1,945 +1,479 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Export data\n",
-        "How to export data for projects, datasets, slices, data rows and models, with examples for each type of v2 export along with details on optional parameters and filters.\n",
-        "\n",
-        "***Beginning with SDK version 3.68, the `export_v2()` method has been enhanced to incorporate streamable functionality.***"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\"\n",
-        "!pip install -q urllib3"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import urllib.request\n",
-        "from PIL import Image\n",
-        "import time"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a project\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-        "\n",
-        "### Parameters\n",
-        "When you export data rows from a project, you may choose to include or exclude certain attributes, including:\n",
-        "- `attachments`\n",
-        "- `metadata_fields`\n",
-        "- `data_row_details`\n",
-        "- `project_details`\n",
-        "- `label_details`\n",
-        "- `performance_details`\n",
-        "- `interpolated_frames`\n",
-        "    - Only applicable for video data rows.\n",
-        "\n",
-        "### Filters\n",
-        "When you export data rows from a project, you can specify the included data rows with the following filters:\n",
-        "- `last_activity_at`\n",
-        "- `label_created_at`\n",
-        "- `data_row_ids`\n",
-        "- `global_keys`\n",
-        "- `batch_ids`\n",
-        "- `workflow_status`\n",
-        "\n",
-        "#### Filter details\n",
-        "You can set the range for `last_activity_at` and `label_created_at` in the following formats: \n",
-        "- `YYYY-MM-DD`\n",
-        "- `YYYY-MM-DD hh:mm:ss`\n",
-        "- `YYYY-MM-DDThh:mm:ss\u00b1hhmm` (ISO 8601)\n",
-        "\n",
-        "The ISO 8061 format allows you to specify the timezone, while the other two formats assume timezone from the user's workspace settings.\n",
-        "\n",
-        "The `last_activity_at` filter captures the creation and modification of labels, metadata, workflow status, comments, and reviews.\n",
-        "\n",
-        "If you wish to specify data rows to export, uncomment the `data_row_ids` or `global_keys` filter and provide a list of applicable IDs. The data rows must be part of a batch attached to the project in question. You can provide up to 2,000 data row IDs.\n",
-        "\n",
-        "The `batch_ids` filter allows you to specify data rows for export based on their associated batch ID. This is particularly useful when `data_row_ids` is not sufficient due to 2,000 data row IDs limit. \n",
-        "\n",
-        "\n",
-        "The `workflow_status` filter allows you to specify data rows in a given work flow step. This filter only accepts one value. For example, `filters = {\"workflow_status\": \"InReview\"}`. The filer accepts the following:\n",
-        "- `ToLabel`\n",
-        "- `InReview`\n",
-        "- `InRework`\n",
-        "- `Done`\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert the project ID of the project from which you wish to export data rows.\n",
-        "PROJECT_ID = \"\"\n",
-        "project = client.get_project(PROJECT_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2 Method\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True\n",
-        "}\n",
-        "\n",
-        "# Note: Filters follow AND logic, so typically using one filter is sufficient.\n",
-        "filters= {\n",
-        "  \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n",
-        "  # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n",
-        "  # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n",
-        "  # \"workflow_status\": \"<workflow_status>\"\n",
-        "}\n",
-        "\n",
-        "export_task = project.export_v2(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.errors:\n",
-        "  print(export_task.errors)\n",
-        "\n",
-        "export_json = export_task.result\n",
-        "print(\"results: \", export_json)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Stream Task Export Method\n",
-        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-        "This allows streaming of task results and errors."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True\n",
-        "\n",
-        "}\n",
-        "\n",
-        "# Note: Filters follow AND logic, so typically using one filter is sufficient.\n",
-        "filters= {\n",
-        "  \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n",
-        "  # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n",
-        "  # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n",
-        "  # \"workflow_status\": \"<workflow_status>\"\n",
-        "}\n",
-        "\n",
-        "client.enable_experimental = True\n",
-        "\n",
-        "export_task = project.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Uncomment to get stream results as a written file\n",
-        "\n",
-        "# Provide results with file converter\n",
-        "\n",
-        "# if export_task.has_errors():\n",
-        "#     export_task.get_stream(\n",
-        "#         converter=lb.FileConverter(file_path=\"./errors.txt\"),\n",
-        "#         stream_type=lb.StreamType.ERRORS\n",
-        "#     ).start()\n",
-        "\n",
-        "# if export_task.has_result():\n",
-        "#     export_task.get_stream(\n",
-        "#         converter=lb.FileConverter(file_path=\"./result.txt\"),\n",
-        "#         stream_type=lb.StreamType.RESULT\n",
-        "#     ).start()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a dataset\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-        "\n",
-        "### Parameters\n",
-        "When you export data rows from a dataset, you may choose to include or exclude certain attributes, including:\n",
-        "- `attachments`\n",
-        "- `metadata_fields`\n",
-        "- `data_row_details`\n",
-        "- `project_details`\n",
-        "- `label_details`\n",
-        "- `performance_details`\n",
-        "- `interpolated_frames`\n",
-        "    - Only applicable for video data rows.\n",
-        "- `project_ids`\n",
-        "    - Accepts a list of project IDs. If provided, the labels created _in these projects_ on the exported data rows will be included. \n",
-        "- `model_run_ids`\n",
-        "    - Accepts a list of model run IDs. If provided, the labels and predicitions created _in these model runs_ will be included. \n",
-        "\n",
-        "### Filters\n",
-        "When you export data rows from a dataset, you can specify the included data rows with the following filters:\n",
-        "- `last_activity_at`\n",
-        "- `label_created_at`\n",
-        "- `data_row_ids`\n",
-        "- `global_keys`\n",
-        "\n",
-        "See the _Export data rows from a project_ section above for additional details on each filter. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert the dataset ID of the dataset from which you wish to export data rows.\n",
-        "DATASET_ID = \"\"\n",
-        "dataset = client.get_dataset(DATASET_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2 Method"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True,\n",
-        "  # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n",
-        "  # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n",
-        "}\n",
-        "\n",
-        "# Note: Filters follow AND logic, so typically using one filter is sufficient.\n",
-        "filters= {\n",
-        "  \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n",
-        "  # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n",
-        "}\n",
-        "\n",
-        "export_task = dataset.export_v2(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.errors:\n",
-        "  print(export_task.errors)\n",
-        "\n",
-        "export_json = export_task.result\n",
-        "print(\"results: \", export_json)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Stream Task Export Method\n",
-        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-        "This allows streaming of task results and errors."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True,\n",
-        "  # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n",
-        "  # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n",
-        "}\n",
-        "\n",
-        "# Note: Filters follow AND logic, so typically using one filter is sufficient.\n",
-        "filters= {\n",
-        "  \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n",
-        "  # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n",
-        "}\n",
-        "\n",
-        "client.enable_experimental = True\n",
-        "\n",
-        "export_task = dataset.export(params=export_params, filters=filters)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from Catalog\n",
-        "`get_catalog()` method allows you to fetch all your data from catalog\n",
-        "\n",
-        "### Parameters\n",
-        "When exporting from catalog, you can apply the same parameters as exporting from a project.\n",
-        "\n",
-        "### Filters\n",
-        "When exporting from catalog, you can apply the same filters as exporting from a dataset.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "catalog = client.get_catalog()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True,\n",
-        "  # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n",
-        "  # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n",
-        "}\n",
-        "\n",
-        "filters = {\n",
-        "  \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n",
-        "  # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n",
-        "\n",
-        "}\n",
-        "export_task = catalog.export_v2(params=export_params,filters=filters)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.errors:\n",
-        "  print(export_task.errors)\n",
-        "\n",
-        "export_json = export_task.result\n",
-        "print(\"results: \", export_json)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Stream Task Export Method\n",
-        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-        "This allows streaming of task results and errors."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "export_params = {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True,\n",
-        "  # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n",
-        "  # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n",
-        "}\n",
-        "\n",
-        "filters = {\n",
-        "  \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n",
-        "  # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n",
-        "  # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n",
-        "}\n",
-        "\n",
-        "client.enable_experimental = True\n",
-        "export_task = catalog.export(params=export_params)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a slice\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-        "\n",
-        "### Parameters\n",
-        "When exporting from a slice, you can apply the same parameters as exporting from a dataset.\n",
-        "\n",
-        "### Filters\n",
-        "No filters are applicable to exports from a slice. All the data rows of the slice must be exported."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert the Catalog slice ID of the slice from which you wish to export data rows.\n",
-        "CATALOG_SLICE_ID = \"\"\n",
-        "catalog_slice = client.get_catalog_slice(CATALOG_SLICE_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2 Method"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params = {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True,\n",
-        "  # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n",
-        "  # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n",
-        "}\n",
-        "\n",
-        "export_task = catalog_slice.export_v2(params=export_params)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.errors:\n",
-        "  print(export_task.errors)\n",
-        "\n",
-        "export_json = export_task.result\n",
-        "print(\"results: \", export_json)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Stream Task Export Method\n",
-        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-        "This allows streaming of task results and errors."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params = {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True,\n",
-        "  # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n",
-        "  # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n",
-        "}\n",
-        "\n",
-        "\n",
-        "client.enable_experimental = True\n",
-        "\n",
-        "export_task = catalog_slice.export(params=export_params)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export data rows from a model run\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-        "\n",
-        "### Parameters\n",
-        "- `attachments`\n",
-        "- `metadata_fields`\n",
-        "- `data_row_details`\n",
-        "- `interpolated_frames`\n",
-        "    - Only applicable for video data rows.\n",
-        "- `predictions`\n",
-        "    - If true, all predictions made in the model run will be included for each data row in the export.\n",
-        "\n",
-        "### Filters\n",
-        "No filters are applicable to exports from a model run. All the data rows of the model run must be exported.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert the model run ID of the model run from which you wish to export data rows.\n",
-        "MODEL_RUN_ID = \"\"\n",
-        "model_run = client.get_model_run(MODEL_RUN_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2 Method"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params = {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"predictions\": True,\n",
-        "  \"embeddings\": True\n",
-        "}\n",
-        "\n",
-        "export_task = model_run.export_v2(params=export_params)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.errors:\n",
-        "  print(export_task.errors)\n",
-        "\n",
-        "export_json = export_task.result\n",
-        "print(\"results: \", export_json)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Stream Task Export Method\n",
-        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-        "This allows streaming of task results and errors."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params = {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"predictions\": True,\n",
-        "  \"embeddings\": True\n",
-        "}\n",
-        "\n",
-        "client.enable_experimental = True\n",
-        "\n",
-        "export_task = model_run.export(params=export_params)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Export Data Row\n",
-        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-        "\n",
-        "### Parameters\n",
-        "When exporting data rows, you can apply the same parameters as exporting from a project.\n",
-        "\n",
-        "### Filters\n",
-        "No filters are applicable to export data rows. All the data rows specified in the export task are included."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Insert the global key of the data row you wish to export\n",
-        "DATA_ROW_GLOBAL_KEY = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Export V2 Method"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True\n",
-        "}\n",
-        "\n",
-        "# Provide a list of data row global keys\n",
-        "export_task = lb.DataRow.export_v2(client=client, global_keys=[DATA_ROW_GLOBAL_KEY], params=export_params)\n",
-        "export_task.wait_till_done()\n",
-        "\n",
-        "if export_task.errors:\n",
-        "  print(export_task.errors)\n",
-        "\n",
-        "export_json = export_task.result\n",
-        "print(\"results: \", export_json)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Stream Task Export Method\n",
-        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-        "This allows streaming of task results and errors."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set the export params to include/exclude certain fields.\n",
-        "export_params= {\n",
-        "  \"attachments\": True,\n",
-        "  \"metadata_fields\": True,\n",
-        "  \"data_row_details\": True,\n",
-        "  \"project_details\": True,\n",
-        "  \"label_details\": True,\n",
-        "  \"performance_details\": True,\n",
-        "  \"interpolated_frames\": True,\n",
-        "  \"embeddings\": True\n",
-        "}\n",
-        "\n",
-        "client.enable_experimental = True\n",
-        "\n",
-        "# Provide a list of data row global keys\n",
-        "export_task = lb.DataRow.export(client=client, global_keys=[DATA_ROW_GLOBAL_KEY], params=export_params)\n",
-        "export_task.wait_till_done()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Provide results with JSON converter\n",
-        "# Returns streamed JSON output strings from export task results/errors, one by one\n",
-        "\n",
-        "# Callback used for JSON Converter\n",
-        "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-        "  print(output.json_str)\n",
-        "\n",
-        "if export_task.has_errors():\n",
-        "  export_task.get_stream(\n",
-        "  converter=lb.JsonConverter(),\n",
-        "  stream_type=lb.StreamType.ERRORS\n",
-        "  ).start(stream_handler=lambda error: print(error))\n",
-        "\n",
-        "if export_task.has_result():\n",
-        "  export_json = export_task.get_stream(\n",
-        "    converter=lb.JsonConverter(),\n",
-        "    stream_type=lb.StreamType.RESULT\n",
-        "  ).start(stream_handler=json_stream_handler)\n",
-        "\n",
-        "print(\"file size: \", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))\n",
-        "print(\"line count: \", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Export data\n",
+                "How to export data for projects, datasets, slices, data rows and models, with examples for each type of v2 export along with details on optional parameters and filters.\n",
+                "\n",
+                "***Beginning with SDK version 3.68, the `export_v2()` method has been enhanced to incorporate streamable functionality.***"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q urllib3",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport time",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a project\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+                "\n",
+                "### Parameters\n",
+                "When you export data rows from a project, you may choose to include or exclude certain attributes, including:\n",
+                "- `attachments`\n",
+                "- `metadata_fields`\n",
+                "- `data_row_details`\n",
+                "- `project_details`\n",
+                "- `label_details`\n",
+                "- `performance_details`\n",
+                "- `interpolated_frames`\n",
+                "    - Only applicable for video data rows.\n",
+                "\n",
+                "### Filters\n",
+                "When you export data rows from a project, you can specify the included data rows with the following filters:\n",
+                "- `last_activity_at`\n",
+                "- `label_created_at`\n",
+                "- `data_row_ids`\n",
+                "- `global_keys`\n",
+                "- `batch_ids`\n",
+                "- `workflow_status`\n",
+                "\n",
+                "#### Filter details\n",
+                "You can set the range for `last_activity_at` and `label_created_at` in the following formats: \n",
+                "- `YYYY-MM-DD`\n",
+                "- `YYYY-MM-DD hh:mm:ss`\n",
+                "- `YYYY-MM-DDThh:mm:ss\u00b1hhmm` (ISO 8601)\n",
+                "\n",
+                "The ISO 8061 format allows you to specify the timezone, while the other two formats assume timezone from the user's workspace settings.\n",
+                "\n",
+                "The `last_activity_at` filter captures the creation and modification of labels, metadata, workflow status, comments, and reviews.\n",
+                "\n",
+                "If you wish to specify data rows to export, uncomment the `data_row_ids` or `global_keys` filter and provide a list of applicable IDs. The data rows must be part of a batch attached to the project in question. You can provide up to 2,000 data row IDs.\n",
+                "\n",
+                "The `batch_ids` filter allows you to specify data rows for export based on their associated batch ID. This is particularly useful when `data_row_ids` is not sufficient due to 2,000 data row IDs limit. \n",
+                "\n",
+                "\n",
+                "The `workflow_status` filter allows you to specify data rows in a given work flow step. This filter only accepts one value. For example, `filters = {\"workflow_status\": \"InReview\"}`. The filer accepts the following:\n",
+                "- `ToLabel`\n",
+                "- `InReview`\n",
+                "- `InRework`\n",
+                "- `Done`\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2 Method\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nexport_task = project.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Stream Task Export Method\n",
+                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+                "This allows streaming of task results and errors."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nclient.enable_experimental = True\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Uncomment to get stream results as a written file\n\n# Provide results with file converter\n\n# if export_task.has_errors():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./errors.txt\"),\n#         stream_type=lb.StreamType.ERRORS\n#     ).start()\n\n# if export_task.has_result():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./result.txt\"),\n#         stream_type=lb.StreamType.RESULT\n#     ).start()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a dataset\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+                "\n",
+                "### Parameters\n",
+                "When you export data rows from a dataset, you may choose to include or exclude certain attributes, including:\n",
+                "- `attachments`\n",
+                "- `metadata_fields`\n",
+                "- `data_row_details`\n",
+                "- `project_details`\n",
+                "- `label_details`\n",
+                "- `performance_details`\n",
+                "- `interpolated_frames`\n",
+                "    - Only applicable for video data rows.\n",
+                "- `project_ids`\n",
+                "    - Accepts a list of project IDs. If provided, the labels created _in these projects_ on the exported data rows will be included. \n",
+                "- `model_run_ids`\n",
+                "    - Accepts a list of model run IDs. If provided, the labels and predicitions created _in these model runs_ will be included. \n",
+                "\n",
+                "### Filters\n",
+                "When you export data rows from a dataset, you can specify the included data rows with the following filters:\n",
+                "- `last_activity_at`\n",
+                "- `label_created_at`\n",
+                "- `data_row_ids`\n",
+                "- `global_keys`\n",
+                "\n",
+                "See the _Export data rows from a project_ section above for additional details on each filter. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Insert the dataset ID of the dataset from which you wish to export data rows.\nDATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2 Method"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nexport_task = dataset.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Stream Task Export Method\n",
+                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+                "This allows streaming of task results and errors."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nclient.enable_experimental = True\n\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from Catalog\n",
+                "`get_catalog()` method allows you to fetch all your data from catalog\n",
+                "\n",
+                "### Parameters\n",
+                "When exporting from catalog, you can apply the same parameters as exporting from a project.\n",
+                "\n",
+                "### Filters\n",
+                "When exporting from catalog, you can apply the same filters as exporting from a dataset.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "catalog = client.get_catalog()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\nexport_task = catalog.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Stream Task Export Method\n",
+                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+                "This allows streaming of task results and errors."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\n\nclient.enable_experimental = True\nexport_task = catalog.export(params=export_params)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "def json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a slice\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+                "\n",
+                "### Parameters\n",
+                "When exporting from a slice, you can apply the same parameters as exporting from a dataset.\n",
+                "\n",
+                "### Filters\n",
+                "No filters are applicable to exports from a slice. All the data rows of the slice must be exported."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Insert the Catalog slice ID of the slice from which you wish to export data rows.\nCATALOG_SLICE_ID = \"\"\ncatalog_slice = client.get_catalog_slice(CATALOG_SLICE_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2 Method"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nexport_task = catalog_slice.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Stream Task Export Method\n",
+                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+                "This allows streaming of task results and errors."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nclient.enable_experimental = True\n\nexport_task = catalog_slice.export(params=export_params)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export data rows from a model run\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+                "\n",
+                "### Parameters\n",
+                "- `attachments`\n",
+                "- `metadata_fields`\n",
+                "- `data_row_details`\n",
+                "- `interpolated_frames`\n",
+                "    - Only applicable for video data rows.\n",
+                "- `predictions`\n",
+                "    - If true, all predictions made in the model run will be included for each data row in the export.\n",
+                "\n",
+                "### Filters\n",
+                "No filters are applicable to exports from a model run. All the data rows of the model run must be exported.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Insert the model run ID of the model run from which you wish to export data rows.\nMODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2 Method"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nexport_task = model_run.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Stream Task Export Method\n",
+                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+                "This allows streaming of task results and errors."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Export Data Row\n",
+                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+                "\n",
+                "### Parameters\n",
+                "When exporting data rows, you can apply the same parameters as exporting from a project.\n",
+                "\n",
+                "### Filters\n",
+                "No filters are applicable to export data rows. All the data rows specified in the export task are included."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Insert the global key of the data row you wish to export\nDATA_ROW_GLOBAL_KEY = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export V2 Method"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export_v2(client=client,\n                                   global_keys=[DATA_ROW_GLOBAL_KEY],\n                                   params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Stream Task Export Method\n",
+                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+                "This allows streaming of task results and errors."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export(client=client,\n                                global_keys=[DATA_ROW_GLOBAL_KEY],\n                                params=export_params)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
index 0c45acf52..c1d7188ad 100644
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ b/examples/extras/classification_confusion_matrix.ipynb
@@ -1,228 +1,109 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification-confusion-matrix.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification-confusion-matrix.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Classification Confusion matrix\n",
-        "\n",
-        "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
-        "\n",
-        "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
-        "\n",
-        "## Installs"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q 'labelbox[data]' scikit-learn matplotlib ipywidgets"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Imports"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Imports and etc\n",
-        "\n",
-        "from sklearn.metrics import ConfusionMatrixDisplay\n",
-        "from sklearn.metrics import classification_report\n",
-        "import matplotlib.pyplot as plt\n",
-        "import ipywidgets as widgets\n",
-        "import labelbox as lb\n",
-        "\n",
-        "def get_labelers():\n",
-        "    labels = project.label_generator()\n",
-        "    labelers = []\n",
-        "\n",
-        "    for l in labels:\n",
-        "        if not l.extra['Created By'] in labelers:\n",
-        "            labelers.append(l.extra['Created By'])\n",
-        "\n",
-        "    return labelers\n",
-        "\n",
-        "\n",
-        "def get_paired_labels(labeler1, labeler2):\n",
-        "    labels = project.label_generator()\n",
-        "    l1 = []\n",
-        "    l2 = []\n",
-        "    label_dict = {}\n",
-        "\n",
-        "    for l in labels:\n",
-        "        if not l.data.uid in label_dict:\n",
-        "            label_dict[l.data.uid] = {}\n",
-        "\n",
-        "        # Check if benchmark\n",
-        "        if l.extra['Benchmark ID']:\n",
-        "            label_dict[l.data.uid]['Benchmarks'] = l.annotations[0].value.answer.name\n",
-        "\n",
-        "        # Skip labels not created by either of our labelers\n",
-        "        if l.extra['Created By'] != labeler1 and l.extra[\n",
-        "                'Created By'] != labeler2:\n",
-        "            continue\n",
-        "\n",
-        "        label_dict[l.data.uid][l.extra['Created By']] = \\\n",
-        "            l.annotations[0].value.answer.name\n",
-        "\n",
-        "    for uid in label_dict.keys():\n",
-        "        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n",
-        "            l1.append(label_dict[uid][labeler1])\n",
-        "            l2.append(label_dict[uid][labeler2])\n",
-        "\n",
-        "    return l1, l2\n",
-        "\n",
-        "\n",
-        "def get_label_dict():\n",
-        "    labels = project.label_generator()\n",
-        "    labelers = []\n",
-        "    label_dict = {}\n",
-        "\n",
-        "    for l in labels:\n",
-        "        if not l.data.uid in label_dict:\n",
-        "            label_dict[l.data.uid] = {}\n",
-        "\n",
-        "        label_dict[l.data.uid][l.extra['Created By']] = \\\n",
-        "            l.annotations[0].value.answer.name\n",
-        "\n",
-        "        if not l.extra['Created By'] in labelers:\n",
-        "            labelers.append(l.extra['Created By'])\n",
-        "\n",
-        "    print(labelers)\n",
-        "    return label_dict, labelers"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Labelbox API + project and dataset\n",
-        "\n",
-        "# Add your api key\n",
-        "API_KEY = None\n",
-        "lb = lb.Client(api_key=API_KEY)\n",
-        "\n",
-        "# Add your project and dataset uids here\n",
-        "project = lb.get_project('xxx')\n",
-        "dataset = lb.get_dataset('xxx')"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Labeler Selection"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Selectors\n",
-        "\n",
-        "labelers = get_labelers()\n",
-        "\n",
-        "items = [\n",
-        "    widgets.Dropdown(\n",
-        "        options=['Benchmarks'] + labelers,\n",
-        "        description='Compare this:',\n",
-        "        disabled=False,\n",
-        "    ),\n",
-        "    widgets.Dropdown(\n",
-        "        options=labelers,\n",
-        "        description='Against this:',\n",
-        "        disabled=False,\n",
-        "    )]\n",
-        "\n",
-        "\n",
-        "widgets.VBox(items)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Confusion Matrix"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Plot Matrix\n",
-        "\n",
-        "l1, l2 = get_paired_labels(items[0].value, items[1].value)\n",
-        "\n",
-        "fig, ax = plt.subplots(figsize=(15,15))\n",
-        "c = ConfusionMatrixDisplay.from_predictions(l1, l2,\n",
-        "                                        labels=list(set(l1)), sample_weight=None,\n",
-        "                                        normalize=None, display_labels=None, include_values=True,\n",
-        "                                        xticks_rotation='vertical', values_format=None,\n",
-        "                                        cmap='Blues', ax=ax, colorbar=False)\n",
-        "\n",
-        "_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n",
-        "\n",
-        "cr = classification_report(\n",
-        "    l1,\n",
-        "    l2,\n",
-        "    output_dict=False,\n",
-        "    target_names=list(set(l1)),\n",
-        "    zero_division=1\n",
-        ")\n",
-        "\n",
-        "print(cr)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Classification Confusion matrix\n",
+                "\n",
+                "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
+                "\n",
+                "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
+                "\n",
+                "## Installs"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q 'labelbox[data]' scikit-learn matplotlib ipywidgets",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Imports"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Imports and etc\n\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.metrics import classification_report\nimport matplotlib.pyplot as plt\nimport ipywidgets as widgets\nimport labelbox as lb\n\n\ndef get_labelers():\n    labels = project.label_generator()\n    labelers = []\n\n    for l in labels:\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    return labelers\n\n\ndef get_paired_labels(labeler1, labeler2):\n    labels = project.label_generator()\n    l1 = []\n    l2 = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        # Check if benchmark\n        if l.extra[\"Benchmark ID\"]:\n            label_dict[\n                l.data.uid][\"Benchmarks\"] = l.annotations[0].value.answer.name\n\n        # Skip labels not created by either of our labelers\n        if (l.extra[\"Created By\"] != labeler1 and\n                l.extra[\"Created By\"] != labeler2):\n            continue\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n    for uid in label_dict.keys():\n        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n            l1.append(label_dict[uid][labeler1])\n            l2.append(label_dict[uid][labeler2])\n\n    return l1, l2\n\n\ndef get_label_dict():\n    labels = project.label_generator()\n    labelers = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    print(labelers)\n    return label_dict, labelers",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Labelbox API + project and dataset\n\n# Add your api key\nAPI_KEY = None\nlb = lb.Client(api_key=API_KEY)\n\n# Add your project and dataset uids here\nproject = lb.get_project(\"xxx\")\ndataset = lb.get_dataset(\"xxx\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Labeler Selection"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Selectors\n\nlabelers = get_labelers()\n\nitems = [\n    widgets.Dropdown(\n        options=[\"Benchmarks\"] + labelers,\n        description=\"Compare this:\",\n        disabled=False,\n    ),\n    widgets.Dropdown(\n        options=labelers,\n        description=\"Against this:\",\n        disabled=False,\n    ),\n]\n\nwidgets.VBox(items)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Confusion Matrix"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Plot Matrix\n\nl1, l2 = get_paired_labels(items[0].value, items[1].value)\n\nfig, ax = plt.subplots(figsize=(15, 15))\nc = ConfusionMatrixDisplay.from_predictions(\n    l1,\n    l2,\n    labels=list(set(l1)),\n    sample_weight=None,\n    normalize=None,\n    display_labels=None,\n    include_values=True,\n    xticks_rotation=\"vertical\",\n    values_format=None,\n    cmap=\"Blues\",\n    ax=ax,\n    colorbar=False,\n)\n\n_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n\ncr = classification_report(l1,\n                           l2,\n                           output_dict=False,\n                           target_names=list(set(l1)),\n                           zero_division=1)\n\nprint(cr)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/foundry/object_detection.ipynb b/examples/foundry/object_detection.ipynb
index a7de826db..a477d2541 100644
--- a/examples/foundry/object_detection.ipynb
+++ b/examples/foundry/object_detection.ipynb
@@ -1,351 +1,260 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=190/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Foundry overview\n",
-        "\n",
-        "This notebook is used to go over the basic of foundry through the Python SDK"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:\n",
-        "\n",
-        "* Predict (infer) labels from your data\n",
-        "* Compare the performance of different foundational models with your data and ontologies.\n",
-        "* Prototype, diagnose, and refine a machine learning app to solve specific business needs.\n",
-        "\n",
-        "Foundry creates model runs that predict data row annotations based on your input."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q labelbox"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from labelbox.schema.conflict_resolution_strategy import ConflictResolutionStrategy\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "\n",
-        "Provide a valid API key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# End-to-end example: Run foundry and send to annotate from catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into catelog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as data row for a dataset\n",
-        "global_key = str(uuid.uuid4())\n",
-        "\n",
-        "test_img_url = {\n",
-        "    \"row_data\":\n",
-        "        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n",
-        "    \"global_key\":\n",
-        "        global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"foundry-demo-dataset\")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an ontology that matches model\n",
-        "\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your model and data type.\n",
-        "\n",
-        "For example, when using Amazon Rekognition you would need to create a bounding box annotation for your ontology since it only supports object detection. Likewise when using YOLOv8 you would need to create a classification annotation for your ontology since it only supports image classification. \n",
-        "\n",
-        "In this tutorial, we will use Amazon Rekognition to detect objects in an image dataset. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create ontology with two bounding boxes that is included with Amazon Rekognition: Car and Person\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    classifications=[],\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Car\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Person\")\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Image Bounding Box Annotation Demo Foundry\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Image)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a labeling project\n",
-        "\n",
-        "Connect the ontology to the labeling project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(name=\"Foundry Image Demo\",\n",
-        "                                media_type=lb.MediaType.Image)\n",
-        "\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Create foundry application in UI\n",
-        "\n",
-        "Currently we do not support this workflow through the SDK\n",
-        "#### Workflow:\n",
-        "\n",
-        "1. Navigate to model and select ***Create*** > ***App***\n",
-        "\n",
-        "2. Select ***Amazon Rekognition*** and name your foundry application\n",
-        "\n",
-        "3. Customize your perimeters and then select ***Save & Create***"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#Select your foundry application inside the UI and copy the APP ID from the top right corner\n",
-        "AMAZON_REKOGNITION_APP_ID = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Run foundry app on data rows\n",
-        "\n",
-        "This step is meant to generate annotations that can later be reused as pre-labels in a project. You must provide your app ID from the previous step for this method to run, please see the [Foundry Apps Guide](https://docs.labelbox.com/docs/foundry-apps#run-app-using-sdk) for more information.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "task = client.run_foundry_app(model_run_name=f\"Amazon-{str(uuid.uuid4())}\",\n",
-        "                              data_rows=lb.GlobalKeys(\n",
-        "                                  [global_key] # Provide a list of global keys\n",
-        "                                  ),\n",
-        "                              app_id=AMAZON_REKOGNITION_APP_ID)\n",
-        "\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "#Obtain model run ID from task\n",
-        "MODEL_RUN_ID = task.metadata[\"modelRunId\"]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6: Map ontology through the UI\n",
-        "\n",
-        "Mapping a model's ontology to a project's ontology is currently not supported through the SDK, however, to showcase how to send foundry predictions to a project, we are going to generate the mapping of the foundry app ontology to the project ontology through the UI.\n",
-        "\n",
-        "#### Workflow\n",
-        "\n",
-        "1. Navigate to your dataset you created for your model run\n",
-        "2. Select ***Select all*** in the top right corner\n",
-        "3. Select ***Manage selection*** > ***Send to Annotate***\n",
-        "4. Specify the project we created from the project dropdown menu\n",
-        "5. Selecting a workflow step is not required since we are not sending annotations from the UI to a project using this notebook \n",
-        "6. Mark ***Include model predictions*** then scroll down and select ***Map***\n",
-        "7. Select the incoming ontology and matching ontology feature for both Car and Person\n",
-        "8. Once both features are mapped press the ***Copy ontology mapping as JSON*** in the top right corner\n",
-        "9. Do not save this configuration, since we are not sending predictions to a project using this UI modal. We will be sending predictions in the following steps using the SDK"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Copy map ontology through the UI then paste JSON file here\n",
-        "PREDICTIONS_ONTOLOGY_MAPPING = {}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send model generated annotations from catalog to annotate\n",
-        "\n",
-        "### Parameters\n",
-        "\n",
-        "When you send predicted data rows to annotate from catalog, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
-        "\n",
-        "* `predictions_ontology_mapping`\n",
-        "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
-        "* `exclude_data_rows_in_project`\n",
-        "    - Excludes data rows that are already in the project. \n",
-        "* `override_existing_annotations_rule` \n",
-        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
-        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-        "        * Options include:\n",
-        "            * ConflictResolutionStrategy.KeepExisting\n",
-        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-        "* `param batch_priority`\n",
-        "    - The priority of the batch.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run = client.get_model_run(MODEL_RUN_ID)\n",
-        "\n",
-        "send_to_annotations_params = {\n",
-        "    \"predictions_ontology_mapping\": PREDICTIONS_ONTOLOGY_MAPPING,\n",
-        "    \"exclude_data_rows_in_project\": False,\n",
-        "    \"override_existing_annotations_rule\": ConflictResolutionStrategy.OverrideWithPredictions,\n",
-        "    \"batch_priority\": 5,\n",
-        "}\n",
-        "\n",
-        "\n",
-        "task = model_run.send_to_annotate_from_model(\n",
-        "    destination_project_id=project.uid,\n",
-        "    task_queue_id=None, #ID of workflow task, set ID to None if you want to convert pre-labels to ground truths or obtain task queue id through project.task_queues().\n",
-        "    batch_name=\"Foundry Demo Batch\",\n",
-        "    data_rows=lb.GlobalKeys(\n",
-        "        [global_key] # Provide a list of global keys from foundry app task\n",
-        "    ),\n",
-        "    params=send_to_annotations_params\n",
-        "    )\n",
-        "\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Clean up"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()\n",
-        "# model_run.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [],
-      "cell_type": "markdown"
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Foundry overview\n",
+                "\n",
+                "This notebook is used to go over the basic of foundry through the Python SDK"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:\n",
+                "\n",
+                "* Predict (infer) labels from your data\n",
+                "* Compare the performance of different foundational models with your data and ontologies.\n",
+                "* Prototype, diagnose, and refine a machine learning app to solve specific business needs.\n",
+                "\n",
+                "Foundry creates model runs that predict data row annotations based on your input."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q labelbox",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "\n",
+                "Provide a valid API key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# End-to-end example: Run foundry and send to annotate from catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into catelog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an ontology that matches model\n",
+                "\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your model and data type.\n",
+                "\n",
+                "For example, when using Amazon Rekognition you would need to create a bounding box annotation for your ontology since it only supports object detection. Likewise when using YOLOv8 you would need to create a classification annotation for your ontology since it only supports image classification. \n",
+                "\n",
+                "In this tutorial, we will use Amazon Rekognition to detect objects in an image dataset. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create ontology with two bounding boxes that is included with Amazon Rekognition: Car and Person\nontology_builder = lb.OntologyBuilder(\n    classifications=[],\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Car\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Person\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Bounding Box Annotation Demo Foundry\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a labeling project\n",
+                "\n",
+                "Connect the ontology to the labeling project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(name=\"Foundry Image Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Create foundry application in UI\n",
+                "\n",
+                "Currently we do not support this workflow through the SDK\n",
+                "#### Workflow:\n",
+                "\n",
+                "1. Navigate to model and select ***Create*** > ***App***\n",
+                "\n",
+                "2. Select ***Amazon Rekognition*** and name your foundry application\n",
+                "\n",
+                "3. Customize your perimeters and then select ***Save & Create***"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Select your foundry application inside the UI and copy the APP ID from the top right corner\nAMAZON_REKOGNITION_APP_ID = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Run foundry app on data rows\n",
+                "\n",
+                "This step is meant to generate annotations that can later be reused as pre-labels in a project. You must provide your app ID from the previous step for this method to run, please see the [Foundry Apps Guide](https://docs.labelbox.com/docs/foundry-apps#run-app-using-sdk) for more information.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "task = client.run_foundry_app(\n    model_run_name=f\"Amazon-{str(uuid.uuid4())}\",\n    data_rows=lb.GlobalKeys([global_key]),  # Provide a list of global keys\n    app_id=AMAZON_REKOGNITION_APP_ID,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\n\n# Obtain model run ID from task\nMODEL_RUN_ID = task.metadata[\"modelRunId\"]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6: Map ontology through the UI\n",
+                "\n",
+                "Mapping a model's ontology to a project's ontology is currently not supported through the SDK, however, to showcase how to send foundry predictions to a project, we are going to generate the mapping of the foundry app ontology to the project ontology through the UI.\n",
+                "\n",
+                "#### Workflow\n",
+                "\n",
+                "1. Navigate to your dataset you created for your model run\n",
+                "2. Select ***Select all*** in the top right corner\n",
+                "3. Select ***Manage selection*** > ***Send to Annotate***\n",
+                "4. Specify the project we created from the project dropdown menu\n",
+                "5. Selecting a workflow step is not required since we are not sending annotations from the UI to a project using this notebook \n",
+                "6. Mark ***Include model predictions*** then scroll down and select ***Map***\n",
+                "7. Select the incoming ontology and matching ontology feature for both Car and Person\n",
+                "8. Once both features are mapped press the ***Copy ontology mapping as JSON*** in the top right corner\n",
+                "9. Do not save this configuration, since we are not sending predictions to a project using this UI modal. We will be sending predictions in the following steps using the SDK"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Copy map ontology through the UI then paste JSON file here\nPREDICTIONS_ONTOLOGY_MAPPING = {}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send model generated annotations from catalog to annotate\n",
+                "\n",
+                "### Parameters\n",
+                "\n",
+                "When you send predicted data rows to annotate from catalog, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
+                "\n",
+                "* `predictions_ontology_mapping`\n",
+                "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
+                "* `exclude_data_rows_in_project`\n",
+                "    - Excludes data rows that are already in the project. \n",
+                "* `override_existing_annotations_rule` \n",
+                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
+                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+                "        * Options include:\n",
+                "            * ConflictResolutionStrategy.KeepExisting\n",
+                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+                "* `param batch_priority`\n",
+                "    - The priority of the batch.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run = client.get_model_run(MODEL_RUN_ID)\n\nsend_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    None,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths or obtain task queue id through project.task_queues().\n    batch_name=\"Foundry Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Clean up"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [],
+            "cell_type": "markdown"
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index 6e3929b12..29d6acdf2 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -1,230 +1,152 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Install required libraries"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox\"\n",
-        "!pip install -q transformers"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Imports"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import transformers\n",
-        "transformers.logging.set_verbosity(50)\n",
-        "import torch\n",
-        "import torch.nn.functional as F\n",
-        "from PIL import Image\n",
-        "import requests\n",
-        "from tqdm import tqdm\n",
-        "import numpy as np"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Labelbox Credentials"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Select data rows in Labelbox for custom embeddings"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get images from a Labelbox dataset,\n",
-        "# Ensure the images are available by obtaining a token from your cloud provider if necessary\n",
-        "DATASET_ID = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset = client.get_dataset(DATASET_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "export_task = dataset.export_v2()\n",
-        "\n",
-        "export_task.wait_till_done()\n",
-        "if export_task.errors:\n",
-        "\tprint(export_task.errors)\n",
-        "export_json = export_task.result\n",
-        "\n",
-        "data_row_urls = [dr_url['data_row']['row_data'] for dr_url in export_json]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get a HuggingFace Model to generate custom embeddings"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get ResNet-50 from HuggingFace\n",
-        "image_processor = transformers.AutoImageProcessor.from_pretrained(\"microsoft/resnet-50\")\n",
-        "model = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\n",
-        "new_custom_embedding_id = client.create_embedding(name=\"My new awesome embedding\", dims=2048).id\n",
-        "\n",
-        "# Or use an existing embedding from your workspace\n",
-        "# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Generate and upload custom embeddings"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "img_emb = []\n",
-        "\n",
-        "for url in tqdm(data_row_urls):\n",
-        "    try:\n",
-        "        response = requests.get(url, stream=True)\n",
-        "        if response.status_code == 200:\n",
-        "            # Open the image, convert to RGB, and resize to 224x224\n",
-        "            image = Image.open(response.raw).convert('RGB').resize((224, 224))\n",
-        "\n",
-        "            # Preprocess the image for model input\n",
-        "            img_hf = image_processor(image, return_tensors=\"pt\")\n",
-        "\n",
-        "            # Pass the image through the model to get embeddings\n",
-        "            with torch.no_grad():\n",
-        "                last_layer = model(**img_hf, output_hidden_states=True).last_hidden_state\n",
-        "                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n",
-        "                resnet_embeddings = torch.flatten(resnet_embeddings, start_dim=1, end_dim=3)\n",
-        "                img_emb.append(resnet_embeddings.cpu().numpy())\n",
-        "        else:\n",
-        "            continue\n",
-        "    except Exception as e:\n",
-        "        print(f\"Error processing URL: {url}. Exception: {e}\")\n",
-        "        continue\n",
-        "\n",
-        "data_rows = []\n",
-        "    \n",
-        "# Create data rows payload to send to a dataset\n",
-        "for url, embedding in tqdm(zip(data_row_urls, img_emb)):\n",
-        "    data_rows.append({\n",
-        "        \"row_data\": url,\n",
-        "        \"embeddings\": [{\"embedding_id\": new_custom_embedding_id, \"vector\": embedding[0].tolist()}]\n",
-        "    })"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload to a new dataset\n",
-        "dataset = client.create_dataset(name='image_custom_embedding_resnet', iam_integration=None)\n",
-        "task = dataset.create_data_rows(data_rows)\n",
-        "print(task.errors)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Install required libraries"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox\"\n%pip install -q transformers",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Imports"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Labelbox Credentials"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Select data rows in Labelbox for custom embeddings"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "dataset = client.get_dataset(DATASET_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Get a HuggingFace Model to generate custom embeddings"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Generate and upload custom embeddings"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/integrations/langchain/langchain.ipynb b/examples/integrations/langchain/langchain.ipynb
index 17c54110b..60c9fb099 100644
--- a/examples/integrations/langchain/langchain.ipynb
+++ b/examples/integrations/langchain/langchain.ipynb
@@ -1,392 +1,178 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# LangChain -> Labelbox\n",
-        "This notebook is used to show an example workflow of getting LangChain traces into Labelbox conversation data format. Please review the [associated written guide](https://labelbox.com/guides/turn-langchain-logs-into-conversational-data-with-labelbox/) for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install --upgrade --quiet  langchain langsmith langchainhub\n",
-        "!pip install --upgrade --quiet  langchain-openai tiktoken pandas duckduckgo-search\n",
-        "!pip install --upgrade --quiet  \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "from uuid import uuid4\n",
-        "import os\n",
-        "import functools\n",
-        "\n",
-        "# LangSmith Imports\n",
-        "from langsmith.client import Client\n",
-        "from langchain import hub\n",
-        "from langchain.agents import AgentExecutor\n",
-        "from langchain.agents.format_scratchpad.openai_tools import (\n",
-        "    format_to_openai_tool_messages,\n",
-        ")\n",
-        "from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser\n",
-        "from langchain_community.tools import DuckDuckGoSearchResults\n",
-        "from langchain_openai import ChatOpenAI\n",
-        "from langsmith.evaluation import EvaluationResult\n",
-        "from langsmith.schemas import Example, Run, DataType\n",
-        "from langchain.smith import run_on_dataset\n",
-        "from langchain.evaluation import EvaluatorType\n",
-        "from langchain.smith import RunEvalConfig\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## API Key and Setup\n",
-        "Provide a valid API key below for Labelbox, LangSmith and OpenAI in order for the notebook to work correctly."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "LB_API_KEY = \"\"\n",
-        "LS_API_KEY = \"\"\n",
-        "OPENAI_API_KEY = \"\"\n",
-        "\n",
-        "unique_id = uuid4().hex[0:8]\n",
-        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-        "os.environ[\"LANGCHAIN_PROJECT\"] = f\"Tracing Walkthrough - {unique_id}\"\n",
-        "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
-        "os.environ[\"LANGCHAIN_API_KEY\"] = LS_API_KEY\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
-        "\n",
-        "lb_client = lb.Client(LB_API_KEY)\n",
-        "client = Client()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### LangSmith Dataset Name\n",
-        "Create a sample chat data set with an example chat based run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset_name = f\"Sample Dataset - {str(uuid4())}\"\n",
-        "dataset = client.create_dataset(dataset_name, description=\"A sample dataset in LangSmith.\", data_type=DataType.chat)\n",
-        "client.create_chat_example(\n",
-        "    messages=[{\"type\": \"ai\", \"data\": {\"content\": \"hi how are you\"}}, {\"type\": \"human\", \"data\": {\"content\": \"Im doing great how about you\"}}],\n",
-        "    generations={\"type\": \"ai\", \"data\": {\"content\": \"Im doing great\"}}, # Custom model output\n",
-        "    dataset_id=dataset.id,\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### LangSmith\n",
-        "Below is an example of running a list of raw text evaluation strings and a LangSmith example run with Chat Gpt 3.5. Please review [LangSmith Docs](https://docs.smith.langchain.com/) for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "tools = [\n",
-        "    DuckDuckGoSearchResults(\n",
-        "        name=\"duck_duck_go\"\n",
-        "    ),  # General internet search using DuckDuckGo\n",
-        "]\n",
-        "\n",
-        "llm = ChatOpenAI(\n",
-        "    model=\"gpt-3.5-turbo-16k\",\n",
-        "    temperature=0,\n",
-        ")\n",
-        "\n",
-        "# Since chains can be stateful (e.g. they can have memory), we provide\n",
-        "# a way to initialize a new chain for each row in the dataset. This is done\n",
-        "# by passing in a factory function that returns a new chain for each row.\n",
-        "def create_agent(prompt, llm_with_tools):\n",
-        "    runnable_agent = (\n",
-        "        {\n",
-        "            \"input\": lambda x: x[\"input\"],\n",
-        "            \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
-        "                x[\"intermediate_steps\"]\n",
-        "            ),\n",
-        "        }\n",
-        "        | prompt\n",
-        "        | llm_with_tools\n",
-        "        | OpenAIToolsAgentOutputParser()\n",
-        "    )\n",
-        "    return AgentExecutor(agent=runnable_agent, tools=tools, handle_parsing_errors=True)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "def max_pred_length(runs, examples):\n",
-        "    predictions = [len(run.outputs[\"output\"]) for run in runs]\n",
-        "    return EvaluationResult(key=\"max_pred_length\", score=max(predictions))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "def check_not_idk(run: Run, example: Example):\n",
-        "    \"\"\"Illustration of a custom evaluator.\"\"\"\n",
-        "    agent_response = run.outputs[\"output\"]\n",
-        "    if \"don't know\" in agent_response or \"not sure\" in agent_response:\n",
-        "        score = 0\n",
-        "    else:\n",
-        "        score = 1\n",
-        "    # You can access the dataset labels in example.outputs[key]\n",
-        "    # You can also access the model inputs in run.inputs[key]\n",
-        "    return EvaluationResult(\n",
-        "        key=\"not_uncertain\",\n",
-        "        score=score,\n",
-        "    )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "evaluation_config = RunEvalConfig(\n",
-        "    # Evaluators can either be an evaluator type (e.g., \"qa\", \"criteria\", \"embedding_distance\", etc.) or a configuration for that evaluator\n",
-        "    evaluators=[\n",
-        "        check_not_idk,\n",
-        "        # Measures whether a QA response is \"Correct\", based on a reference answer\n",
-        "        # You can also select via the raw string \"qa\"\n",
-        "        EvaluatorType.QA,\n",
-        "        # Measure the embedding distance between the output and the reference answer\n",
-        "        # Equivalent to: EvalConfig.EmbeddingDistance(embeddings=OpenAIEmbeddings())\n",
-        "        EvaluatorType.EMBEDDING_DISTANCE,\n",
-        "        # Grade whether the output satisfies the stated criteria.\n",
-        "        # You can select a default one such as \"helpfulness\" or provide your own.\n",
-        "        RunEvalConfig.LabeledCriteria(\"helpfulness\"),\n",
-        "        # The LabeledScoreString evaluator outputs a score on a scale from 1-10.\n",
-        "        # You can use default criteria or write our own rubric\n",
-        "        RunEvalConfig.LabeledScoreString(\n",
-        "            {\n",
-        "                \"accuracy\": \"\"\"\n",
-        "Score 1: The answer is completely unrelated to the reference.\n",
-        "Score 3: The answer has minor relevance but does not align with the reference.\n",
-        "Score 5: The answer has moderate relevance but contains inaccuracies.\n",
-        "Score 7: The answer aligns with the reference but has minor errors or omissions.\n",
-        "Score 10: The answer is completely accurate and aligns perfectly with the reference.\"\"\"\n",
-        "            },\n",
-        "            normalize_by=10,\n",
-        "        ),\n",
-        "    ],\n",
-        "    batch_evaluators=[max_pred_length],\n",
-        ")\n",
-        "\n",
-        "llm_with_tools = llm.bind_tools(tools)\n",
-        "prompt = hub.pull(\"gabe/labelboxtutorialdemo\") # Change prompt in LangSmith hub to reflect example run\n",
-        "\n",
-        "chain_results = run_on_dataset(\n",
-        "    dataset_name=dataset_name,\n",
-        "    llm_or_chain_factory=functools.partial(\n",
-        "        create_agent, prompt=prompt, llm_with_tools=llm_with_tools\n",
-        "    ),\n",
-        "    evaluation=evaluation_config,\n",
-        "    verbose=True,\n",
-        "    client=client,\n",
-        "    project_name=f\"tools-agent-test-5d466cbc-{unique_id}\",\n",
-        "    # Project metadata communicates the experiment parameters,\n",
-        "    # Useful for reviewing the test results\n",
-        "    project_metadata={\n",
-        "        \"env\": \"testing-notebook\",\n",
-        "        \"model\": \"gpt-3.5-turbo\",\n",
-        "        \"prompt\": \"5d466cbc\",\n",
-        "    },\n",
-        ")\n",
-        "\n",
-        "# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.\n",
-        "# These are logged as warnings here and captured as errors in the tracing UI."
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Labelbox\n",
-        "Below converts the results of the above LangSmith run to Labelbox conversation text. Please review [Labelbox conversation data docs](https://docs.labelbox.com/docs/llm-human-preference) for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "def import_conversational (chain_results: dict[str:str], user_id_dict: dict[str:dict[str:str]]) -> dict[str:str]:\n",
-        "  '''Converts LangSmith chain_results from model invocation to Labelbox conversation data for model response comparison. Output is based on popular model response and custom model response towards prompts.\n",
-        "\n",
-        "  Args:\n",
-        "    chain_results(dict[str:str]): Results from LangSmith model invocation against example dataset runs.\n",
-        "    user_id_dict(dict[str:dict[str:str]]): Dictionary mapping of LangSmith example run type to Labelbox chat names and alignment.\n",
-        "\n",
-        "  Returns:\n",
-        "      dict[str:str]: Labelbox conversation text format\n",
-        "  '''\n",
-        "  lb_conversations = []\n",
-        "  for key, conversational in chain_results[\"results\"].items():\n",
-        "    lb_conversation =   {\n",
-        "    \"row_data\": {\n",
-        "      \"type\": \"application/vnd.labelbox.conversational\",\n",
-        "      \"version\": 1,\n",
-        "      \"messages\": [],\n",
-        "      \"modelOutputs\": []\n",
-        "    },\n",
-        "    \"global_key\": key,\n",
-        "    \"media_type\": \"CONVERSATIONAL\",\n",
-        "    }\n",
-        "    if \"input\" in conversational[\"output\"]:\n",
-        "      for i, input in enumerate(conversational[\"output\"][\"input\"]):\n",
-        "        lb_conversation[\"row_data\"][\"messages\"].append({\n",
-        "            \"content\": input[\"data\"][\"content\"],\n",
-        "            \"timestampUsec\": i + 1,\n",
-        "            \"user\": {\n",
-        "                \"userId\": user_id_dict[input[\"type\"]][\"id\"],\n",
-        "                \"name\": input[\"type\"]\n",
-        "            },\n",
-        "            \"canLabel\": True,\n",
-        "            \"align\": user_id_dict[input[\"type\"]][\"align\"],\n",
-        "            \"messageId\": str(uuid4())\n",
-        "        })\n",
-        "\n",
-        "    # Custom model output\n",
-        "    if \"reference\" in conversational:\n",
-        "      reference = conversational[\"reference\"][\"output\"]\n",
-        "      lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n",
-        "          \"title\": \"Custom Model Response\",\n",
-        "          \"content\": reference[\"data\"][\"content\"],\n",
-        "          \"modelConfigName\": \"Custom Model - Example Config\"\n",
-        "          })\n",
-        "\n",
-        "    # Popular model output\n",
-        "    if \"output\" in conversational[\"output\"]:\n",
-        "      output = conversational[\"output\"][\"output\"]\n",
-        "      lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n",
-        "          \"title\": \"Popular LLM Response\",\n",
-        "          \"content\": output,\n",
-        "          \"modelConfigName\": \"GPT-3.5 - Example Config\"\n",
-        "          })\n",
-        "\n",
-        "\n",
-        "    lb_conversations.append(lb_conversation)\n",
-        "  return lb_conversations"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Labelbox Dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "dataset = lb_client.create_dataset(name=\"demo_langchain\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Attach Conversation Text to Dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "task = dataset.create_data_rows(import_conversational(chain_results, {\"human\": {\"id\":\"human\",\"align\": \"right\"}, \"ai\":{\"id\":\"ai\", \"align\": \"left\"}}))\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(task.errors)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# LangChain -> Labelbox\n",
+                "This notebook is used to show an example workflow of getting LangChain traces into Labelbox conversation data format. Please review the [associated written guide](https://labelbox.com/guides/turn-langchain-logs-into-conversational-data-with-labelbox/) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install --upgrade --quiet  langchain langsmith langchainhub\n%pip install --upgrade --quiet  langchain-openai tiktoken pandas duckduckgo-search\n%pip install --upgrade --quiet  \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nfrom uuid import uuid4\nimport os\nimport functools\n\n# LangSmith Imports\nfrom langsmith.client import Client\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor\nfrom langchain.agents.format_scratchpad.openai_tools import (\n    format_to_openai_tool_messages,)\nfrom langchain.agents.output_parsers.openai_tools import (\n    OpenAIToolsAgentOutputParser,)\nfrom langchain_community.tools import DuckDuckGoSearchResults\nfrom langchain_openai import ChatOpenAI\nfrom langsmith.evaluation import EvaluationResult\nfrom langsmith.schemas import Example, Run, DataType\nfrom langchain.smith import run_on_dataset\nfrom langchain.evaluation import EvaluatorType\nfrom langchain.smith import RunEvalConfig",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API Key and Setup\n",
+                "Provide a valid API key below for Labelbox, LangSmith and OpenAI in order for the notebook to work correctly."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "LB_API_KEY = \"\"\nLS_API_KEY = \"\"\nOPENAI_API_KEY = \"\"\n\nunique_id = uuid4().hex[0:8]\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = f\"Tracing Walkthrough - {unique_id}\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = LS_API_KEY\n\nos.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n\nlb_client = lb.Client(LB_API_KEY)\nclient = Client()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### LangSmith Dataset Name\n",
+                "Create a sample chat data set with an example chat based run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "dataset_name = f\"Sample Dataset - {str(uuid4())}\"\ndataset = client.create_dataset(\n    dataset_name,\n    description=\"A sample dataset in LangSmith.\",\n    data_type=DataType.chat,\n)\nclient.create_chat_example(\n    messages=[\n        {\n            \"type\": \"ai\",\n            \"data\": {\n                \"content\": \"hi how are you\"\n            }\n        },\n        {\n            \"type\": \"human\",\n            \"data\": {\n                \"content\": \"Im doing great how about you\"\n            }\n        },\n    ],\n    generations={\n        \"type\": \"ai\",\n        \"data\": {\n            \"content\": \"Im doing great\"\n        },\n    },  # Custom model output\n    dataset_id=dataset.id,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### LangSmith\n",
+                "Below is an example of running a list of raw text evaluation strings and a LangSmith example run with Chat Gpt 3.5. Please review [LangSmith Docs](https://docs.smith.langchain.com/) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "tools = [\n    DuckDuckGoSearchResults(\n        name=\"duck_duck_go\"),  # General internet search using DuckDuckGo\n]\n\nllm = ChatOpenAI(\n    model=\"gpt-3.5-turbo-16k\",\n    temperature=0,\n)\n\n\n# Since chains can be stateful (e.g. they can have memory), we provide\n# a way to initialize a new chain for each row in the dataset. This is done\n# by passing in a factory function that returns a new chain for each row.\ndef create_agent(prompt, llm_with_tools):\n    runnable_agent = ({\n        \"input\":\n            lambda x: x[\"input\"],\n        \"agent_scratchpad\":\n            lambda x: format_to_openai_tool_messages(x[\"intermediate_steps\"]),\n    } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser())\n    return AgentExecutor(agent=runnable_agent,\n                         tools=tools,\n                         handle_parsing_errors=True)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "def max_pred_length(runs, examples):\n    predictions = [len(run.outputs[\"output\"]) for run in runs]\n    return EvaluationResult(key=\"max_pred_length\", score=max(predictions))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "def check_not_idk(run: Run, example: Example):\n    \"\"\"Illustration of a custom evaluator.\"\"\"\n    agent_response = run.outputs[\"output\"]\n    if \"don't know\" in agent_response or \"not sure\" in agent_response:\n        score = 0\n    else:\n        score = 1\n    # You can access the dataset labels in example.outputs[key]\n    # You can also access the model inputs in run.inputs[key]\n    return EvaluationResult(\n        key=\"not_uncertain\",\n        score=score,\n    )",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "evaluation_config = RunEvalConfig(\n    # Evaluators can either be an evaluator type (e.g., \"qa\", \"criteria\", \"embedding_distance\", etc.) or a configuration for that evaluator\n    evaluators=[\n        check_not_idk,\n        # Measures whether a QA response is \"Correct\", based on a reference answer\n        # You can also select via the raw string \"qa\"\n        EvaluatorType.QA,\n        # Measure the embedding distance between the output and the reference answer\n        # Equivalent to: EvalConfig.EmbeddingDistance(embeddings=OpenAIEmbeddings())\n        EvaluatorType.EMBEDDING_DISTANCE,\n        # Grade whether the output satisfies the stated criteria.\n        # You can select a default one such as \"helpfulness\" or provide your own.\n        RunEvalConfig.LabeledCriteria(\"helpfulness\"),\n        # The LabeledScoreString evaluator outputs a score on a scale from 1-10.\n        # You can use default criteria or write our own rubric\n        RunEvalConfig.LabeledScoreString(\n            {\n                \"accuracy\":\n                    \"\"\"\nScore 1: The answer is completely unrelated to the reference.\nScore 3: The answer has minor relevance but does not align with the reference.\nScore 5: The answer has moderate relevance but contains inaccuracies.\nScore 7: The answer aligns with the reference but has minor errors or omissions.\nScore 10: The answer is completely accurate and aligns perfectly with the reference.\"\"\"\n            },\n            normalize_by=10,\n        ),\n    ],\n    batch_evaluators=[max_pred_length],\n)\n\nllm_with_tools = llm.bind_tools(tools)\nprompt = hub.pull(\"gabe/labelboxtutorialdemo\"\n                 )  # Change prompt in LangSmith hub to reflect example run\n\nchain_results = run_on_dataset(\n    dataset_name=dataset_name,\n    llm_or_chain_factory=functools.partial(create_agent,\n                                           prompt=prompt,\n                                           llm_with_tools=llm_with_tools),\n    evaluation=evaluation_config,\n    verbose=True,\n    client=client,\n    project_name=f\"tools-agent-test-5d466cbc-{unique_id}\",\n    # Project metadata communicates the experiment parameters,\n    # Useful for reviewing the test results\n    project_metadata={\n        \"env\": \"testing-notebook\",\n        \"model\": \"gpt-3.5-turbo\",\n        \"prompt\": \"5d466cbc\",\n    },\n)\n\n# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.\n# These are logged as warnings here and captured as errors in the tracing UI.",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Labelbox\n",
+                "Below converts the results of the above LangSmith run to Labelbox conversation text. Please review [Labelbox conversation data docs](https://docs.labelbox.com/docs/llm-human-preference) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "def import_conversational(\n        chain_results: dict[str:str],\n        user_id_dict: dict[str:dict[str:str]]) -> dict[str:str]:\n    \"\"\"Converts LangSmith chain_results from model invocation to Labelbox conversation data for model response comparison. Output is based on popular model response and custom model response towards prompts.\n\n    Args:\n      chain_results(dict[str:str]): Results from LangSmith model invocation against example dataset runs.\n      user_id_dict(dict[str:dict[str:str]]): Dictionary mapping of LangSmith example run type to Labelbox chat names and alignment.\n\n    Returns:\n        dict[str:str]: Labelbox conversation text format\n    \"\"\"\n    lb_conversations = []\n    for key, conversational in chain_results[\"results\"].items():\n        lb_conversation = {\n            \"row_data\": {\n                \"type\": \"application/vnd.labelbox.conversational\",\n                \"version\": 1,\n                \"messages\": [],\n                \"modelOutputs\": [],\n            },\n            \"global_key\": key,\n            \"media_type\": \"CONVERSATIONAL\",\n        }\n        if \"input\" in conversational[\"output\"]:\n            for i, input in enumerate(conversational[\"output\"][\"input\"]):\n                lb_conversation[\"row_data\"][\"messages\"].append({\n                    \"content\": input[\"data\"][\"content\"],\n                    \"timestampUsec\": i + 1,\n                    \"user\": {\n                        \"userId\": user_id_dict[input[\"type\"]][\"id\"],\n                        \"name\": input[\"type\"],\n                    },\n                    \"canLabel\": True,\n                    \"align\": user_id_dict[input[\"type\"]][\"align\"],\n                    \"messageId\": str(uuid4()),\n                })\n\n        # Custom model output\n        if \"reference\" in conversational:\n            reference = conversational[\"reference\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Custom Model Response\",\n                \"content\": reference[\"data\"][\"content\"],\n                \"modelConfigName\": \"Custom Model - Example Config\",\n            })\n\n        # Popular model output\n        if \"output\" in conversational[\"output\"]:\n            output = conversational[\"output\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Popular LLM Response\",\n                \"content\": output,\n                \"modelConfigName\": \"GPT-3.5 - Example Config\",\n            })\n\n        lb_conversations.append(lb_conversation)\n    return lb_conversations",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create Labelbox Dataset"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "dataset = lb_client.create_dataset(name=\"demo_langchain\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Attach Conversation Text to Dataset"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "task = dataset.create_data_rows(\n    import_conversational(\n        chain_results,\n        {\n            \"human\": {\n                \"id\": \"human\",\n                \"align\": \"right\"\n            },\n            \"ai\": {\n                \"id\": \"ai\",\n                \"align\": \"left\"\n            },\n        },\n    ))\ntask.wait_till_done()\n\nprint(task.errors)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/integrations/sam/meta_sam.ipynb b/examples/integrations/sam/meta_sam.ipynb
index 07d671dc4..848bcd84e 100644
--- a/examples/integrations/sam/meta_sam.ipynb
+++ b/examples/integrations/sam/meta_sam.ipynb
@@ -1,471 +1,251 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_labelbox.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_labelbox.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup\n",
-        "This notebook is used to show how to use Meta's Segment Anything model to create masks that can then be uploaded to a Labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\"\n",
-        "!pip install -q ultralytics==8.0.20\n",
-        "!pip install -q 'git+https://github.com/facebookresearch/segment-anything.git'"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Check if in google colab\n",
-        "try:\n",
-        "  import google.colab\n",
-        "  IN_COLAB = True\n",
-        "except:\n",
-        "  IN_COLAB = False"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "from IPython import display\n",
-        "display.clear_output()\n",
-        "\n",
-        "import ultralytics\n",
-        "ultralytics.checks()\n",
-        "\n",
-        "import cv2\n",
-        "import numpy as np\n",
-        "from ultralytics import YOLO\n",
-        "from IPython.display import display, Image\n",
-        "import torch\n",
-        "import matplotlib.pyplot as plt\n",
-        "from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor\n",
-        "import os\n",
-        "import urllib.request\n",
-        "import uuid\n",
-        "\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "\n",
-        "HOME = os.getcwd()\n",
-        "\n",
-        "if IN_COLAB:\n",
-        "    from google.colab.patches import cv2_imshow"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY=\"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Predicting bounding boxes around common objects using YOLOv8\n",
-        "\n",
-        "First, we start with loading the YOLOv8 model, getting a sample image, and running the model on it to generate bounding boxes around some common objects."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Utilize YOLOV8 to Create Bounding Boxes\n",
-        "\n",
-        "We use YOLOV8 in this demo to obtain bounding boxes around our images that we can later feed into SAM for our masks."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Below we run inference on a image using the YOLOv8 model."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# You can also use the Labelbox Client API to get specific images or an entire\n",
-        "# dataset from your Catalog. Refer to these docs:\n",
-        "# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n",
-        "\n",
-        "IMAGE_PATH = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/chairs.jpeg\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model = YOLO(f'{HOME}/yolov8n.pt')\n",
-        "results = model.predict(source=IMAGE_PATH, conf=0.25)\n",
-        "\n",
-        "# print(results[0].boxes.xyxy) # print bounding box coordinates\n",
-        "\n",
-        "# print(results[0].boxes.conf) # print confidence scores\n",
-        "\n",
-        "#for c in results[0].boxes.cls:\n",
-        "# print(model.names[int(c)]) # print predicted classes"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Below we visualize the bounding boxes on the image using CV2."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "image_bgr = cv2.imread(\"./chairs.jpeg\")\n",
-        "\n",
-        "for box in results[0].boxes.xyxy:\n",
-        "  cv2.rectangle(image_bgr, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)\n",
-        "\n",
-        "if IN_COLAB:\n",
-        "  cv2_imshow(image_bgr)\n",
-        "else:\n",
-        "  cv2.imshow(\"demo\", image_bgr)\n",
-        "  cv2.waitKey()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Predicting segmentation masks using Meta's Segment Anything model\n",
-        "\n",
-        "Now we load Meta's Segment Anything model and feed the bounding boxes to it, so it can generate segmentation masks within them."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Download SAM model weights\n",
-        "\n",
-        "CHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n",
-        "\n",
-        "if not os.path.isfile(CHECKPOINT_PATH):\n",
-        "    req = urllib.request.urlretrieve(\"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\", \"sam_vit_h_4b8939.pth\")\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
-        "MODEL_TYPE = \"vit_h\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\n",
-        "mask_predictor = SamPredictor(sam)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "transformed_boxes = mask_predictor.transform.apply_boxes_torch(results[0].boxes.xyxy, image_bgr.shape[:2])\n",
-        "\n",
-        "mask_predictor.set_image(image_bgr)\n",
-        "\n",
-        "masks, scores, logits = mask_predictor.predict_torch(\n",
-        "    boxes = transformed_boxes,\n",
-        "    multimask_output=False,\n",
-        "    point_coords=None,\n",
-        "    point_labels=None\n",
-        ")\n",
-        "masks = np.array(masks.cpu())\n",
-        "\n",
-        "# print(masks)\n",
-        "# print(scores)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Here we visualize the segmentation masks drawn on the image."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n",
-        "\n",
-        "final_mask = None\n",
-        "for i in range(len(masks) - 1):\n",
-        "  if final_mask is None:\n",
-        "    final_mask = np.bitwise_or(masks[i][0], masks[i+1][0])\n",
-        "  else:\n",
-        "    final_mask = np.bitwise_or(final_mask, masks[i+1][0])\n",
-        "\n",
-        "plt.figure(figsize=(10, 10))\n",
-        "plt.imshow(image_rgb)\n",
-        "plt.axis('off')\n",
-        "plt.imshow(final_mask, cmap='gray', alpha=0.7)\n",
-        "\n",
-        "plt.show()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Uploading predicted segmentation masks with class names to Labelbox using Python SDK"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox ObjectAnnotation of type mask for each predicted mask\n",
-        "\n",
-        "# Identifying what values in the numpy array correspond to the mask annotation\n",
-        "color = (1, 1, 1)\n",
-        "\n",
-        "class_names = []\n",
-        "for c in results[0].boxes.cls:\n",
-        "  class_names.append(model.names[int(c)])\n",
-        "\n",
-        "annotations = []\n",
-        "for idx, mask in enumerate(masks):\n",
-        "  mask_data = lb_types.MaskData.from_2D_arr(np.asarray(mask[0], dtype=\"uint8\"))\n",
-        "  mask_annotation = lb_types.ObjectAnnotation(\n",
-        "    name = class_names[idx], # this is the class predicted in Step 1 (object detector)\n",
-        "    value=lb_types.Mask(mask=mask_data, color=color),\n",
-        "  )\n",
-        "  annotations.append(mask_annotation)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new dataset\n",
-        "\n",
-        "# read more here: https://docs.labelbox.com/reference/data-row-global-keys\n",
-        "global_key = \"my_unique_global_key\"\n",
-        "\n",
-        "test_img_url = {\n",
-        "    \"row_data\": IMAGE_PATH,\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"auto-mask-classification-dataset\")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new ontology if you don't have one\n",
-        "\n",
-        "# Add all unique classes detected in Step 1\n",
-        "tools = []\n",
-        "for name in set(class_names):\n",
-        "  tools.append(lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=name))\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    classifications=[],\n",
-        "    tools=tools\n",
-        "  )\n",
-        "\n",
-        "ontology = client.create_ontology(\"auto-mask-classification-ontology\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Image\n",
-        "                                  )\n",
-        "\n",
-        "# Or get an existing ontology by name or ID (uncomment one of the below)\n",
-        "\n",
-        "# ontology = client.get_ontologies(\"Demo Chair\").get_one()\n",
-        "\n",
-        "# ontology = client.get_ontology(\"clhee8kzt049v094h7stq7v25\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new project if you don't have one\n",
-        "\n",
-        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
-        "# Queue mode will be deprecated once dataset mode is deprecated\n",
-        "project = client.create_project(name=\"auto-mask-classification-project\",\n",
-        "                                media_type=lb.MediaType.Image\n",
-        "                                )\n",
-        "\n",
-        "# Or get an existing project by ID (uncomment the below)\n",
-        "\n",
-        "# project = get_project(\"fill_in_project_id\")\n",
-        "\n",
-        "# If the project already has an ontology set up, comment out this line\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new batch of data for the project you specified above\n",
-        "\n",
-        "data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n",
-        "\n",
-        "batch = project.create_batch(\n",
-        "    \"auto-mask-classification-batch\",  # each batch in a project must have a unique name\n",
-        "    data_rows=data_row_ids,\n",
-        "\n",
-        "    # you can also specify global_keys instead of data_rows\n",
-        "    #global_keys=[global_key],  # paginated collection of data row objects, list of data row ids or global keys\n",
-        "\n",
-        "    priority=1  # priority between 1(highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(f\"Batch: {batch}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels = []\n",
-        "labels.append(\n",
-        "    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n",
-        "                   annotations=annotations))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the predictions to your specified project and data rows as pre-labels\n",
-        "\n",
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client=client,\n",
-        "    project_id=project.uid,\n",
-        "    name=\"mal_job\" + str(uuid.uuid4()),\n",
-        "    predictions=labels\n",
-        ")\n",
-        "upload_job.wait_until_done()\n",
-        "\n",
-        "print(f\"Errors: {upload_job.errors}\", )\n",
-        "print(f\"Status of uploads: {upload_job.statuses}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#dataset.delete()\n",
-        "#project.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup\n",
+                "This notebook is used to show how to use Meta's Segment Anything model to create masks that can then be uploaded to a Labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q 'git+https://github.com/facebookresearch/segment-anything.git'",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "from IPython import display\n\ndisplay.clear_output()\n\nimport ultralytics\n\nultralytics.checks()\n\nimport cv2\nimport numpy as np\nfrom ultralytics import YOLO\nfrom IPython.display import display, Image\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import (\n    sam_model_registry,\n    SamAutomaticMaskGenerator,\n    SamPredictor,\n)\nimport os\nimport urllib.request\nimport uuid\n\nimport labelbox as lb\nimport labelbox.types as lb_types\n\nHOME = os.getcwd()\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Predicting bounding boxes around common objects using YOLOv8\n",
+                "\n",
+                "First, we start with loading the YOLOv8 model, getting a sample image, and running the model on it to generate bounding boxes around some common objects."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Utilize YOLOV8 to Create Bounding Boxes\n",
+                "\n",
+                "We use YOLOV8 in this demo to obtain bounding boxes around our images that we can later feed into SAM for our masks."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Below we run inference on a image using the YOLOv8 model."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# You can also use the Labelbox Client API to get specific images or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n\nIMAGE_PATH = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/chairs.jpeg\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "model = YOLO(f\"{HOME}/yolov8n.pt\")\nresults = model.predict(source=IMAGE_PATH, conf=0.25)\n\n# print(results[0].boxes.xyxy) # print bounding box coordinates\n\n# print(results[0].boxes.conf) # print confidence scores\n\n# for c in results[0].boxes.cls:\n# print(model.names[int(c)]) # print predicted classes",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Below we visualize the bounding boxes on the image using CV2."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "image_bgr = cv2.imread(\"./chairs.jpeg\")\n\nfor box in results[0].boxes.xyxy:\n    cv2.rectangle(\n        image_bgr,\n        (int(box[0]), int(box[1])),\n        (int(box[2]), int(box[3])),\n        (0, 255, 0),\n        2,\n    )\n\nif IN_COLAB:\n    cv2_imshow(image_bgr)\nelse:\n    cv2.imshow(\"demo\", image_bgr)\n    cv2.waitKey()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Predicting segmentation masks using Meta's Segment Anything model\n",
+                "\n",
+                "Now we load Meta's Segment Anything model and feed the bounding boxes to it, so it can generate segmentation masks within them."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Download SAM model weights\n\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nMODEL_TYPE = \"vit_h\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(\n    device=DEVICE)\nmask_predictor = SamPredictor(sam)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n    results[0].boxes.xyxy, image_bgr.shape[:2])\n\nmask_predictor.set_image(image_bgr)\n\nmasks, scores, logits = mask_predictor.predict_torch(\n    boxes=transformed_boxes,\n    multimask_output=False,\n    point_coords=None,\n    point_labels=None,\n)\nmasks = np.array(masks.cpu())\n\n# print(masks)\n# print(scores)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Here we visualize the segmentation masks drawn on the image."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n\nfinal_mask = None\nfor i in range(len(masks) - 1):\n    if final_mask is None:\n        final_mask = np.bitwise_or(masks[i][0], masks[i + 1][0])\n    else:\n        final_mask = np.bitwise_or(final_mask, masks[i + 1][0])\n\nplt.figure(figsize=(10, 10))\nplt.imshow(image_rgb)\nplt.axis(\"off\")\nplt.imshow(final_mask, cmap=\"gray\", alpha=0.7)\n\nplt.show()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Uploading predicted segmentation masks with class names to Labelbox using Python SDK"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox ObjectAnnotation of type mask for each predicted mask\n\n# Identifying what values in the numpy array correspond to the mask annotation\ncolor = (1, 1, 1)\n\nclass_names = []\nfor c in results[0].boxes.cls:\n    class_names.append(model.names[int(c)])\n\nannotations = []\nfor idx, mask in enumerate(masks):\n    mask_data = lb_types.MaskData.from_2D_arr(np.asarray(mask[0],\n                                                         dtype=\"uint8\"))\n    mask_annotation = lb_types.ObjectAnnotation(\n        name=class_names[\n            idx],  # this is the class predicted in Step 1 (object detector)\n        value=lb_types.Mask(mask=mask_data, color=color),\n    )\n    annotations.append(mask_annotation)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new dataset\n\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = \"my_unique_global_key\"\n\ntest_img_url = {\"row_data\": IMAGE_PATH, \"global_key\": global_key}\n\ndataset = client.create_dataset(name=\"auto-mask-classification-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new ontology if you don't have one\n\n# Add all unique classes detected in Step 1\ntools = []\nfor name in set(class_names):\n    tools.append(lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=name))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"auto-mask-classification-ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"Demo Chair\").get_one()\n\n# ontology = client.get_ontology(\"clhee8kzt049v094h7stq7v25\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"auto-mask-classification-project\",\n                                media_type=lb.MediaType.Image)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new batch of data for the project you specified above\n\ndata_row_ids = client.get_data_row_ids_for_global_keys([global_key])[\"results\"]\n\nbatch = project.create_batch(\n    \"auto-mask-classification-batch\",  # each batch in a project must have a unique name\n    data_rows=data_row_ids,\n    # you can also specify global_keys instead of data_rows\n    # global_keys=[global_key],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "labels = []\nlabels.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the predictions to your specified project and data rows as pre-labels\n\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# dataset.delete()\n# project.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/integrations/sam/meta_sam_video.ipynb b/examples/integrations/sam/meta_sam_video.ipynb
index 1a3c1d7e0..45e425c36 100644
--- a/examples/integrations/sam/meta_sam_video.ipynb
+++ b/examples/integrations/sam/meta_sam_video.ipynb
@@ -1,656 +1,235 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_labelbox_video.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_labelbox_video.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup\n",
-        "\n",
-        "This notebook is used to show how to use Meta's Segment Anything model and YOLO to create masks for videos that can then be uploaded to a Labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### General dependencies"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "%pip install -q \"labelbox[data]\"\n",
-        "%pip install -q ultralytics==8.0.20\n",
-        "%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Check if in google colab\n",
-        "try:\n",
-        "    import google.colab\n",
-        "\n",
-        "    IN_COLAB = True\n",
-        "except:\n",
-        "    IN_COLAB = False"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import os\n",
-        "import urllib\n",
-        "import cv2\n",
-        "import PIL\n",
-        "from PIL import Image\n",
-        "import numpy as np\n",
-        "import uuid\n",
-        "import tempfile\n",
-        "\n",
-        "if IN_COLAB:\n",
-        "    from google.colab.patches import cv2_imshow\n",
-        "\n",
-        "from IPython import display\n",
-        "\n",
-        "display.clear_output()\n",
-        "from IPython.display import display, Image\n",
-        "from io import BytesIO\n",
-        "\n",
-        "# YOLOv8 dependencies\n",
-        "import ultralytics\n",
-        "\n",
-        "ultralytics.checks()\n",
-        "from ultralytics import YOLO\n",
-        "\n",
-        "# SAM dependencies\n",
-        "import torch\n",
-        "import matplotlib.pyplot as plt\n",
-        "from segment_anything import sam_model_registry, SamPredictor\n",
-        "\n",
-        "# Labelbox dependencies\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# You can also use the Labelbox Client API to get specific videos or an entire\n",
-        "# dataset from your Catalog. Refer to these docs:\n",
-        "# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n",
-        "HOME = os.getcwd()\n",
-        "VIDEO_PATH = os.path.join(HOME, \"skateboarding.mp4\")\n",
-        "\n",
-        "if not os.path.isfile(VIDEO_PATH):\n",
-        "    req = urllib.request.urlretrieve(\n",
-        "        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/skateboarding.mp4\",\n",
-        "        \"skateboarding.mp4\",\n",
-        "    )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### YOLOv8 setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Instantiate YOLOv8 model\n",
-        "model = YOLO(f\"{HOME}/yolov8n.pt\")\n",
-        "colors = np.random.randint(0, 256, size=(len(model.names), 3))\n",
-        "\n",
-        "print(model.names)\n",
-        "\n",
-        "# Specify which classes you care about. The rest of classes will be filtered out.\n",
-        "chosen_class_ids = [0]  # person"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### SAM setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Download SAM model weights\n",
-        "CHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n",
-        "\n",
-        "if not os.path.isfile(CHECKPOINT_PATH):\n",
-        "    req = urllib.request.urlretrieve(\n",
-        "        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n",
-        "        \"sam_vit_h_4b8939.pth\",\n",
-        "    )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Instantiate SAM model\n",
-        "\n",
-        "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-        "sam = sam_model_registry[\"vit_h\"](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\n",
-        "mask_predictor = SamPredictor(sam)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Labelbox setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = None\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Helper functions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Cast color to ints\n",
-        "def get_color(color):\n",
-        "    return (int(color[0]), int(color[1]), int(color[2]))\n",
-        "\n",
-        "\n",
-        "# Get video dimensions\n",
-        "def get_video_dimensions(input_cap):\n",
-        "    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
-        "    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
-        "    return height, width\n",
-        "\n",
-        "\n",
-        "# Get output video writer with same dimensions and fps as input video\n",
-        "def get_output_video_writer(input_cap, output_path):\n",
-        "    # Get the video's properties (width, height, FPS)\n",
-        "    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
-        "    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
-        "    fps = int(input_cap.get(cv2.CAP_PROP_FPS))\n",
-        "\n",
-        "    # Define the output video file\n",
-        "    output_codec = cv2.VideoWriter_fourcc(*\"mp4v\")  # MP4 codec\n",
-        "    output_video = cv2.VideoWriter(output_path, output_codec, fps,\n",
-        "                                   (width, height))\n",
-        "\n",
-        "    return output_video\n",
-        "\n",
-        "\n",
-        "# Visualize a video frame with bounding boxes, classes and confidence scores\n",
-        "def visualize_detections(frame, boxes, conf_thresholds, class_ids):\n",
-        "    frame_copy = np.copy(frame)\n",
-        "    for idx in range(len(boxes)):\n",
-        "        class_id = int(class_ids[idx])\n",
-        "        conf = float(conf_thresholds[idx])\n",
-        "        x1, y1, x2, y2 = (\n",
-        "            int(boxes[idx][0]),\n",
-        "            int(boxes[idx][1]),\n",
-        "            int(boxes[idx][2]),\n",
-        "            int(boxes[idx][3]),\n",
-        "        )\n",
-        "        color = colors[class_id]\n",
-        "        label = f\"{model.names[class_id]}: {conf:.2f}\"\n",
-        "        cv2.rectangle(frame_copy, (x1, y1), (x2, y2), get_color(color), 2)\n",
-        "        cv2.putText(\n",
-        "            frame_copy,\n",
-        "            label,\n",
-        "            (x1, y1 - 10),\n",
-        "            cv2.FONT_HERSHEY_SIMPLEX,\n",
-        "            0.9,\n",
-        "            get_color(color),\n",
-        "            2,\n",
-        "        )\n",
-        "    return frame_copy\n",
-        "\n",
-        "\n",
-        "def add_color_to_mask(mask, color):\n",
-        "    next_mask = mask.astype(np.uint8)\n",
-        "    next_mask = np.expand_dims(next_mask, 0).repeat(3, axis=0)\n",
-        "    next_mask = np.moveaxis(next_mask, 0, -1)\n",
-        "    return next_mask * color\n",
-        "\n",
-        "\n",
-        "# Merge masks into a single, multi-colored mask\n",
-        "def merge_masks_colored(masks, class_ids):\n",
-        "    filtered_class_ids = []\n",
-        "    filtered_masks = []\n",
-        "    for idx, cid in enumerate(class_ids):\n",
-        "        if int(cid) in chosen_class_ids:\n",
-        "            filtered_class_ids.append(cid)\n",
-        "            filtered_masks.append(masks[idx])\n",
-        "\n",
-        "    merged_with_colors = add_color_to_mask(\n",
-        "        filtered_masks[0][0],\n",
-        "        get_color(colors[int(filtered_class_ids[0])])).astype(np.uint8)\n",
-        "\n",
-        "    if len(filtered_masks) == 1:\n",
-        "        return merged_with_colors\n",
-        "\n",
-        "    for i in range(1, len(filtered_masks)):\n",
-        "        curr_mask_with_colors = add_color_to_mask(\n",
-        "            filtered_masks[i][0], get_color(colors[int(filtered_class_ids[i])]))\n",
-        "        merged_with_colors = np.bitwise_or(merged_with_colors,\n",
-        "                                           curr_mask_with_colors)\n",
-        "\n",
-        "    return merged_with_colors.astype(np.uint8)\n",
-        "\n",
-        "\n",
-        "def get_instance_uri(client: lb.Client, global_key, array):\n",
-        "    \"\"\"Reads a numpy array into a temp Labelbox data row to-be-uploaded to Labelbox\n",
-        "    Args:\n",
-        "        client        :   Required (lb.Client) - Labelbox Client object\n",
-        "        global_key    :   Required (str) - Data row global key\n",
-        "        array         :   Required (np.ndarray) - NumPy ndarray representation of an image\n",
-        "    Returns:\n",
-        "        Temp Labelbox data row to-be-uploaded to Labelbox as row data\n",
-        "    \"\"\"\n",
-        "    # Convert array to PIL image\n",
-        "    image_as_pil = PIL.Image.fromarray(array)\n",
-        "    # Convert PIL image to PNG file bytes\n",
-        "    image_as_bytes = BytesIO()\n",
-        "    image_as_pil.save(image_as_bytes, format=\"PNG\")\n",
-        "    image_as_bytes = image_as_bytes.getvalue()\n",
-        "    # Convert PNG file bytes to a temporary Labelbox URL\n",
-        "    url = client.upload_data(\n",
-        "        content=image_as_bytes,\n",
-        "        filename=f\"{uuid.uuid4()}{global_key}\",\n",
-        "        content_type=\"image/jpeg\",\n",
-        "        sign=True,\n",
-        "    )\n",
-        "    # Return the URL\n",
-        "    return url\n",
-        "\n",
-        "\n",
-        "def get_local_instance_uri(array):\n",
-        "    # Convert array to PIL image\n",
-        "    image_as_pil = PIL.Image.fromarray(array)\n",
-        "\n",
-        "    with tempfile.NamedTemporaryFile(suffix=\".png\",\n",
-        "                                     dir=\"/content\",\n",
-        "                                     delete=False) as temp_file:\n",
-        "        image_as_pil.save(temp_file)\n",
-        "        file_name = temp_file.name\n",
-        "\n",
-        "    # Return the URL\n",
-        "    return file_name\n",
-        "\n",
-        "\n",
-        "def create_mask_frame(frame_num, instance_uri):\n",
-        "    return lb_types.MaskFrame(index=frame_num, instance_uri=instance_uri)\n",
-        "\n",
-        "\n",
-        "def create_mask_instances(class_ids):\n",
-        "    instances = []\n",
-        "    for cid in list(set(class_ids)):  # get unique class ids\n",
-        "        if int(cid) in chosen_class_ids:\n",
-        "            color = get_color(colors[int(cid)])\n",
-        "            name = model.names[int(cid)]\n",
-        "            instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n",
-        "    return instances\n",
-        "\n",
-        "\n",
-        "def create_video_mask_annotation(frames, instance):\n",
-        "    return lb_types.VideoMaskAnnotation(frames=frames, instances=[instance])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Labelbox create dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new dataset\n",
-        "# read more here: https://docs.labelbox.com/reference/data-row-global-keys\n",
-        "global_key = os.path.basename(VIDEO_PATH)\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": VIDEO_PATH,\n",
-        "    \"global_key\": global_key,\n",
-        "    \"media_type\": \"VIDEO\"\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"yolo-sam-video-masks-dataset\")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Run through YOLOv8 on the video once quickly to get unique class ids present\n",
-        "# This will inform which classes we add to the ontology\n",
-        "\n",
-        "cap = cv2.VideoCapture(VIDEO_PATH)\n",
-        "\n",
-        "unique_class_ids = set()\n",
-        "\n",
-        "# Loop through the frames of the video\n",
-        "frame_num = 1\n",
-        "while cap.isOpened():\n",
-        "    if frame_num % 30 == 0 or frame_num == 1:\n",
-        "        print(\"Processing frame number\", frame_num)\n",
-        "    ret, frame = cap.read()\n",
-        "    if not ret:\n",
-        "        break\n",
-        "\n",
-        "    # Run frame through YOLOv8 and get class ids predicted\n",
-        "    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n",
-        "    for cid in detections[0].boxes.cls:\n",
-        "        unique_class_ids.add(int(cid))\n",
-        "    frame_num += 1\n",
-        "\n",
-        "cap.release()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "unique_class_ids"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new ontology if you don't have one\n",
-        "\n",
-        "# Add all chosen classes into the ontology\n",
-        "tools = []\n",
-        "for cls in chosen_class_ids:\n",
-        "    tools.append(\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=model.names[cls]))\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n",
-        "\n",
-        "ontology = client.create_ontology(\n",
-        "    \"yolo-sam-video-masks-ontology\",\n",
-        "    ontology_builder.asdict(),\n",
-        ")\n",
-        "\n",
-        "# Or get an existing ontology by name or ID (uncomment one of the below)\n",
-        "\n",
-        "# ontology = client.get_ontologies(\"yolo-sam-video-masks-ontology\").get_one()\n",
-        "\n",
-        "# ontology = client.get_ontology(\"\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new project if you don't have one\n",
-        "\n",
-        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
-        "# Queue mode will be deprecated once dataset mode is deprecated\n",
-        "project = client.create_project(name=\"yolo-sam-video-masks-project\",\n",
-        "                                media_type=lb.MediaType.Video)\n",
-        "\n",
-        "# Or get an existing project by ID (uncomment the below)\n",
-        "\n",
-        "# project = get_project(\"fill_in_project_id\")\n",
-        "\n",
-        "# If the project already has an ontology set up, comment out this line\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new batch of data for the project you specified above\n",
-        "\n",
-        "# Uncomment if you are using `data_rows` parameter below\n",
-        "# data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n",
-        "\n",
-        "batch = project.create_batch(\n",
-        "    \"yolo-sam-video-masks-project\",  # each batch in a project must have a unique name\n",
-        "    # you can also specify global_keys instead of data_rows\n",
-        "    global_keys=[global_key],\n",
-        "    # you can also specify data_rows instead of global_keys\n",
-        "    # data_rows=data_row_ids,\n",
-        "    priority=1,  # priority between 1(highest) - 5(lowest)\n",
-        ")\n",
-        "\n",
-        "print(f\"Batch: {batch}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "tools = ontology.tools()\n",
-        "\n",
-        "feature_schema_ids = dict()\n",
-        "for tool in tools:\n",
-        "    feature_schema_ids[tool.name] = tool.feature_schema_id\n",
-        "\n",
-        "print(feature_schema_ids)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Loop through each frame of video and process it\n",
-        "* Run YOLOv8 and then SAM on each frame, and write visualization videos to disk\n",
-        "* This might take a few minutes to run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "cap = cv2.VideoCapture(VIDEO_PATH)\n",
-        "\n",
-        "output_video_boxes = get_output_video_writer(\n",
-        "    cap, \"/content/skateboarding_boxes.mp4\")\n",
-        "output_video_masks = get_output_video_writer(\n",
-        "    cap, \"/content/skateboarding_masks.mp4\")\n",
-        "mask_frames = []\n",
-        "\n",
-        "# Loop through the frames of the video\n",
-        "frame_num = 1\n",
-        "while cap.isOpened():\n",
-        "    if frame_num % 30 == 0 or frame_num == 1:\n",
-        "        print(\"Processing frames\", frame_num, \"-\", frame_num + 29)\n",
-        "    ret, frame = cap.read()\n",
-        "    if not ret:\n",
-        "        break\n",
-        "\n",
-        "    # Run frame through YOLOv8 to get detections\n",
-        "    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n",
-        "\n",
-        "    # Write detections to output video\n",
-        "    frame_with_detections = visualize_detections(\n",
-        "        frame,\n",
-        "        detections[0].boxes.cpu().xyxy,\n",
-        "        detections[0].boxes.cpu().conf,\n",
-        "        detections[0].boxes.cpu().cls,\n",
-        "    )\n",
-        "    output_video_boxes.write(frame_with_detections)\n",
-        "\n",
-        "    # Run frame and detections through SAM to get masks\n",
-        "    transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n",
-        "        detections[0].boxes.xyxy, list(get_video_dimensions(cap)))\n",
-        "    if len(transformed_boxes) == 0:\n",
-        "        print(\"No boxes found on frame\", frame_num)\n",
-        "        output_video_masks.write(frame)\n",
-        "        frame_num += 1\n",
-        "        continue\n",
-        "    mask_predictor.set_image(frame)\n",
-        "    masks, scores, logits = mask_predictor.predict_torch(\n",
-        "        boxes=transformed_boxes,\n",
-        "        multimask_output=False,\n",
-        "        point_coords=None,\n",
-        "        point_labels=None,\n",
-        "    )\n",
-        "    masks = np.array(masks.cpu())\n",
-        "    if masks is None or len(masks) == 0:\n",
-        "        print(\"No masks found on frame\", frame_num)\n",
-        "        output_video_masks.write(frame)\n",
-        "        frame_num += 1\n",
-        "        continue\n",
-        "    merged_colored_mask = merge_masks_colored(masks, detections[0].boxes.cls)\n",
-        "\n",
-        "    # Write masks to output video\n",
-        "    image_combined = cv2.addWeighted(frame, 0.7, merged_colored_mask, 0.7, 0)\n",
-        "    output_video_masks.write(image_combined)\n",
-        "\n",
-        "    # Create video mask annotation for upload to Labelbox\n",
-        "    instance_uri = get_instance_uri(client, global_key, merged_colored_mask)\n",
-        "    mask_frame = create_mask_frame(frame_num, instance_uri)\n",
-        "    mask_frames.append(mask_frame)\n",
-        "    print(\"Boxes found on frame\", frame_num)\n",
-        "    frame_num += 1\n",
-        "\n",
-        "    # For the purposes of this demo, only look at the first 80 frames\n",
-        "    if frame_num > 80:\n",
-        "        break\n",
-        "\n",
-        "cap.release()\n",
-        "output_video_boxes.release()\n",
-        "output_video_masks.release()\n",
-        "cv2.destroyAllWindows()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create annotations for LB upload\n",
-        "mask_instances = create_mask_instances(unique_class_ids)\n",
-        "annotations = []\n",
-        "for instance in mask_instances:\n",
-        "    annotations.append(create_video_mask_annotation(mask_frames, instance))\n",
-        "\n",
-        "labels = []\n",
-        "labels.append(\n",
-        "    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Upload the predictions to your specified project and data rows as pre-labels\n",
-        "\n",
-        "Note: This may take a few minutes, depending on size of video and number of masks"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-        "    client=client,\n",
-        "    project_id=project.uid,\n",
-        "    name=\"mal_import_job\" + str(uuid.uuid4()),\n",
-        "    predictions=labels,\n",
-        ")\n",
-        "upload_job.wait_until_done()\n",
-        "print(f\"Errors: {upload_job.errors}\",)\n",
-        "print(f\"Status of uploads: {upload_job.statuses}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Setup\n",
+                "\n",
+                "This notebook is used to show how to use Meta's Segment Anything model and YOLO to create masks for videos that can then be uploaded to a Labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### General dependencies"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import os\nimport urllib\nimport cv2\nimport PIL\nfrom PIL import Image\nimport numpy as np\nimport uuid\nimport tempfile\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow\n\nfrom IPython import display\n\ndisplay.clear_output()\nfrom IPython.display import display, Image\nfrom io import BytesIO\n\n# YOLOv8 dependencies\nimport ultralytics\n\nultralytics.checks()\nfrom ultralytics import YOLO\n\n# SAM dependencies\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Labelbox dependencies\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# You can also use the Labelbox Client API to get specific videos or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\nHOME = os.getcwd()\nVIDEO_PATH = os.path.join(HOME, \"skateboarding.mp4\")\n\nif not os.path.isfile(VIDEO_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/skateboarding.mp4\",\n        \"skateboarding.mp4\",\n    )",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### YOLOv8 setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Instantiate YOLOv8 model\nmodel = YOLO(f\"{HOME}/yolov8n.pt\")\ncolors = np.random.randint(0, 256, size=(len(model.names), 3))\n\nprint(model.names)\n\n# Specify which classes you care about. The rest of classes will be filtered out.\nchosen_class_ids = [0]  # person",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### SAM setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Download SAM model weights\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Instantiate SAM model\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nsam = sam_model_registry[\"vit_h\"](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\nmask_predictor = SamPredictor(sam)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Labelbox setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Helper functions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Cast color to ints\ndef get_color(color):\n    return (int(color[0]), int(color[1]), int(color[2]))\n\n\n# Get video dimensions\ndef get_video_dimensions(input_cap):\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    return height, width\n\n\n# Get output video writer with same dimensions and fps as input video\ndef get_output_video_writer(input_cap, output_path):\n    # Get the video's properties (width, height, FPS)\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = int(input_cap.get(cv2.CAP_PROP_FPS))\n\n    # Define the output video file\n    output_codec = cv2.VideoWriter_fourcc(*\"mp4v\")  # MP4 codec\n    output_video = cv2.VideoWriter(output_path, output_codec, fps,\n                                   (width, height))\n\n    return output_video\n\n\n# Visualize a video frame with bounding boxes, classes and confidence scores\ndef visualize_detections(frame, boxes, conf_thresholds, class_ids):\n    frame_copy = np.copy(frame)\n    for idx in range(len(boxes)):\n        class_id = int(class_ids[idx])\n        conf = float(conf_thresholds[idx])\n        x1, y1, x2, y2 = (\n            int(boxes[idx][0]),\n            int(boxes[idx][1]),\n            int(boxes[idx][2]),\n            int(boxes[idx][3]),\n        )\n        color = colors[class_id]\n        label = f\"{model.names[class_id]}: {conf:.2f}\"\n        cv2.rectangle(frame_copy, (x1, y1), (x2, y2), get_color(color), 2)\n        cv2.putText(\n            frame_copy,\n            label,\n            (x1, y1 - 10),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            0.9,\n            get_color(color),\n            2,\n        )\n    return frame_copy\n\n\ndef add_color_to_mask(mask, color):\n    next_mask = mask.astype(np.uint8)\n    next_mask = np.expand_dims(next_mask, 0).repeat(3, axis=0)\n    next_mask = np.moveaxis(next_mask, 0, -1)\n    return next_mask * color\n\n\n# Merge masks into a single, multi-colored mask\ndef merge_masks_colored(masks, class_ids):\n    filtered_class_ids = []\n    filtered_masks = []\n    for idx, cid in enumerate(class_ids):\n        if int(cid) in chosen_class_ids:\n            filtered_class_ids.append(cid)\n            filtered_masks.append(masks[idx])\n\n    merged_with_colors = add_color_to_mask(\n        filtered_masks[0][0],\n        get_color(colors[int(filtered_class_ids[0])])).astype(np.uint8)\n\n    if len(filtered_masks) == 1:\n        return merged_with_colors\n\n    for i in range(1, len(filtered_masks)):\n        curr_mask_with_colors = add_color_to_mask(\n            filtered_masks[i][0], get_color(colors[int(filtered_class_ids[i])]))\n        merged_with_colors = np.bitwise_or(merged_with_colors,\n                                           curr_mask_with_colors)\n\n    return merged_with_colors.astype(np.uint8)\n\n\ndef get_instance_uri(client: lb.Client, global_key, array):\n    \"\"\"Reads a numpy array into a temp Labelbox data row to-be-uploaded to Labelbox\n    Args:\n        client        :   Required (lb.Client) - Labelbox Client object\n        global_key    :   Required (str) - Data row global key\n        array         :   Required (np.ndarray) - NumPy ndarray representation of an image\n    Returns:\n        Temp Labelbox data row to-be-uploaded to Labelbox as row data\n    \"\"\"\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n    # Convert PIL image to PNG file bytes\n    image_as_bytes = BytesIO()\n    image_as_pil.save(image_as_bytes, format=\"PNG\")\n    image_as_bytes = image_as_bytes.getvalue()\n    # Convert PNG file bytes to a temporary Labelbox URL\n    url = client.upload_data(\n        content=image_as_bytes,\n        filename=f\"{uuid.uuid4()}{global_key}\",\n        content_type=\"image/jpeg\",\n        sign=True,\n    )\n    # Return the URL\n    return url\n\n\ndef get_local_instance_uri(array):\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n\n    with tempfile.NamedTemporaryFile(suffix=\".png\",\n                                     dir=\"/content\",\n                                     delete=False) as temp_file:\n        image_as_pil.save(temp_file)\n        file_name = temp_file.name\n\n    # Return the URL\n    return file_name\n\n\ndef create_mask_frame(frame_num, instance_uri):\n    return lb_types.MaskFrame(index=frame_num, instance_uri=instance_uri)\n\n\ndef create_mask_instances(class_ids):\n    instances = []\n    for cid in list(set(class_ids)):  # get unique class ids\n        if int(cid) in chosen_class_ids:\n            color = get_color(colors[int(cid)])\n            name = model.names[int(cid)]\n            instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n    return instances\n\n\ndef create_video_mask_annotation(frames, instance):\n    return lb_types.VideoMaskAnnotation(frames=frames, instances=[instance])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Labelbox create dataset"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new dataset\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = os.path.basename(VIDEO_PATH)\n\nasset = {\n    \"row_data\": VIDEO_PATH,\n    \"global_key\": global_key,\n    \"media_type\": \"VIDEO\",\n}\n\ndataset = client.create_dataset(name=\"yolo-sam-video-masks-dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Run through YOLOv8 on the video once quickly to get unique class ids present\n# This will inform which classes we add to the ontology\n\ncap = cv2.VideoCapture(VIDEO_PATH)\n\nunique_class_ids = set()\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frame number\", frame_num)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 and get class ids predicted\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n    for cid in detections[0].boxes.cls:\n        unique_class_ids.add(int(cid))\n    frame_num += 1\n\ncap.release()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "unique_class_ids",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new ontology if you don't have one\n\n# Add all chosen classes into the ontology\ntools = []\nfor cls in chosen_class_ids:\n    tools.append(\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=model.names[cls]))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"yolo-sam-video-masks-ontology\",\n    ontology_builder.asdict(),\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"yolo-sam-video-masks-ontology\").get_one()\n\n# ontology = client.get_ontology(\"\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"yolo-sam-video-masks-project\",\n                                media_type=lb.MediaType.Video)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new batch of data for the project you specified above\n\n# Uncomment if you are using `data_rows` parameter below\n# data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n\nbatch = project.create_batch(\n    \"yolo-sam-video-masks-project\",  # each batch in a project must have a unique name\n    # you can also specify global_keys instead of data_rows\n    global_keys=[global_key],\n    # you can also specify data_rows instead of global_keys\n    # data_rows=data_row_ids,\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "tools = ontology.tools()\n\nfeature_schema_ids = dict()\nfor tool in tools:\n    feature_schema_ids[tool.name] = tool.feature_schema_id\n\nprint(feature_schema_ids)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Loop through each frame of video and process it\n",
+                "* Run YOLOv8 and then SAM on each frame, and write visualization videos to disk\n",
+                "* This might take a few minutes to run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "cap = cv2.VideoCapture(VIDEO_PATH)\n\noutput_video_boxes = get_output_video_writer(\n    cap, \"/content/skateboarding_boxes.mp4\")\noutput_video_masks = get_output_video_writer(\n    cap, \"/content/skateboarding_masks.mp4\")\nmask_frames = []\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frames\", frame_num, \"-\", frame_num + 29)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 to get detections\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n\n    # Write detections to output video\n    frame_with_detections = visualize_detections(\n        frame,\n        detections[0].boxes.cpu().xyxy,\n        detections[0].boxes.cpu().conf,\n        detections[0].boxes.cpu().cls,\n    )\n    output_video_boxes.write(frame_with_detections)\n\n    # Run frame and detections through SAM to get masks\n    transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n        detections[0].boxes.xyxy, list(get_video_dimensions(cap)))\n    if len(transformed_boxes) == 0:\n        print(\"No boxes found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    mask_predictor.set_image(frame)\n    masks, scores, logits = mask_predictor.predict_torch(\n        boxes=transformed_boxes,\n        multimask_output=False,\n        point_coords=None,\n        point_labels=None,\n    )\n    masks = np.array(masks.cpu())\n    if masks is None or len(masks) == 0:\n        print(\"No masks found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    merged_colored_mask = merge_masks_colored(masks, detections[0].boxes.cls)\n\n    # Write masks to output video\n    image_combined = cv2.addWeighted(frame, 0.7, merged_colored_mask, 0.7, 0)\n    output_video_masks.write(image_combined)\n\n    # Create video mask annotation for upload to Labelbox\n    instance_uri = get_instance_uri(client, global_key, merged_colored_mask)\n    mask_frame = create_mask_frame(frame_num, instance_uri)\n    mask_frames.append(mask_frame)\n    print(\"Boxes found on frame\", frame_num)\n    frame_num += 1\n\n    # For the purposes of this demo, only look at the first 80 frames\n    if frame_num > 80:\n        break\n\ncap.release()\noutput_video_boxes.release()\noutput_video_masks.release()\ncv2.destroyAllWindows()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Create annotations for LB upload\nmask_instances = create_mask_instances(unique_class_ids)\nannotations = []\nfor instance in mask_instances:\n    annotations.append(create_video_mask_annotation(mask_frames, instance))\n\nlabels = []\nlabels.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Upload the predictions to your specified project and data rows as pre-labels\n",
+                "\n",
+                "Note: This may take a few minutes, depending on size of video and number of masks"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/custom_metrics_basics.ipynb b/examples/model_experiments/custom_metrics_basics.ipynb
index 546296f46..26a4b8d0f 100644
--- a/examples/model_experiments/custom_metrics_basics.ipynb
+++ b/examples/model_experiments/custom_metrics_basics.ipynb
@@ -1,395 +1,257 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_diagnostics/custom_metrics_basics.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_diagnostics/custom_metrics_basics.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "----\n",
-        "\n",
-        "# Model Diagnostics - Custom Metrics Basics\n",
-        "\n",
-        "\n",
-        "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
-        "    * Iterate faster\n",
-        "    * Measure and report on model quality\n",
-        "    * Understand marginal value of additional labels and modeling efforts\n",
-        "\n",
-        "\n",
-        "* For an end-to-end demo of diagnostics using custom metrics checkout this [notebook](custom_metrics_demo.ipynb)\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Environment Setup\n",
-        "\n",
-        "Install dependencies"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Import libraries"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox.types as lb_types\n",
-        "import labelbox as lb\n",
-        "import uuid\n",
-        "import json"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Custom Metrics\n",
-        "* Users can provide metrics at the following levels of granularity:\n",
-        "    1. data rows\n",
-        "    2. features\n",
-        "    3. subclasses\n",
-        "* Additionally, metrics can be given custom names to best describe what they are measuring.\n",
-        "    \n",
-        "* Limits and Behavior:\n",
-        "    * At a data row cannot have more than 20 metrics\n",
-        "    * Metrics are upserted, so if a metric already exists, its value will be replaced\n",
-        "    * Metrics can have values in the range [0,100000]\n",
-        "* Currently `ScalarMetric`s and `ConfusionMatrixMetric`s are supported. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### ScalarMetric\n",
-        "    * A `ScalarMetric` is a metric with just a single scalar value."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "from labelbox.data.annotation_types import (ScalarMetric,\n",
-        "                                            ScalarMetricAggregation,\n",
-        "                                            ConfusionMatrixMetric)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_row_metric = ScalarMetric(metric_name=\"iou_custom\", value=0.5)\n",
-        "\n",
-        "feature_metric = ScalarMetric(metric_name=\"iou_custom\", feature_name=\"cat\", value=0.5)\n",
-        "\n",
-        "subclass_metric = ScalarMetric(metric_name=\"iou_custom\",\n",
-        "                               feature_name=\"cat\",\n",
-        "                               subclass_name=\"organge\",\n",
-        "                               value=0.5)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### ConfusionMatrixMetric\n",
-        "- A `ConfusionMatrixMetric` contains 4 numbers [True postivie, False Postive, True Negative, False Negateive]\n",
-        "- Confidence is also supported a key value pairs, where the score is the key and the value is the metric value.\n",
-        "- In the user interface, these metrics are used to derive precision,recall, and f1 scores. The reason these are not directly uploaded is that the raw data allows us to do processing on the front end.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_row_metric = ConfusionMatrixMetric(metric_name=\"50pct_iou\",\n",
-        "                                        feature_name=\"cat\",\n",
-        "                                        subclass_name=\"organge\",\n",
-        "                                        value=[1, 0, 1, 0])\n",
-        "\n",
-        "feature_metric = ConfusionMatrixMetric(metric_name=\"50pct_iou\",\n",
-        "                                       feature_name=\"cat\",\n",
-        "                                       subclass_name=\"organge\",\n",
-        "                                       value=[1, 0, 1, 0])\n",
-        "\n",
-        "subclass_metric = ConfusionMatrixMetric(metric_name=\"50pct_iou\",\n",
-        "                                        feature_name=\"cat\",\n",
-        "                                        subclass_name=\"organge\",\n",
-        "                                        value=[1, 0, 1, 0])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Confidence\n",
-        "* Users can provide confidence scores along with metrics\n",
-        "* This enables them to explore their model performance without necessarily knowing the optimal thresholds for each class.\n",
-        "* Users can filter on confidence and value in the UI to perform powerful queries.\n",
-        "* The keys represent a confidence score (must be between 0 and 1) and the values represent either a scalar metric or for confusion matrix metrics [TP,FP,TN,FN]"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "confusion_matrix_metric_with_confidence = ConfusionMatrixMetric(\n",
-        "    metric_name=\"confusion_matrix_50pct_iou\",\n",
-        "    feature_name=\"cat\",\n",
-        "    subclass_name=\"organge\",\n",
-        "    value={\n",
-        "        0.1: [1, 0, 1, 0],\n",
-        "        0.3: [1, 0, 1, 0],\n",
-        "        0.5: [1, 0, 1, 0],\n",
-        "        0.7: [1, 0, 1, 0],\n",
-        "        0.9: [1, 0, 1, 0]\n",
-        "    })\n",
-        "\n",
-        "scalar_metric_with_confidence = ScalarMetric(metric_name=\"iou_custom\",\n",
-        "                                             value={\n",
-        "                                                 0.1: 0.2,\n",
-        "                                                 0.3: 0.25,\n",
-        "                                                 0.5: 0.3,\n",
-        "                                                 0.7: 0.4,\n",
-        "                                                 0.9: 0.3\n",
-        "                                             })"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Aggregations\n",
-        "* This is an optional field on the `ScalarMetric` object (by default it uses Arithmetic Mean).\n",
-        "* Aggregations occur in two cases:\n",
-        "    1. When a user provides a feature or subclass level metric, Labelbox automatically aggregates all metrics with the same parent to create a value for that parent.\n",
-        "        * E.g. A user provides cat and dog iou. The data row level metric for iou is the average of both of those.\n",
-        "        * The exception to this is when the data row level iou is explicitly set, then the aggregation will not take effect (on a per data row basis). \n",
-        "    2. When users create slices or want aggregate statistics on their models, the selected aggregation is applied."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\"\"\"\n",
-        "If the following metrics are uploaded then\n",
-        "in the web app, users will see:\n",
-        "true positives dog = 4\n",
-        "true positives cat = 3\n",
-        "true positives = 7\n",
-        "\"\"\"\n",
-        "\n",
-        "feature_metric = ScalarMetric(metric_name=\"true_positives\",\n",
-        "                              feature_name=\"cat\",\n",
-        "                              value=3,\n",
-        "                              aggregation=ScalarMetricAggregation.SUM)\n",
-        "\n",
-        "feature_metric = ScalarMetric(metric_name=\"true_positives\",\n",
-        "                              feature_name=\"dog\",\n",
-        "                              value=4,\n",
-        "                              aggregation=ScalarMetricAggregation.SUM)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Built-in Metrics:\n",
-        "* The SDK Provides a set of default metrics that make metrics easy to use.\n",
-        "1. `confusion_matrix_metric()`\n",
-        "    * Computes a single confusion matrix metric for all the predictions and labels provided. \n",
-        "2. `miou_metric()`\n",
-        "    * Computes a single iou score for all predictions and labels provided    \n",
-        "3. `feature_confusion_matrix_metric()`\n",
-        "    * Computes the iou score for each of the classes found in the predictions and labels\n",
-        "4. `feature_miou_metric()`\n",
-        "     * Computes a confusion matrix metric for each of the classes found in the predictions and labels\n",
-        "------\n",
-        "* Note that all of these functions expect the prediction and ground truth annotations to correspond to the same data row. These functions should be called for each data row that you need metrics for."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "from labelbox.data.metrics import (feature_miou_metric, miou_metric,\n",
-        "                                   confusion_matrix_metric,\n",
-        "                                   feature_confusion_matrix_metric)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "predictions = [\n",
-        "    lb_types.ObjectAnnotation(name=\"cat\",\n",
-        "                     value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n",
-        "                                     end=lb_types.Point(x=10, y=10)))\n",
-        "]\n",
-        "\n",
-        "ground_truths = [\n",
-        "    lb_types.ObjectAnnotation(name=\"cat\",\n",
-        "                     value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0), end=lb_types.Point(x=8,\n",
-        "                                                                      y=8)))\n",
-        "]"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(feature_miou_metric(ground_truths, predictions))\n",
-        "print(miou_metric(ground_truths, predictions))\n",
-        "print(confusion_matrix_metric(ground_truths, predictions))\n",
-        "print(feature_confusion_matrix_metric(ground_truths, predictions))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Adjust iou for iou calcuations.\n",
-        "# Set it higher than 0.64 and we get a false postive and a false negative for the other ground truth object.\n",
-        "print(feature_confusion_matrix_metric(ground_truths, predictions, iou=0.9))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# subclasses are included by default\n",
-        "predictions = [\n",
-        "    lb_types.ObjectAnnotation(\n",
-        "        name=\"cat\",\n",
-        "        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0), end=lb_types.Point(x=10, y=10)),\n",
-        "        classifications=[\n",
-        "            lb_types.ClassificationAnnotation(\n",
-        "                name=\"height\",\n",
-        "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"tall\")))\n",
-        "        ])\n",
-        "]\n",
-        "\n",
-        "ground_truths = [\n",
-        "    lb_types.ObjectAnnotation(\n",
-        "        name=\"cat\",\n",
-        "        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0), end=lb_types.Point(x=10, y=10)),\n",
-        "        classifications=[\n",
-        "            lb_types.ClassificationAnnotation(\n",
-        "                name=\"height\",\n",
-        "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"short\")))\n",
-        "        ])\n",
-        "]\n",
-        "conf_matrix_metrics = feature_confusion_matrix_metric(ground_truths,\n",
-        "                                                      predictions)\n",
-        "iou_metrics = feature_confusion_matrix_metric(ground_truths,\n",
-        "                                              predictions,\n",
-        "                                              include_subclasses=False)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "print(\"Subclasses:\", conf_matrix_metrics[0].value)\n",
-        "print(\"Excluding Subclasses:\", iou_metrics[0].value)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Uploading Custom Metrics\n",
-        "* Custom metrics are uploaded the same way as any MEA upload. NDJson must be created. Fortunately this is made easy with converter functions.\n",
-        "* First construct a metric annotation in one of two ways:\n",
-        "    1. Manually\n",
-        "    2. Using one of the provided functions `feature_miou_metric`, `miou_metric`, `confusion_matrix_metric`, `feature_confusion_matrix_metric`.\n",
-        "* Then add the metric annotation to a label ( This step associates the metrics with a data row)\n",
-        "* Convert to ndjson and upload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Continuing with the last example:\n",
-        "global_key = \"<global_key>\"\n",
-        "metrics = [*conf_matrix_metrics, *iou_metrics]\n",
-        "labels = [\n",
-        "    lb_types.Label(data=lb_types.ImageData(global_key=global_key), annotations=metrics)\n",
-        "]\n",
-        "# We can upload these metric with other annotations\n",
-        "#model_run.add_predictions(f'diagnostics-import-{uuid.uuid4()}', labels)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "----\n",
+                "\n",
+                "# Model Diagnostics - Custom Metrics Basics\n",
+                "\n",
+                "\n",
+                "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
+                "    * Iterate faster\n",
+                "    * Measure and report on model quality\n",
+                "    * Understand marginal value of additional labels and modeling efforts\n",
+                "\n",
+                "\n",
+                "* For an end-to-end demo of diagnostics using custom metrics checkout this [notebook](custom_metrics_demo.ipynb)\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Environment Setup\n",
+                "\n",
+                "Install dependencies"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Import libraries"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox.types as lb_types\nimport labelbox as lb\nimport uuid\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Custom Metrics\n",
+                "* Users can provide metrics at the following levels of granularity:\n",
+                "    1. data rows\n",
+                "    2. features\n",
+                "    3. subclasses\n",
+                "* Additionally, metrics can be given custom names to best describe what they are measuring.\n",
+                "    \n",
+                "* Limits and Behavior:\n",
+                "    * At a data row cannot have more than 20 metrics\n",
+                "    * Metrics are upserted, so if a metric already exists, its value will be replaced\n",
+                "    * Metrics can have values in the range [0,100000]\n",
+                "* Currently `ScalarMetric`s and `ConfusionMatrixMetric`s are supported. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### ScalarMetric\n",
+                "    * A `ScalarMetric` is a metric with just a single scalar value."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "from labelbox.data.annotation_types import (\n    ScalarMetric,\n    ScalarMetricAggregation,\n    ConfusionMatrixMetric,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "data_row_metric = ScalarMetric(metric_name=\"iou_custom\", value=0.5)\n\nfeature_metric = ScalarMetric(metric_name=\"iou_custom\",\n                              feature_name=\"cat\",\n                              value=0.5)\n\nsubclass_metric = ScalarMetric(\n    metric_name=\"iou_custom\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=0.5,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### ConfusionMatrixMetric\n",
+                "- A `ConfusionMatrixMetric` contains 4 numbers [True postivie, False Postive, True Negative, False Negateive]\n",
+                "- Confidence is also supported a key value pairs, where the score is the key and the value is the metric value.\n",
+                "- In the user interface, these metrics are used to derive precision,recall, and f1 scores. The reason these are not directly uploaded is that the raw data allows us to do processing on the front end.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "data_row_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nfeature_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nsubclass_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Confidence\n",
+                "* Users can provide confidence scores along with metrics\n",
+                "* This enables them to explore their model performance without necessarily knowing the optimal thresholds for each class.\n",
+                "* Users can filter on confidence and value in the UI to perform powerful queries.\n",
+                "* The keys represent a confidence score (must be between 0 and 1) and the values represent either a scalar metric or for confusion matrix metrics [TP,FP,TN,FN]"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "confusion_matrix_metric_with_confidence = ConfusionMatrixMetric(\n    metric_name=\"confusion_matrix_50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value={\n        0.1: [1, 0, 1, 0],\n        0.3: [1, 0, 1, 0],\n        0.5: [1, 0, 1, 0],\n        0.7: [1, 0, 1, 0],\n        0.9: [1, 0, 1, 0],\n    },\n)\n\nscalar_metric_with_confidence = ScalarMetric(\n    metric_name=\"iou_custom\",\n    value={\n        0.1: 0.2,\n        0.3: 0.25,\n        0.5: 0.3,\n        0.7: 0.4,\n        0.9: 0.3\n    },\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Aggregations\n",
+                "* This is an optional field on the `ScalarMetric` object (by default it uses Arithmetic Mean).\n",
+                "* Aggregations occur in two cases:\n",
+                "    1. When a user provides a feature or subclass level metric, Labelbox automatically aggregates all metrics with the same parent to create a value for that parent.\n",
+                "        * E.g. A user provides cat and dog iou. The data row level metric for iou is the average of both of those.\n",
+                "        * The exception to this is when the data row level iou is explicitly set, then the aggregation will not take effect (on a per data row basis). \n",
+                "    2. When users create slices or want aggregate statistics on their models, the selected aggregation is applied."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "\"\"\"\nIf the following metrics are uploaded then\nin the web app, users will see:\ntrue positives dog = 4\ntrue positives cat = 3\ntrue positives = 7\n\"\"\"\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"cat\",\n    value=3,\n    aggregation=ScalarMetricAggregation.SUM,\n)\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"dog\",\n    value=4,\n    aggregation=ScalarMetricAggregation.SUM,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Built-in Metrics:\n",
+                "* The SDK Provides a set of default metrics that make metrics easy to use.\n",
+                "1. `confusion_matrix_metric()`\n",
+                "    * Computes a single confusion matrix metric for all the predictions and labels provided. \n",
+                "2. `miou_metric()`\n",
+                "    * Computes a single iou score for all predictions and labels provided    \n",
+                "3. `feature_confusion_matrix_metric()`\n",
+                "    * Computes the iou score for each of the classes found in the predictions and labels\n",
+                "4. `feature_miou_metric()`\n",
+                "     * Computes a confusion matrix metric for each of the classes found in the predictions and labels\n",
+                "------\n",
+                "* Note that all of these functions expect the prediction and ground truth annotations to correspond to the same data row. These functions should be called for each data row that you need metrics for."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "from labelbox.data.metrics import (\n    feature_miou_metric,\n    miou_metric,\n    confusion_matrix_metric,\n    feature_confusion_matrix_metric,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "predictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=8, y=8)),\n    )\n]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(feature_miou_metric(ground_truths, predictions))\nprint(miou_metric(ground_truths, predictions))\nprint(confusion_matrix_metric(ground_truths, predictions))\nprint(feature_confusion_matrix_metric(ground_truths, predictions))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Adjust iou for iou calcuations.\n# Set it higher than 0.64 and we get a false postive and a false negative for the other ground truth object.\nprint(feature_confusion_matrix_metric(ground_truths, predictions, iou=0.9))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# subclasses are included by default\npredictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"tall\")),\n            )\n        ],\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"short\")),\n            )\n        ],\n    )\n]\nconf_matrix_metrics = feature_confusion_matrix_metric(ground_truths,\n                                                      predictions)\niou_metrics = feature_confusion_matrix_metric(ground_truths,\n                                              predictions,\n                                              include_subclasses=False)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "print(\"Subclasses:\", conf_matrix_metrics[0].value)\nprint(\"Excluding Subclasses:\", iou_metrics[0].value)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Uploading Custom Metrics\n",
+                "* Custom metrics are uploaded the same way as any MEA upload. NDJson must be created. Fortunately this is made easy with converter functions.\n",
+                "* First construct a metric annotation in one of two ways:\n",
+                "    1. Manually\n",
+                "    2. Using one of the provided functions `feature_miou_metric`, `miou_metric`, `confusion_matrix_metric`, `feature_confusion_matrix_metric`.\n",
+                "* Then add the metric annotation to a label ( This step associates the metrics with a data row)\n",
+                "* Convert to ndjson and upload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Continuing with the last example:\nglobal_key = \"<global_key>\"\nmetrics = [*conf_matrix_metrics, *iou_metrics]\nlabels = [\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=metrics)\n]\n# We can upload these metric with other annotations\n# model_run.add_predictions(f'diagnostics-import-{uuid.uuid4()}', labels)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/custom_metrics_demo.ipynb b/examples/model_experiments/custom_metrics_demo.ipynb
index 318b382c6..a9ad4ead7 100644
--- a/examples/model_experiments/custom_metrics_demo.ipynb
+++ b/examples/model_experiments/custom_metrics_demo.ipynb
@@ -1,1243 +1,426 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "----\n",
-        "\n",
-        "# Model Diagnostics - Custom Metrics Demo\n",
-        "\n",
-        "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
-        "    * Iterate faster\n",
-        "    * Measure and report on model quality\n",
-        "    * Understand marginal value of additional labels and modeling efforts\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Environment setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q 'labelbox[data]'"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import uuid\n",
-        "import requests\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API Key\n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Radio (single-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(\n",
-        "      answer = lb_types.ClassificationAnswer(\n",
-        "        name = \"second_radio_answer\", confidence=0.5,\n",
-        "        custom_metrics=[{ 'name': 'iou', 'value': 0.1 },\n",
-        "                  { 'name': 'f1', 'value': 0.33 },\n",
-        "                  { 'name': 'precision', 'value': 0.55 },\n",
-        "                  { 'name': 'recall', 'value': 0.33 },\n",
-        "                  { 'name': 'tagsCount', 'value': 43 },\n",
-        "                  { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }],\n",
-        "        )\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_prediction_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {'name': 'first_radio_answer', 'confidence': 0.5,\n",
-        "             'customMetrics': [\n",
-        "                  { 'name': 'iou', 'value': 0.1 },\n",
-        "                  { 'name': 'f1', 'value': 0.33 },\n",
-        "                  { 'name': 'precision', 'value': 0.55 },\n",
-        "                  { 'name': 'recall', 'value': 0.33 },\n",
-        "                  { 'name': 'tagsCount', 'value': 43 },\n",
-        "                  { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "                ]\n",
-        "            }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: checklist (multi-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\",\n",
-        "            confidence=0.5,\n",
-        "            custom_metrics=[\n",
-        "            { 'name': 'iou', 'value': 0.5 },\n",
-        "            { 'name': 'f1', 'value': 0.33 },\n",
-        "            { 'name': 'precision', 'value': 0.55 },\n",
-        "            { 'name': 'recall', 'value': 0.33 },\n",
-        "            { 'name': 'tagsCount', 'value': 43 },\n",
-        "            { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\",\n",
-        "            confidence=0.5,\n",
-        "            custom_metrics= [\n",
-        "            { 'name': 'iou', 'value': 0.5 },\n",
-        "            { 'name': 'f1', 'value': 0.33 },\n",
-        "            { 'name': 'precision', 'value': 0.55 },\n",
-        "            { 'name': 'recall', 'value': 0.33 },\n",
-        "            { 'name': 'tagsCount', 'value': 43 },\n",
-        "            { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        " )\n",
-        "checklist_prediction_ndjson = {\n",
-        "  'name': 'checklist_question',\n",
-        "  'answer': [\n",
-        "    {'name': 'first_checklist_answer' , 'confidence': 0.5,\n",
-        "     'customMetrics': [\n",
-        "            { 'name': 'iou', 'value': 0.5 },\n",
-        "            { 'name': 'f1', 'value': 0.33 },\n",
-        "            { 'name': 'precision', 'value': 0.55 },\n",
-        "            { 'name': 'recall', 'value': 0.33 },\n",
-        "            { 'name': 'tagsCount', 'value': 43 },\n",
-        "            { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]},\n",
-        "    {'name': 'second_checklist_answer', 'confidence': 0.5,\n",
-        "     'customMetrics': [\n",
-        "            { 'name': 'iou', 'value': 0.5 },\n",
-        "            { 'name': 'f1', 'value': 0.33 },\n",
-        "            { 'name': 'precision', 'value': 0.55 },\n",
-        "            { 'name': 'recall', 'value': 0.33 },\n",
-        "            { 'name': 'tagsCount', 'value': 43 },\n",
-        "            { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Nested radio and checklist"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5,\n",
-        "      custom_metrics=[\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5,\n",
-        "              custom_metrics=[\n",
-        "                    { 'name': 'iou', 'value': 0.5 },\n",
-        "                    { 'name': 'f1', 'value': 0.33 },\n",
-        "                    { 'name': 'precision', 'value': 0.55 },\n",
-        "                    { 'name': 'recall', 'value': 0.33 },\n",
-        "                    { 'name': 'tagsCount', 'value': 43 },\n",
-        "                    { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "              ]\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"confidence\": 0.5 ,\n",
-        "  \"answer\": { \"name\": \"first_radio_answer\", \"confidence\": 0.5,\n",
-        "            'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]\n",
-        "      },\n",
-        "      \"classifications\" : [\n",
-        "      {\n",
-        "        \"name\": \"sub_radio_question\",\n",
-        "        \"answer\": { \"name\": \"first_sub_radio_answer\", \"confidence\": 0.5,\n",
-        "                   'customMetrics': [\n",
-        "                    { 'name': 'iou', 'value': 0.5 },\n",
-        "                    { 'name': 'f1', 'value': 0.33 },\n",
-        "                    { 'name': 'precision', 'value': 0.55 },\n",
-        "                    { 'name': 'recall', 'value': 0.33 },\n",
-        "                    { 'name': 'tagsCount', 'value': 43 },\n",
-        "                    { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "                  ]\n",
-        "                }\n",
-        "      }\n",
-        "      ]\n",
-        "}\n",
-        "\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5,\n",
-        "      custom_metrics = [\n",
-        "            { 'name': 'iou', 'value': 0.5 },\n",
-        "            { 'name': 'f1', 'value': 0.33 },\n",
-        "            { 'name': 'precision', 'value': 0.55 },\n",
-        "            { 'name': 'recall', 'value': 0.33 },\n",
-        "            { 'name': 'tagsCount', 'value': 43 },\n",
-        "            { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "        ],\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5,\n",
-        "            custom_metrics= [\n",
-        "                { 'name': 'iou', 'value': 0.5 },\n",
-        "                { 'name': 'f1', 'value': 0.33 },\n",
-        "                { 'name': 'precision', 'value': 0.55 },\n",
-        "                { 'name': 'recall', 'value': 0.33 },\n",
-        "                { 'name': 'tagsCount', 'value': 43 },\n",
-        "                { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "            ]\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"confidence\": 0.5 ,\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5,\n",
-        "      'customMetrics': [\n",
-        "            { 'name': 'iou', 'value': 0.5 },\n",
-        "            { 'name': 'f1', 'value': 0.33 },\n",
-        "            { 'name': 'precision', 'value': 0.55 },\n",
-        "            { 'name': 'recall', 'value': 0.33 },\n",
-        "            { 'name': 'tagsCount', 'value': 43 },\n",
-        "            { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "        ],\n",
-        "      \"classifications\" : [{\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\n",
-        "              \"name\": \"first_sub_checklist_answer\", \"confidence\": 0.5,\n",
-        "              'customMetrics': [\n",
-        "                { 'name': 'iou', 'value': 0.5 },\n",
-        "                { 'name': 'f1', 'value': 0.33 },\n",
-        "                { 'name': 'precision', 'value': 0.55 },\n",
-        "                { 'name': 'recall', 'value': 0.33 },\n",
-        "                { 'name': 'tagsCount', 'value': 43 },\n",
-        "                { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding Box"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "bbox_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"bounding_box\",\n",
-        "  confidence=0.5,\n",
-        "  custom_metrics= [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=1915, y=1307),  # x= left + width , y = top + height\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "\n",
-        "bbox_prediction_ndjson = {\n",
-        "  \"name\": \"bounding_box\",\n",
-        "  \"confidence\": 0.5,\n",
-        "  'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  \"bbox\": {\n",
-        "          \"top\": 977,\n",
-        "          \"left\": 1690,\n",
-        "          \"height\": 330,\n",
-        "          \"width\": 225\n",
-        "      }\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding box with nested classification "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    confidence=0.5,\n",
-        "    custom_metrics = [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.2 },\n",
-        "              { 'name': 'precision', 'value': 0.1 },\n",
-        "              { 'name': 'recall', 'value': 0.3 },\n",
-        "              { 'name': 'tagsCount', 'value': 23 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "                name=\"first_sub_radio_answer\",\n",
-        "                confidence=0.5,\n",
-        "                custom_metrics =[\n",
-        "                        { 'name': 'iou', 'value': 0.5 },\n",
-        "                        { 'name': 'f1', 'value': 0.2 },\n",
-        "                        { 'name': 'precision', 'value': 0.1 },\n",
-        "                        { 'name': 'recall', 'value': 0.3 },\n",
-        "                        { 'name': 'tagsCount', 'value': 23 },\n",
-        "                        { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "                    ]\n",
-        "            ))\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "## NDJSON\n",
-        "bbox_with_radio_subclass_prediction_ndjson = {\n",
-        "    \"name\": \"bbox_with_radio_subclass\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.2 },\n",
-        "              { 'name': 'precision', 'value': 0.1 },\n",
-        "              { 'name': 'recall', 'value': 0.3 },\n",
-        "              { 'name': 'tagsCount', 'value': 23 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"sub_radio_question\",\n",
-        "        \"answer\":\n",
-        "            { \"name\":\"first_sub_radio_answer\",\n",
-        "             \"confidence\": 0.5,\n",
-        "             'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.2 },\n",
-        "              { 'name': 'precision', 'value': 0.1 },\n",
-        "              { 'name': 'recall', 'value': 0.3 },\n",
-        "              { 'name': 'tagsCount', 'value': 23 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]}\n",
-        "\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "          \"top\": 933,\n",
-        "          \"left\": 541,\n",
-        "          \"height\": 191,\n",
-        "          \"width\": 330\n",
-        "        }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polygon"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Anotation\n",
-        "polygon_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon\",\n",
-        "  confidence = 0.5,\n",
-        "  custom_metrics = [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  value=lb_types.Polygon(\n",
-        "        points=[lb_types.Point(x=1489.581,y=183.934), lb_types.Point(x=2278.306,y=256.885), lb_types.Point(x=2428.197,y=200.437), lb_types.Point(x=2560.0,y=335.419),\n",
-        "                lb_types.Point(x=2557.386,y=503.165), lb_types.Point(x=2320.596,y=503.103), lb_types.Point(x=2156.083, y=628.943), lb_types.Point(x=2161.111,y=785.519),\n",
-        "                lb_types.Point(x=2002.115, y=894.647), lb_types.Point(x=1838.456,y=877.874), lb_types.Point(x=1436.53,y=874.636), lb_types.Point(x=1411.403,y=758.579),\n",
-        "                lb_types.Point(x=1353.853,y=751.74), lb_types.Point(x=1345.264, y=453.461), lb_types.Point(x=1426.011,y=421.129)]\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "polygon_prediction_ndjson = {\n",
-        "  \"name\": \"polygon\",\n",
-        "  \"confidence\": 0.5,\n",
-        "  'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  \"polygon\": [\n",
-        "    {\"x\": 1489.581, \"y\": 183.934},\n",
-        "    {\"x\": 2278.306, \"y\": 256.885},\n",
-        "    {\"x\": 2428.197, \"y\": 200.437},\n",
-        "    {\"x\": 2560.0, \"y\": 335.419},\n",
-        "    {\"x\": 2557.386, \"y\": 503.165},\n",
-        "    {\"x\": 2320.596, \"y\": 503.103},\n",
-        "    {\"x\": 2156.083, \"y\": 628.943},\n",
-        "    {\"x\": 2161.111, \"y\": 785.519},\n",
-        "    {\"x\": 2002.115, \"y\": 894.647},\n",
-        "    {\"x\": 1838.456, \"y\": 877.874},\n",
-        "    {\"x\": 1436.53, \"y\": 874.636},\n",
-        "    {\"x\": 1411.403, \"y\": 758.579},\n",
-        "    {\"x\": 1353.853, \"y\": 751.74},\n",
-        "    {\"x\": 1345.264, \"y\": 453.461},\n",
-        "    {\"x\": 1426.011, \"y\": 421.129},\n",
-        "    {\"x\": 1489.581, \"y\": 183.934}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",\n",
-        "  value=lb_types.Text(\n",
-        "    answer=\"sample text\",\n",
-        "    confidence=0.5,\n",
-        "    custom_metrics = [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ]\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_annotation_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "  'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  \"confidence\": 0.5\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Point"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "point_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"point\",\n",
-        "  confidence=0.5,\n",
-        "  custom_metrics= [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  value = lb_types.Point(x=1166.606, y=1441.768),\n",
-        ")\n",
-        "\n",
-        "point_prediction_ndjson = {\n",
-        "  \"name\": \"point\",\n",
-        "  \"confidence\": 0.5,\n",
-        "  'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  \"classifications\": [],\n",
-        "  \"point\": {\"x\": 1166.606, \"y\": 1441.768}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polyline"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "polyline_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline\",\n",
-        "  confidence=0.5,\n",
-        "  custom_metrics=[\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  value=lb_types.Line(\n",
-        "        points=[lb_types.Point(x=2534.353, y=249.471), lb_types.Point(x=2429.492, y=182.092), lb_types.Point(x=2294.322, y=221.962), lb_types.Point(x=2224.491, y=180.463), lb_types.Point(x=2136.123, y=204.716),\n",
-        "                lb_types.Point(x=1712.247, y=173.949), lb_types.Point(x=1703.838, y=84.438), lb_types.Point(x=1579.772, y=82.61), lb_types.Point(x=1583.442, y=167.552),\n",
-        "                lb_types.Point(x=1478.869, y=164.903), lb_types.Point(x=1418.941, y=318.149), lb_types.Point(x=1243.128, y=400.815), lb_types.Point(x=1022.067, y=319.007),\n",
-        "                lb_types.Point(x=892.367, y=379.216), lb_types.Point(x=670.273, y=364.408), lb_types.Point(x=613.114, y=288.16), lb_types.Point(x=377.559, y=238.251),\n",
-        "                lb_types.Point(x=368.087, y=185.064), lb_types.Point(x=246.557, y=167.286), lb_types.Point(x=236.648, y=285.61), lb_types.Point(x=90.929, y=326.412)]\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "\n",
-        "polyline_prediction_ndjson = {\n",
-        "  \"name\": \"polyline\",\n",
-        "  \"confidence\":0.5,\n",
-        "  'customMetrics': [\n",
-        "              { 'name': 'iou', 'value': 0.5 },\n",
-        "              { 'name': 'f1', 'value': 0.33 },\n",
-        "              { 'name': 'precision', 'value': 0.55 },\n",
-        "              { 'name': 'recall', 'value': 0.33 },\n",
-        "              { 'name': 'tagsCount', 'value': 43 },\n",
-        "              { 'name': 'metric_with_a_very_long_name', 'value': 0.334332 }\n",
-        "          ],\n",
-        "  \"classifications\": [],\n",
-        "  \"line\": [\n",
-        "    {\"x\": 2534.353, \"y\": 249.471},\n",
-        "    {\"x\": 2429.492, \"y\": 182.092},\n",
-        "    {\"x\": 2294.322, \"y\": 221.962},\n",
-        "    {\"x\": 2224.491, \"y\": 180.463},\n",
-        "    {\"x\": 2136.123, \"y\": 204.716},\n",
-        "    {\"x\": 1712.247, \"y\": 173.949},\n",
-        "    {\"x\": 1703.838, \"y\": 84.438},\n",
-        "    {\"x\": 1579.772, \"y\": 82.61},\n",
-        "    {\"x\": 1583.442, \"y\": 167.552},\n",
-        "    {\"x\": 1478.869, \"y\": 164.903},\n",
-        "    {\"x\": 1418.941, \"y\": 318.149},\n",
-        "    {\"x\": 1243.128, \"y\": 400.815},\n",
-        "    {\"x\": 1022.067, \"y\": 319.007},\n",
-        "    {\"x\": 892.367, \"y\": 379.216},\n",
-        "    {\"x\": 670.273, \"y\": 364.408},\n",
-        "    {\"x\": 613.114, \"y\": 288.16},\n",
-        "    {\"x\": 377.559, \"y\": 238.251},\n",
-        "    {\"x\": 368.087, \"y\": 185.064},\n",
-        "    {\"x\": 246.557, \"y\": 167.286},\n",
-        "    {\"x\": 236.648, \"y\": 285.61},\n",
-        "    {\"x\": 90.929, \"y\": 326.412}\n",
-        "  ]\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as batch to the project\n",
-        "global_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n",
-        "test_img_urls = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"Custom metrics demo\", iam_integration=None)\n",
-        "task = dataset.create_data_rows([test_img_urls])\n",
-        "\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\"\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ]\n",
-        "            )\n",
-        "          ]\n",
-        "        ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  ],\n",
-        "  tools=[ # List of tools\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.BBOX,\n",
-        "      name=\"bounding_box\"),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.BBOX,\n",
-        "      name=\"bbox_with_radio_subclass\",\n",
-        "      classifications=[\n",
-        "            lb.Classification(\n",
-        "                class_type=lb.Classification.Type.RADIO,\n",
-        "                name=\"sub_radio_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                ]\n",
-        "              ),\n",
-        "        ]\n",
-        "      ),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.POLYGON,\n",
-        "      name=\"polygon\"),\n",
-        " \t  lb.Tool(\n",
-        "      tool=lb.Tool.Type.POINT,\n",
-        "      name=\"point\"),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.LINE,\n",
-        "      name=\"polyline\")]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Image Prediction Import Demo\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Image\n",
-        "                                  )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"model_with_aggregated_custom_metrics\" + str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5. Create the predictions payload\n",
-        "\n",
-        "Create the prediction payload using the snippets of code in ***Supported Predictions*** section.\n",
-        "\n",
-        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label for predictions\n",
-        "label_prediction = []\n",
-        "label_prediction.append(lb_types.Label(\n",
-        "    data=lb_types.ImageData(global_key=global_key),\n",
-        "    annotations = [\n",
-        "      radio_prediction,\n",
-        "      nested_radio_prediction,\n",
-        "      checklist_prediction,\n",
-        "      nested_checklist_prediction,\n",
-        "      bbox_prediction,\n",
-        "      bbox_with_radio_subclass_prediction,\n",
-        "      polyline_prediction,\n",
-        "      polygon_prediction,\n",
-        "      point_prediction,\n",
-        "      text_annotation\n",
-        "\n",
-        "      ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "If using NDJSON"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_prediction_ndjson = []\n",
-        "\n",
-        "for annot in [\n",
-        "    radio_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    bbox_prediction_ndjson,\n",
-        "    bbox_with_radio_subclass_prediction_ndjson,\n",
-        "    polygon_prediction_ndjson,\n",
-        "    point_prediction_ndjson,\n",
-        "    polyline_prediction_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    nested_radio_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson\n",
-        "]:\n",
-        "  annot.update({\n",
-        "      \"dataRow\": {\"globalKey\": global_key}\n",
-        "  })\n",
-        "  label_prediction_ndjson.append(annot)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6. Upload the predictions payload to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_prediction)\n",
-        "\n",
-        "# Errors will appear for prediction uploads that failed.\n",
-        "print(\"Errors:\",  upload_job_prediction.errors)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to a model run\n",
-        "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations.\n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.1. Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"image_prediction_many_kinds\",\n",
-        "                                    media_type=lb.MediaType.Image)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.2. Create a batch to send to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_predictions_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########### Annotations ###########\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"second_radio_answer\"))\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(answer=[\n",
-        "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n",
-        "    ])\n",
-        ")\n",
-        "\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"bounding_box\",\n",
-        "  value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=1915, y=1307),  # x= left + width , y = top + height\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_sub_radio_answer\", confidence=0.5))\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "polygon_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon\",\n",
-        "  value=lb_types.Polygon(\n",
-        "        points=[lb_types.Point(x=1489.581,y=183.934), lb_types.Point(x=2278.306,y=256.885), lb_types.Point(x=2428.197,y=200.437), lb_types.Point(x=2560.0,y=335.419),\n",
-        "                lb_types.Point(x=2557.386,y=503.165), lb_types.Point(x=2320.596,y=503.103), lb_types.Point(x=2156.083, y=628.943), lb_types.Point(x=2161.111,y=785.519),\n",
-        "                lb_types.Point(x=2002.115, y=894.647), lb_types.Point(x=1838.456,y=877.874), lb_types.Point(x=1436.53,y=874.636), lb_types.Point(x=1411.403,y=758.579),\n",
-        "                lb_types.Point(x=1353.853,y=751.74), lb_types.Point(x=1345.264, y=453.461), lb_types.Point(x=1426.011,y=421.129)]\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"free_text\",\n",
-        "    value=lb_types.Text(answer=\"sample text\")\n",
-        ")\n",
-        "\n",
-        "point_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"point\",\n",
-        "  value = lb_types.Point(x=1166.606, y=1441.768),\n",
-        ")\n",
-        "\n",
-        "polyline_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline\",\n",
-        "  value=lb_types.Line(\n",
-        "        points=[lb_types.Point(x=2534.353, y=249.471), lb_types.Point(x=2429.492, y=182.092), lb_types.Point(x=2294.322, y=221.962), lb_types.Point(x=2224.491, y=180.463), lb_types.Point(x=2136.123, y=204.716),\n",
-        "                lb_types.Point(x=1712.247, y=173.949), lb_types.Point(x=1703.838, y=84.438), lb_types.Point(x=1579.772, y=82.61), lb_types.Point(x=1583.442, y=167.552),\n",
-        "                lb_types.Point(x=1478.869, y=164.903), lb_types.Point(x=1418.941, y=318.149), lb_types.Point(x=1243.128, y=400.815), lb_types.Point(x=1022.067, y=319.007),\n",
-        "                lb_types.Point(x=892.367, y=379.216), lb_types.Point(x=670.273, y=364.408), lb_types.Point(x=613.114, y=288.16), lb_types.Point(x=377.559, y=238.251),\n",
-        "                lb_types.Point(x=368.087, y=185.064), lb_types.Point(x=246.557, y=167.286), lb_types.Point(x=236.648, y=285.61), lb_types.Point(x=90.929, y=326.412)]\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.4. Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n",
-        "label = []\n",
-        "annotations = [\n",
-        "    radio_annotation,\n",
-        "    nested_radio_annotation,\n",
-        "    checklist_annotation,\n",
-        "    nested_checklist_annotation,\n",
-        "    text_annotation,\n",
-        "    bbox_annotation,\n",
-        "    bbox_with_radio_subclass_annotation,\n",
-        "    polygon_annotation,\n",
-        "    point_annotation,\n",
-        "    polyline_annotation\n",
-        "]\n",
-        "label.append(\n",
-        "    lb_types.Label(\n",
-        "        data=lb_types.ImageData(global_key=global_key),\n",
-        "        annotations=annotations))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.5. Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"annotation_import_\" + str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.6 Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Optional deletions for cleanup\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "----\n",
+                "\n",
+                "# Model Diagnostics - Custom Metrics Demo\n",
+                "\n",
+                "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
+                "    * Iterate faster\n",
+                "    * Measure and report on model quality\n",
+                "    * Understand marginal value of additional labels and modeling efforts\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Environment setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q 'labelbox[data]'",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API Key\n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Radio (single-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    )),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: checklist (multi-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n        lb_types.ClassificationAnswer(\n            name=\"second_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n    ]),\n)\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\":\n                \"first_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n        {\n            \"name\":\n                \"second_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Nested radio and checklist"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=0.5,\n                    custom_metrics=[\n                        {\n                            \"name\": \"iou\",\n                            \"value\": 0.5\n                        },\n                        {\n                            \"name\": \"f1\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"precision\",\n                            \"value\": 0.55\n                        },\n                        {\n                            \"name\": \"recall\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"tagsCount\",\n                            \"value\": 43\n                        },\n                        {\n                            \"name\": \"metric_with_a_very_long_name\",\n                            \"value\": 0.334332,\n                        },\n                    ],\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\":\n        \"nested_radio_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                            custom_metrics=[\n                                {\n                                    \"name\": \"iou\",\n                                    \"value\": 0.5\n                                },\n                                {\n                                    \"name\": \"f1\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"precision\",\n                                    \"value\": 0.55\n                                },\n                                {\n                                    \"name\": \"recall\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"tagsCount\",\n                                    \"value\": 43\n                                },\n                                {\n                                    \"name\": \"metric_with_a_very_long_name\",\n                                    \"value\": 0.334332,\n                                },\n                            ],\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\":\n                    \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,\n                \"customMetrics\": [\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.55\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 43\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding Box"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "bbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding box with nested classification "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                custom_metrics=[\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.2\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.1\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.3\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 23\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            )),\n        )\n    ],\n)\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.2\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.1\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.3\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 23\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polygon"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",\n    value=lb_types.Text(\n        answer=\"sample text\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    ),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Point"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polyline"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "polyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\ntest_img_urls = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"Custom metrics demo\",\n                                iam_integration=None)\ntask = dataset.create_data_rows([test_img_urls])\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(\n    name=\"model_with_aggregated_custom_metrics\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5. Create the predictions payload\n",
+                "\n",
+                "Create the prediction payload using the snippets of code in ***Supported Predictions*** section.\n",
+                "\n",
+                "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "If using NDJSON"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6. Upload the predictions payload to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to a model run\n",
+                "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations.\n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.1. Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"image_prediction_many_kinds\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.2. Create a batch to send to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.4. Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.5. Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.6 Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Optional deletions for cleanup\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/model_predictions_to_project.ipynb b/examples/model_experiments/model_predictions_to_project.ipynb
index ed7ebc3d4..9cb07e3b4 100644
--- a/examples/model_experiments/model_predictions_to_project.ipynb
+++ b/examples/model_experiments/model_predictions_to_project.ipynb
@@ -1,407 +1,272 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Import Model Run Predictions to a Project\n",
-        "Throughout the process of training your machine learning (ML) model, you may want to export your model-run predictions and import them to your new project. In this notebook, we will demonstrate the process on how to get those predictions moved over."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "from labelbox.schema.conflict_resolution_strategy import ConflictResolutionStrategy\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## API Key and Client\n",
-        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Creating Model Experiment\n",
-        "\n",
-        "In order to interact with Model Run predictions, you must create a Model Experiment with a Model Run and then add predictions. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information.\n",
-        "\n",
-        "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Ontology"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "In this example we are making a simple ontology with a classification feature. The classification feature has two options: option 1 and option 2."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "classification_features = [\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.CHECKLIST,\n",
-        "        name=\"Demo Feature\",\n",
-        "        options=[\n",
-        "            lb.Option(value=\"option 1\"),\n",
-        "            lb.Option(value=\"option 2\")\n",
-        "        ]\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[],\n",
-        "    classifications=classification_features\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\n",
-        "  \"Demo Ontology\",\n",
-        "  ontology_builder.asdict(),\n",
-        "  media_type=lb.MediaType.Image\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Experiment"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model = client.create_model(\n",
-        "  name = f\"Model Experiment Demo {str(uuid.uuid4())}\",\n",
-        "  ontology_id=ontology.uid\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Creating a Model Run from Model Experiment\n",
-        "\n",
-        "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Dataset and Data Rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as data row for a dataset\n",
-        "global_key = \"2560px-Kitano_Street_Kobe01s5s4110\"\n",
-        "\n",
-        "test_img_url = {\n",
-        "    \"row_data\":\n",
-        "        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n",
-        "    \"global_key\":\n",
-        "        global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"foundry-demo-dataset\")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Create Model Run and Attach Data Rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run_name = f\"Model Run Demo {str(uuid.uuid4())}\"\n",
-        "\n",
-        "model_run = model.create_model_run(name=model_run_name)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Add Predictions\n",
-        "In the below code snippet we are adding a sample predictions and attaching them to our data row inside our model run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Demo Feature\",\n",
-        "    value=lb_types.Checklist(answer = [lb_types.ClassificationAnswer(name = \"option 1\", confidence=0.5)])\n",
-        ")\n",
-        "\n",
-        "# Create prediction label\n",
-        "label_prediction = [\n",
-        "    lb_types.Label(\n",
-        "        data = lb_types.ImageData(global_key=global_key),\n",
-        "        annotations = [checklist_prediction]\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_prediction)\n",
-        "\n",
-        "# Errors will appear for prediction uploads that failed.\n",
-        "print(\"Errors:\",  upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Setup Project and Add Predictions\n",
-        "In the steps below we will be creating our target project and setting up the project with the ontology we used with our model run. See [Project](https://docs.labelbox.com/reference/dataset) for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a new project\n",
-        "project = client.create_project(\n",
-        "    name=\"Model Run Import Demo Project\",\n",
-        "    media_type=lb.MediaType.Image\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Setup Ontology\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Ontology Mapping\n",
-        "To send prediction to your annotate project you will need to provide a ontology mapping python dictionary item. This matches ontology feature id to another. You would use this if your ontology was different from your model run to your project. In our case, since we are using the same ontology, you would just need to map the same feature id to each other."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Get ontology dictionary to obtain featureSchemaIds\n",
-        "ontology_normalized = ontology.normalized\n",
-        "\n",
-        "PREDICTIONS_ONTOLOGY_MAPPING = {\n",
-        "    ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:ontology_normalized[\"classifications\"][0][\"featureSchemaId\"], # Classification featureSchemaID\n",
-        "    ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"], # Different Classification Answer featureSchemaIDs\n",
-        "    ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Send Model Predictions from Model Run to Annotate\n",
-        "To send our predictions to our project we will be using the `send_to_annotate_from_model` method from our project. See [Foundry apps](https://docs.labelbox.com/reference/foundry-2#send-foundry-generated-annotations-from-catalog-to-annotate) for more information.\n",
-        "##### Parameters\n",
-        "\n",
-        "When you send predicted data rows to annotate from a model run, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
-        "\n",
-        "* `predictions_ontology_mapping`\n",
-        "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
-        "* `exclude_data_rows_in_project`\n",
-        "    - Excludes data rows that are already in the project. \n",
-        "* `override_existing_annotations_rule` \n",
-        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
-        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-        "        * Options include:\n",
-        "            * ConflictResolutionStrategy.KeepExisting\n",
-        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-        "* `param batch_priority`\n",
-        "    - The priority of the batch.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Import Predictions as pre-labels"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "send_to_annotations_params = {\n",
-        "    \"predictions_ontology_mapping\": PREDICTIONS_ONTOLOGY_MAPPING,\n",
-        "    \"exclude_data_rows_in_project\": False,\n",
-        "    \"override_existing_annotations_rule\": ConflictResolutionStrategy.OverrideWithPredictions,\n",
-        "    \"batch_priority\": 5,\n",
-        "}\n",
-        "\n",
-        "# Send the predictions as pre-labels\n",
-        "queue_id = [queue.uid for queue in project.task_queues() if queue.queue_type == \"INITIAL_LABELING_QUEUE\" ][0]\n",
-        "\n",
-        "task = model_run.send_to_annotate_from_model(\n",
-        "    destination_project_id=project.uid,\n",
-        "    task_queue_id=queue_id, # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths .\n",
-        "    batch_name=\"Prediction Import Demo Batch\",\n",
-        "    data_rows=lb.GlobalKeys(\n",
-        "        [global_key] # Provide a list of global keys from foundry app task\n",
-        "    ),\n",
-        "    params=send_to_annotations_params\n",
-        "    )\n",
-        "\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#project.delete()\n",
-        "#dataset.delete()\n",
-        "#model_run.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Import Model Run Predictions to a Project\n",
+                "Throughout the process of training your machine learning (ML) model, you may want to export your model-run predictions and import them to your new project. In this notebook, we will demonstrate the process on how to get those predictions moved over."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API Key and Client\n",
+                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Creating Model Experiment\n",
+                "\n",
+                "In order to interact with Model Run predictions, you must create a Model Experiment with a Model Run and then add predictions. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information.\n",
+                "\n",
+                "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Ontology"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "In this example we are making a simple ontology with a classification feature. The classification feature has two options: option 1 and option 2."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Demo Feature\",\n        options=[lb.Option(value=\"option 1\"),\n                 lb.Option(value=\"option 2\")],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\"Demo Ontology\",\n                                  ontology_builder.asdict(),\n                                  media_type=lb.MediaType.Image)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Experiment"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model = client.create_model(name=f\"Model Experiment Demo {str(uuid.uuid4())}\",\n                            ontology_id=ontology.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Creating a Model Run from Model Experiment\n",
+                "\n",
+                "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Dataset and Data Rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as data row for a dataset\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create Model Run and Attach Data Rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run_name = f\"Model Run Demo {str(uuid.uuid4())}\"\n\nmodel_run = model.create_model_run(name=model_run_name)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Add Predictions\n",
+                "In the below code snippet we are adding a sample predictions and attaching them to our data row inside our model run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"Demo Feature\",\n    value=lb_types.Checklist(\n        answer=[lb_types.ClassificationAnswer(name=\"option 1\", confidence=0.5)\n               ]),\n)\n\n# Create prediction label\nlabel_prediction = [\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[checklist_prediction],\n    )\n]\n\n# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Setup Project and Add Predictions\n",
+                "In the steps below we will be creating our target project and setting up the project with the ontology we used with our model run. See [Project](https://docs.labelbox.com/reference/dataset) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a new project\nproject = client.create_project(name=\"Model Run Import Demo Project\",\n                                media_type=lb.MediaType.Image)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Setup Ontology\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Ontology Mapping\n",
+                "To send prediction to your annotate project you will need to provide a ontology mapping python dictionary item. This matches ontology feature id to another. You would use this if your ontology was different from your model run to your project. In our case, since we are using the same ontology, you would just need to map the same feature id to each other."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get ontology dictionary to obtain featureSchemaIds\nontology_normalized = ontology.normalized\n\nPREDICTIONS_ONTOLOGY_MAPPING = {\n    ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Send Model Predictions from Model Run to Annotate\n",
+                "To send our predictions to our project we will be using the `send_to_annotate_from_model` method from our project. See [Foundry apps](https://docs.labelbox.com/reference/foundry-2#send-foundry-generated-annotations-from-catalog-to-annotate) for more information.\n",
+                "##### Parameters\n",
+                "\n",
+                "When you send predicted data rows to annotate from a model run, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
+                "\n",
+                "* `predictions_ontology_mapping`\n",
+                "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
+                "* `exclude_data_rows_in_project`\n",
+                "    - Excludes data rows that are already in the project. \n",
+                "* `override_existing_annotations_rule` \n",
+                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
+                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+                "        * Options include:\n",
+                "            * ConflictResolutionStrategy.KeepExisting\n",
+                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+                "* `param batch_priority`\n",
+                "    - The priority of the batch.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Import Predictions as pre-labels"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "send_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Send the predictions as pre-labels\nqueue_id = [\n    queue.uid\n    for queue in project.task_queues()\n    if queue.queue_type == \"INITIAL_LABELING_QUEUE\"\n][0]\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths .\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/model_slices.ipynb b/examples/model_experiments/model_slices.ipynb
index 3fdd61110..87c760328 100644
--- a/examples/model_experiments/model_slices.ipynb
+++ b/examples/model_experiments/model_slices.ipynb
@@ -1,338 +1,247 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Model Slices\n",
-        "\n",
-        "Model Slice functions similarly to Catalog Slice, with both essentially being saved searches. However, there are key differences in their functionalities. While Catalog Slice searches within a specific data catalog, Model Slice extends its data row search across a model run in a model. You can construct a Model Slice by using one or more filters to curate a collection of data rows. Often users will combine filters to surface high-impact data and then save the results as a Model Slice.\n",
-        "\n",
-        "This notebook is used to go over some common Labelbox SDK methods to interact with Model Slices created through the Labelbox platform.\n",
-        "\n",
-        "See [Slices](https://docs.labelbox.com/docs/slices-1) for more information on modifying Model Slices."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install labelbox"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## API Key and Client\n",
-        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your API key\n",
-        "API_KEY = \"\"\n",
-        "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create Model Slice\n",
-        "\n",
-        "In order to interact with model slices, you must create a Model Experiment with a Model Run and then create a Model Slice through the platform. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Creating Model Experiment\n",
-        "\n",
-        "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Ontology"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "classification_features = [\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.CHECKLIST,\n",
-        "        name=\"Quality Issues\",\n",
-        "        options=[\n",
-        "            lb.Option(value=\"blurry\", label=\"Blurry\"),\n",
-        "            lb.Option(value=\"distorted\", label=\"Distorted\")\n",
-        "        ]\n",
-        "    )\n",
-        "\n",
-        "]\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[],\n",
-        "    classifications=classification_features\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\n",
-        "  \"Ontology from new features\",\n",
-        "  ontology_builder.asdict(),\n",
-        "  media_type=lb.MediaType.Image\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Experiment"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model = client.create_model(\n",
-        "  name=\"Model Slice Demo\",\n",
-        "  ontology_id=ontology.uid\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Creating a Model Run from Model Experiment\n",
-        "\n",
-        "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Dataset and Data Rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as data row for a dataset\n",
-        "global_key = str(uuid.uuid4())\n",
-        "\n",
-        "test_img_url = {\n",
-        "    \"row_data\":\n",
-        "        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n",
-        "    \"global_key\":\n",
-        "        global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"foundry-demo-dataset\")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model Run and Attach Data Rows"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run_name = \"Model Slice Demo\"\n",
-        "example_config = {\n",
-        "    \"learning_rate\": 0.001,\n",
-        "    \"batch_size\": 32,\n",
-        "}\n",
-        "model_run = model.create_model_run(name=model_run_name, config=example_config)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Make Model Slice Inside UI\n",
-        "\n",
-        "Creating a Model Slice is currently not supported through the SDK, however, to showcase how to interact with Model Slice, we are going to generate a Model Slice through the UI.\n",
-        "\n",
-        "#### Workflow\n",
-        "\n",
-        "1. Navigate to ***Model*** section of the Labelbox Platform, select the ***Experiment*** type, and select the Model Experiment that was created.\n",
-        "2. You must have a filter created in order to save a slice. For this example, click ***Search your data*** dropdown and then ***Data row***.\n",
-        "3. Change ***is one of*** dropdown to ***is not one of*** then type \"test\" into the ***Search for an id*** search box.\n",
-        "4. Hit ***Enter*** and select ***Save slice***.\n",
-        "5. Give the slice a name and select ***Save***.\n",
-        "6. Above the ***Search your data*** dropdown you will see your slice's name. Select that dropdown and click ***Copy slice ID***.\n",
-        "7. Paste the ***Slice ID*** below."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "SLICE_ID = \"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get Model Slice"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_slice = client.get_model_slice(SLICE_ID)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Obtain Data Row IDs from Model Slice"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_row_ids = model_slice.get_data_row_ids(model_run.uid)\n",
-        "\n",
-        "for data_row_id in data_row_ids:\n",
-        "    print(data_row_id)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Obtain Data Row Identifiers Objects"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "data_rows = model_slice.get_data_row_identifiers(model_run.uid)\n",
-        "\n",
-        "for data_row in data_rows:\n",
-        "    print(data_row)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Model Slice Attributes"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# name (str)\n",
-        "model_slice.name\n",
-        "\n",
-        "# description (str)\n",
-        "model_slice.description\n",
-        "\n",
-        "# updated at (datetime)\n",
-        "model_slice.updated_at\n",
-        "\n",
-        "# created at (datetime)\n",
-        "model_slice.created_at\n",
-        "\n",
-        "# filter (list[dict])\n",
-        "model_slice.filter"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Model Slices\n",
+                "\n",
+                "Model Slice functions similarly to Catalog Slice, with both essentially being saved searches. However, there are key differences in their functionalities. While Catalog Slice searches within a specific data catalog, Model Slice extends its data row search across a model run in a model. You can construct a Model Slice by using one or more filters to curate a collection of data rows. Often users will combine filters to surface high-impact data and then save the results as a Model Slice.\n",
+                "\n",
+                "This notebook is used to go over some common Labelbox SDK methods to interact with Model Slices created through the Labelbox platform.\n",
+                "\n",
+                "See [Slices](https://docs.labelbox.com/docs/slices-1) for more information on modifying Model Slices."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install labelbox",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API Key and Client\n",
+                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create Model Slice\n",
+                "\n",
+                "In order to interact with model slices, you must create a Model Experiment with a Model Run and then create a Model Slice through the platform. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Creating Model Experiment\n",
+                "\n",
+                "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Ontology"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Experiment"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model = client.create_model(name=\"Model Slice Demo\", ontology_id=ontology.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Creating a Model Run from Model Experiment\n",
+                "\n",
+                "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Dataset and Data Rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Model Run and Attach Data Rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run_name = \"Model Slice Demo\"\nexample_config = {\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n}\nmodel_run = model.create_model_run(name=model_run_name, config=example_config)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Make Model Slice Inside UI\n",
+                "\n",
+                "Creating a Model Slice is currently not supported through the SDK, however, to showcase how to interact with Model Slice, we are going to generate a Model Slice through the UI.\n",
+                "\n",
+                "#### Workflow\n",
+                "\n",
+                "1. Navigate to ***Model*** section of the Labelbox Platform, select the ***Experiment*** type, and select the Model Experiment that was created.\n",
+                "2. You must have a filter created in order to save a slice. For this example, click ***Search your data*** dropdown and then ***Data row***.\n",
+                "3. Change ***is one of*** dropdown to ***is not one of*** then type \"test\" into the ***Search for an id*** search box.\n",
+                "4. Hit ***Enter*** and select ***Save slice***.\n",
+                "5. Give the slice a name and select ***Save***.\n",
+                "6. Above the ***Search your data*** dropdown you will see your slice's name. Select that dropdown and click ***Copy slice ID***.\n",
+                "7. Paste the ***Slice ID*** below."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "SLICE_ID = \"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Get Model Slice"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_slice = client.get_model_slice(SLICE_ID)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Obtain Data Row IDs from Model Slice"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "data_row_ids = model_slice.get_data_row_ids(model_run.uid)\n\nfor data_row_id in data_row_ids:\n    print(data_row_id)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Obtain Data Row Identifiers Objects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "data_rows = model_slice.get_data_row_identifiers(model_run.uid)\n\nfor data_row in data_rows:\n    print(data_row)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Model Slice Attributes"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# name (str)\nmodel_slice.name\n\n# description (str)\nmodel_slice.description\n\n# updated at (datetime)\nmodel_slice.updated_at\n\n# created at (datetime)\nmodel_slice.created_at\n\n# filter (list[dict])\nmodel_slice.filter",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index fdeed6417..a849ba4a5 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -1,828 +1,388 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "\n",
-        "<td>\n",
-        "\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# LLM pairwise comparison with Conversational text using Model\n",
-        "\n",
-        "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install \"labelbox[data]\" -q"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Set up"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Replace with your API Key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Supported annotations for conversational text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Entity"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ner_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"ner\",\n",
-        "    confidence=0.5,\n",
-        "    value=lb_types.ConversationEntity(\n",
-        "        start=0,\n",
-        "        end=8,\n",
-        "        message_id=\"message-1\"\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "ner_prediction_ndjson = {\n",
-        "        \"name\": \"ner\",\n",
-        "        \"confidence\": 0.5,\n",
-        "        \"location\": {\n",
-        "            \"start\": 0,\n",
-        "            \"end\": 8\n",
-        "        },\n",
-        "        \"messageId\": \"message-1\"\n",
-        "    }"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Radio (single-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Choose the best response\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"Response B\", confidence=0.5)))\n",
-        "\n",
-        "\n",
-        "\n",
-        "radio_prediction_ndjson = {\n",
-        "    \"name\": \"Choose the best response\",\n",
-        "    \"answer\": {\n",
-        "      \"name\": \"Response B\",\n",
-        "      \"confidence\": 0.5\n",
-        "    }\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "text_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Provide a reason for your choice\",\n",
-        "    value=lb_types.Text(answer=\"the answer to the text questions right here\", confidence=0.5)\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_prediction_ndjson = {\n",
-        "    \"name\": \"Provide a reason for your choice\",\n",
-        "    \"answer\": \"This is the more concise answer\",\n",
-        "    \"confidence\": 0.5\n",
-        "\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Checklist (multi-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "checklist_prediction= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_convo\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  message_id=\"message-1\" # Message specific annotation\n",
-        " )\n",
-        "\n",
-        "\n",
-        "checklist_prediction_ndjson = {\n",
-        "    \"name\": \"checklist_convo\",\n",
-        "    \"answers\": [\n",
-        "        {\"name\": \"first_checklist_answer\",\"confidence\":0.5},\n",
-        "        {\"name\": \"second_checklist_answer\",\"confidence\":0.5}\n",
-        "    ],\n",
-        "    \"messageId\": \"message-1\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Nested radio and checklist"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# Message based\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  message_id=\"message-1\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5 # Confidence scores should be added to the answer\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "# Message based\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"messageId\": \"message-1\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\n",
-        "            \"name\": \"first_sub_checklist_answer\",\n",
-        "            \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "          }\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}\n",
-        "# Global\n",
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5 # Confidence scores should be added to the answer\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "#Global\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"confidence\": 0.5,\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\",\n",
-        "                     \"confidence\": 0.5}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
-        "\n",
-        "```\n",
-        "\"modelOutputs\" : [\n",
-        "  {\n",
-        "      \"title\": \"Name of the response option\",\n",
-        "      \"content\": \"Content of the response\",\n",
-        "      \"modelConfigName\": \"Name of model configuration\"\n",
-        "  }\n",
-        "]\n",
-        "```\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Example of row_data with model outputs"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "pairwise_shopping_2 =  \"\"\"\n",
-        " {\n",
-        "  \"type\": \"application/vnd.labelbox.conversational\",\n",
-        "  \"version\": 1,\n",
-        "  \"messages\": [\n",
-        "    {\n",
-        "      \"messageId\": \"message-0\",\n",
-        "      \"timestampUsec\": 1530718491,\n",
-        "      \"content\": \"Hi! How can I help?\",\n",
-        "      \"user\": {\n",
-        "        \"userId\": \"Bot 002\",\n",
-        "        \"name\": \"Bot\"\n",
-        "      },\n",
-        "      \"align\": \"left\",\n",
-        "      \"canLabel\": false\n",
-        "    },\n",
-        "    {\n",
-        "      \"messageId\": \"message-1\",\n",
-        "      \"timestampUsec\": 1530718503,\n",
-        "      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n",
-        "      \"user\": {\n",
-        "        \"userId\": \"User 00686\",\n",
-        "        \"name\": \"User\"\n",
-        "      },\n",
-        "      \"align\": \"right\",\n",
-        "      \"canLabel\": true\n",
-        "    }\n",
-        "\n",
-        "  ],\n",
-        "  \"modelOutputs\": [\n",
-        "    {\n",
-        "      \"title\": \"Response A\",\n",
-        "      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n",
-        "      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n",
-        "    },\n",
-        "    {\n",
-        "      \"title\": \"Response B\",\n",
-        "      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n",
-        "      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n",
-        "    }\n",
-        "  ]\n",
-        "}\n",
-        "\"\"\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"pairwise_shooping_asset\"\n",
-        "convo_data =  {\n",
-        "        \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n",
-        "        \"global_key\": global_key\n",
-        "}\n",
-        "# Create a dataset\n",
-        "dataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n",
-        "# Create a datarows\n",
-        "task = dataset.create_data_rows([convo_data])\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create an ontology with relevant classifications\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  tools=[\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER,name=\"ner\"),\n",
-        "  ],\n",
-        "  classifications=[\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      scope=lb.Classification.Scope.GLOBAL,\n",
-        "      name=\"Choose the best response\",\n",
-        "      options=[lb.Option(value=\"Response A\"), lb.Option(value=\"Response B\"), lb.Option(value=\"Tie\")]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"Provide a reason for your choice\"\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      name=\"checklist_convo\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      scope = lb.Classification.Scope.INDEX,\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ])\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        scope = lb.Classification.Scope.GLOBAL,\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ])\n",
-        "          ]\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Pairwise comparison ontology\", ontology_builder.asdict(), media_type=lb.MediaType.Conversational)\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create model\n",
-        "model = client.create_model(name=\"Comparison_model_run_\"+ str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create model run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Step 5: Create the predictions payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_prediction = []\n",
-        "label_prediction.append(lb_types.Label(\n",
-        "  data= {\"global_key\": global_key},\n",
-        "  annotations= [\n",
-        "    ner_prediction,\n",
-        "    text_prediction,\n",
-        "    checklist_prediction,\n",
-        "    radio_prediction,\n",
-        "    nested_radio_prediction,\n",
-        "    nested_checklist_prediction\n",
-        "  ]\n",
-        "))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Setup the payload with the annotations that were created in Step 1."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for annotations in [\n",
-        "    ner_prediction_ndjson,\n",
-        "    text_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    radio_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson\n",
-        "    ]:\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6: Upload the predictions payload to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_prediction)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.1 Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(name=\"Conversational Human Evaluation Demo\",\n",
-        "                                    media_type=lb.MediaType.Conversational)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.2 Create a batch to send to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_convo_prediction_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ner_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"ner\",\n",
-        "    value=lb_types.ConversationEntity(\n",
-        "        start=0,\n",
-        "        end=8,\n",
-        "        message_id=\"message-1\"\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Choose the best response\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"Response B\"))\n",
-        ")\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"Provide a reason for your choice\",\n",
-        "    value=lb_types.Text(answer=\"the answer to the text questions right here\")\n",
-        ")\n",
-        "\n",
-        "checklist_annotation= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_convo\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  message_id=\"message-1\" # Message specific annotation\n",
-        " )\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  message_id=\"message-1\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.4 Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_annotation = []\n",
-        "label_annotation.append(lb_types.Label(\n",
-        "  data=lb_types.ConversationData(\n",
-        "    global_key=global_key\n",
-        "  ),\n",
-        "  annotations= [\n",
-        "    ner_annotation,\n",
-        "    text_annotation,\n",
-        "    checklist_annotation,\n",
-        "    radio_annotation,\n",
-        "    nested_radio_annotation,\n",
-        "    nested_checklist_annotation\n",
-        "  ]\n",
-        "))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.5 Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"label_import_job\"+ str(uuid.uuid4()),\n",
-        "    labels=label_annotation)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.6 Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Option deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# LLM pairwise comparison with Conversational text using Model\n",
+                "\n",
+                "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install \"labelbox[data]\" -q",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Set up"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Replace with your API Key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Supported annotations for conversational text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Entity"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Radio (single-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Checklist (multi-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Nested radio and checklist"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
+                "\n",
+                "```\n",
+                "\"modelOutputs\" : [\n",
+                "  {\n",
+                "      \"title\": \"Name of the response option\",\n",
+                "      \"content\": \"Content of the response\",\n",
+                "      \"modelConfigName\": \"Name of model configuration\"\n",
+                "  }\n",
+                "]\n",
+                "```\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Example of row_data with model outputs"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Step 5: Create the predictions payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Setup the payload with the annotations that were created in Step 1."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6: Upload the predictions payload to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.1 Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.2 Create a batch to send to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.4 Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.5 Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.6 Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Option deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_predictions.ipynb b/examples/prediction_upload/conversational_predictions.ipynb
index 1d265e780..c2063cc8d 100644
--- a/examples/prediction_upload/conversational_predictions.ipynb
+++ b/examples/prediction_upload/conversational_predictions.ipynb
@@ -1,784 +1,359 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Conversational Text Prediction Import\n",
-        "* This notebook will provide examples of each supported prediction type for conversational text assets, and also cover MAL and Label Import methods:\n",
-        "\n",
-        "Suported annotations that can be uploaded through the SDK\n",
-        "\n",
-        "* Classification Radio \n",
-        "* Classification Checklist \n",
-        "* Classification Free Text \n",
-        "* NER\n",
-        "\n",
-        "**Not** supported annotations\n",
-        "\n",
-        "* Bouding box \n",
-        "* Polygon \n",
-        "* Point\n",
-        "* Polyline \n",
-        "* Segmentation Mask \n",
-        "* Relationships\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import uuid\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Add your api key\n",
-        "API_KEY = \"\"\n",
-        "client = lb.Client(api_key=API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########### Radio Classification ###########\n",
-        "\n",
-        "# Python annotation\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"second_radio_answer\", confidence=0.5))\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_prediction_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"second_radio_answer\", \"confidence\": 0.5}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "# message based classifications\n",
-        "ner_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"ner\",\n",
-        "    confidence=0.5,\n",
-        "    value=lb_types.ConversationEntity(\n",
-        "        start=0,\n",
-        "        end=8,\n",
-        "        message_id=\"4\"\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "ner_prediction_ndjson = {\n",
-        "        \"name\": \"ner\",\n",
-        "        \"confidence\":0.5,\n",
-        "        \"location\": {\n",
-        "            \"start\": 0,\n",
-        "            \"end\": 8\n",
-        "        },\n",
-        "        \"messageId\": \"4\"\n",
-        "    }"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### Classification free text #####\n",
-        "# Confidence scores are not supported for text predictions\n",
-        "\n",
-        "text_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"text_convo\",\n",
-        "    value=lb_types.Text(answer=\"the answer to the text questions are right here\"),\n",
-        "    message_id=\"0\"\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_prediction_ndjson = {\n",
-        "    \"name\": \"text_convo\",\n",
-        "    \"answer\": \"the answer to the text questions are right here\",\n",
-        "    \"messageId\": \"0\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "##### Checklist Classification #######\n",
-        "\n",
-        "checklist_prediction= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_convo\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\", confidence=0.5\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\", confidence=0.5\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  message_id=\"2\"\n",
-        " )\n",
-        "\n",
-        "\n",
-        "checklist_prediction_ndjson = {\n",
-        "    \"name\": \"checklist_convo\",\n",
-        "    \"answers\": [\n",
-        "        {\"name\": \"first_checklist_answer\", \"confidence\":0.5},\n",
-        "        {\"name\": \"second_checklist_answer\", \"confidence\":0.5}\n",
-        "    ],\n",
-        "    \"messageId\": \"2\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Radio Classification ######\n",
-        "\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_convo\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\", confidence=0.5)),\n",
-        "    message_id=\"0\"\n",
-        ")\n",
-        "\n",
-        "radio_prediction_ndjson = {\n",
-        "    \"name\": \"radio_convo\",\n",
-        "    \"answer\": {\n",
-        "        \"name\": \"first_radio_answer\", \"confidence\":0.5\n",
-        "    },\n",
-        "    \"messageId\": \"0\",\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# ############ global nested classifications ###########\n",
-        "\n",
-        "# Message based\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  message_id=\"10\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5 # Confidence scores should be added to the answer\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "# Message based\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"messageId\": \"10\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\n",
-        "            \"name\": \"first_sub_checklist_answer\",\n",
-        "            \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "          }\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}\n",
-        "# Global\n",
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5 # Confidence scores should be added to the answer\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "#Global\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"confidence\": 0.5,\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\",\n",
-        "                     \"confidence\": 0.5}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create one Labelbox dataset\n",
-        "\n",
-        "global_key = \"conversation-1.json\"\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"conversational_annotation_import_demo_dataset\")\n",
-        "task = dataset.create_data_rows([asset])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\", task.errors)\n",
-        "print(\"Failed data rows: \", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  tools=[\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER,name=\"ner\")\n",
-        "    ],\n",
-        "  classifications=[\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      name=\"text_convo\"),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      name=\"checklist_convo\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_convo\",\n",
-        "      scope=lb.Classification.Scope.INDEX,\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      scope = lb.Classification.Scope.INDEX,\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ])\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        scope = lb.Classification.Scope.GLOBAL,\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ])\n",
-        "          ]\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Conversation Annotations\", ontology_builder.asdict())"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Mode and Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"Conversational_model_run_\"+ str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the predictions payload\n",
-        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-        "\n",
-        "The resulting payload should have exactly the same content for annotations that are supported by both"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Python annotations"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_prediction = []\n",
-        "label_prediction.append(\n",
-        "  lb_types.Label(\n",
-        "    data= {\"global_key\": global_key},\n",
-        "    annotations=[\n",
-        "      ner_prediction,\n",
-        "      checklist_prediction,\n",
-        "      text_prediction,\n",
-        "      radio_prediction,\n",
-        "      nested_checklist_prediction,\n",
-        "      nested_radio_prediction\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "if using NDJSON : "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_prediction_ndjson = []\n",
-        "for annotations in [\n",
-        "    ner_prediction_ndjson,\n",
-        "    text_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    radio_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson\n",
-        "    ]:\n",
-        "  annotations.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_prediction_ndjson.append(annotations)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6: Upload the predictions payload to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_prediction)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7 : Send annotations to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.1 Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(name=\"Conversational Text Prediction Import Demo\",\n",
-        "                                    media_type=lb.MediaType.Conversational)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.2 Create a batch to send to the project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_convo_prediction_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ner_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"ner\",\n",
-        "    value=lb_types.ConversationEntity(\n",
-        "        start=0,\n",
-        "        end=8,\n",
-        "        message_id=\"4\"\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"text_convo\",\n",
-        "    value=lb_types.Text(answer=\"the answer to the text questions are right here\"),\n",
-        "    message_id=\"0\"\n",
-        ")\n",
-        "\n",
-        "checklist_annotation= lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_convo\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  message_id=\"2\"\n",
-        " )\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_convo\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\")),\n",
-        "    message_id=\"0\"\n",
-        ")\n",
-        "\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  message_id=\"10\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.4 Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "    data=lb_types.ConversationData(\n",
-        "      global_key=global_key\n",
-        "    ),\n",
-        "    annotations=[\n",
-        "      ner_annotation,\n",
-        "      text_annotation,\n",
-        "      checklist_annotation,\n",
-        "      radio_annotation,\n",
-        "      nested_radio_annotation,\n",
-        "      nested_checklist_annotation\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.5 Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"text_label_import_job\"+ str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.6 Send the annotations to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Option deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Conversational Text Prediction Import\n",
+                "* This notebook will provide examples of each supported prediction type for conversational text assets, and also cover MAL and Label Import methods:\n",
+                "\n",
+                "Suported annotations that can be uploaded through the SDK\n",
+                "\n",
+                "* Classification Radio \n",
+                "* Classification Checklist \n",
+                "* Classification Free Text \n",
+                "* NER\n",
+                "\n",
+                "**Not** supported annotations\n",
+                "\n",
+                "* Bouding box \n",
+                "* Polygon \n",
+                "* Point\n",
+                "* Polyline \n",
+                "* Segmentation Mask \n",
+                "* Relationships\n",
+                "\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########### Radio Classification ###########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# message based classifications\nner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "##### Classification free text #####\n# Confidence scores are not supported for text predictions\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions are right here\",\n    \"messageId\": \"0\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "##### Checklist Classification #######\n\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"2\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Radio Classification ######\n\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n    message_id=\"0\",\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n    \"messageId\": \"0\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# ############ global nested classifications ###########\n\n# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\")],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Mode and Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(\n    name=\"Conversational_model_run_\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the predictions payload\n",
+                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+                "\n",
+                "The resulting payload should have exactly the same content for annotations that are supported by both"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Python annotations"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "if using NDJSON : "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_prediction_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotations)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6: Upload the predictions payload to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7 : Send annotations to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.1 Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(\n    name=\"Conversational Text Prediction Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.2 Create a batch to send to the project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.4 Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.5 Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.6 Send the annotations to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Option deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/geospatial_predictions.ipynb b/examples/prediction_upload/geospatial_predictions.ipynb
index 636a7a43b..43d83f4c9 100644
--- a/examples/prediction_upload/geospatial_predictions.ipynb
+++ b/examples/prediction_upload/geospatial_predictions.ipynb
@@ -1,1316 +1,381 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Geospatial Prediction Import \n",
-        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for tiled imagery assets.\n",
-        "\n",
-        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-        "\n",
-        "**Supported annotations that can be uploaded through the SDK**\n",
-        "- Bounding box\n",
-        "- Point\n",
-        "- Polygons \n",
-        "- Polyline\n",
-        "- Free form text classifications\n",
-        "- Classification - radio\n",
-        "- Classification - checklist\n",
-        "\n",
-        "**NOT** supported:\n",
-        "- Segmentation masks\n",
-        "\n",
-        "\n",
-        "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q 'labelbox[data]'"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import os\n",
-        "\n",
-        "import uuid\n",
-        "import numpy as np\n",
-        "from PIL import Image\n",
-        "import cv2\n",
-        "\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API Key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions\n",
-        "- Each cell shows the python annotation and the NDJson annotation for each annotation type."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "####### Point #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "point_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"point_geo\",\n",
-        "  confidence=0.4,\n",
-        "  value = lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "point_prediction_ndjson = {\n",
-        "    \"name\": \"point_geo\",\n",
-        "    \"confidence\": 0.4,\n",
-        "    \"point\": {\n",
-        "         \"x\": -99.20647859573366,\n",
-        "         \"y\": 19.40018029091072\n",
-        "     }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "####### Polyline #######\n",
-        "# Coordinates\n",
-        "coords = [\n",
-        "            [\n",
-        "                -99.20842051506044,\n",
-        "                19.40032196622975\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20809864997865,\n",
-        "                19.39758963475322\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20758366584778,\n",
-        "                19.39776167179227\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20728325843811,\n",
-        "                19.3973265189299\n",
-        "            ]\n",
-        "        ]\n",
-        "\n",
-        "line_points = []\n",
-        "line_points_ndjson = []\n",
-        "\n",
-        "for sub in coords:\n",
-        "  line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n",
-        "  line_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
-        "\n",
-        "# Python Annotation\n",
-        "polyline_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline_geo\",\n",
-        "  confidence=0.5,\n",
-        "  value = lb_types.Line(points=line_points),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "polyline_prediction_ndjson = {\n",
-        "    \"name\": \"polyline_geo\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"line\": line_points_ndjson\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "####### Polygon #######\n",
-        "# Coordinates in the desired EPSG coordinate system\n",
-        "coords_polygon = [\n",
-        "    [\n",
-        "        -99.21042680740356,\n",
-        "        19.40036244486966\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2104160785675,\n",
-        "        19.40017017124035\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2103409767151,\n",
-        "        19.400008256428897\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21014785766603,\n",
-        "        19.400008256428897\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21019077301027,\n",
-        "        19.39983622176518\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21022295951845,\n",
-        "        19.399674306621385\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21029806137086,\n",
-        "        19.39951239131646\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2102873325348,\n",
-        "        19.399340356128437\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21025514602663,\n",
-        "        19.399117722085677\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21024441719057,\n",
-        "        19.39892544698541\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2102336883545,\n",
-        "        19.39874329141769\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21021223068239,\n",
-        "        19.398561135646027\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21018004417421,\n",
-        "        19.398399219233365\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21011567115785,\n",
-        "        19.39822718286836\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20992255210878,\n",
-        "        19.398136104719125\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20974016189577,\n",
-        "        19.398085505725305\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20957922935487,\n",
-        "        19.398004547302467\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20939683914186,\n",
-        "        19.39792358883935\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20918226242067,\n",
-        "        19.39786286996558\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20899987220764,\n",
-        "        19.397822390703805\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20891404151918,\n",
-        "        19.397994427496787\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.398176583902874\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.398368859888045\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.398540896103246\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.39872305189756\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.39890520748796\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.39907724313608\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20889258384706,\n",
-        "        19.399259398329956\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.399431433603585\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.39961358840092\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20890331268312,\n",
-        "        19.399785623300048\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20897841453552,\n",
-        "        19.399937418648214\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20919299125673,\n",
-        "        19.399937418648214\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.2093861103058,\n",
-        "        19.39991717927664\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20956850051881,\n",
-        "        19.39996777770086\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20961141586305,\n",
-        "        19.40013981222548\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20963287353517,\n",
-        "        19.40032196622975\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20978307724,\n",
-        "        19.4004130431554\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.20996546745302,\n",
-        "        19.40039280384301\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21019077301027,\n",
-        "        19.400372564528084\n",
-        "    ],\n",
-        "    [\n",
-        "        -99.21042680740356,\n",
-        "        19.40036244486966\n",
-        "    ]\n",
-        "\n",
-        "]\n",
-        "\n",
-        "polygon_points = []\n",
-        "polygon_points_ndjson = []\n",
-        "\n",
-        "for sub in coords_polygon:\n",
-        "  polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n",
-        "  polygon_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
-        "\n",
-        "# Python Annotation\n",
-        "polygon_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon_geo\",\n",
-        "  confidence=0.5,\n",
-        "  value = lb_types.Polygon(points=polygon_points),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "polygon_prediction_ndjson = {\n",
-        "    \"name\": \"polygon_geo\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"polygon\": polygon_points_ndjson\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "####### Bounding Box #######\n",
-        "coord_object =  {\n",
-        "    \"coordinates\" : [[\n",
-        "            [\n",
-        "                -99.20746564865112,\n",
-        "                19.39799442829336\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20746564865112,\n",
-        "                19.39925939999194\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20568466186523,\n",
-        "                19.39925939999194\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20568466186523,\n",
-        "                19.39799442829336\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20746564865112,\n",
-        "                19.39799442829336\n",
-        "            ]\n",
-        "        ]]\n",
-        "}\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "bbox_top_left = lb_types.Point(x= -99.20746564865112, y=19.39799442829336)\n",
-        "bbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"bbox_geo\",\n",
-        "  confidence=0.5,\n",
-        "  value = lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right)\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_prediction_ndjson = {\n",
-        "    \"name\" : \"bbox_geo\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"bbox\" : {\n",
-        "        'top': coord_object[\"coordinates\"][0][1][1],\n",
-        "        'left': coord_object[\"coordinates\"][0][1][0],\n",
-        "        'height': coord_object[\"coordinates\"][0][3][1] - coord_object[\"coordinates\"][0][1][1],\n",
-        "        'width': coord_object[\"coordinates\"][0][3][0] - coord_object[\"coordinates\"][0][1][0]\n",
-        "    }\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "####### Classification - radio (single choice) #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question_geo\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_radio_answer\", confidence=0.5))\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_prediction_ndjson = {\n",
-        "    \"name\": \"radio_question_geo\",\n",
-        "    \"answer\": { \"name\": \"first_radio_answer\", \"confidence\": 0.5}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification - Checklist (multi-choice) #######\n",
-        "\n",
-        "coord_object_checklist = {\n",
-        "    \"coordinates\": [\n",
-        "       [\n",
-        "            [\n",
-        "                -99.210266,\n",
-        "                19.39540372195134\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.210266,\n",
-        "                19.396901\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20621067903966,\n",
-        "                19.396901\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.20621067903966,\n",
-        "                19.39540372195134\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.210266,\n",
-        "                19.39540372195134\n",
-        "            ]\n",
-        "      ]\n",
-        "    ]\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_checklist_geo\",\n",
-        "    confidence=0.5,\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=-99.210266, y=19.39540372195134), # Top left\n",
-        "        end=lb_types.Point(x=-99.20621067903966, y=19.396901), # Bottom right\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"checklist_class_name\",\n",
-        "            value=lb_types.Checklist(\n",
-        "                answer=[lb_types.ClassificationAnswer(name=\"first_checklist_answer\", confidence=0.5)]\n",
-        "            )\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_with_checklist_subclass_ndjson = {\n",
-        "    \"name\": \"bbox_checklist_geo\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"checklist_class_name\",\n",
-        "        \"answer\": [\n",
-        "            { \"name\":\"first_checklist_answer\", \"confidence\": 0.5}\n",
-        "        ]\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "        'top': coord_object_checklist[\"coordinates\"][0][1][1],\n",
-        "        'left': coord_object_checklist[\"coordinates\"][0][1][0],\n",
-        "        'height': coord_object_checklist[\"coordinates\"][0][3][1] - coord_object_checklist[\"coordinates\"][0][1][1],\n",
-        "        'width': coord_object_checklist[\"coordinates\"][0][3][0] - coord_object_checklist[\"coordinates\"][0][1][0]\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification free form text with bbox #######\n",
-        "\n",
-        "coord_object_text ={\n",
-        "    \"coordinates\": [\n",
-        "      [\n",
-        "        [\n",
-        "            -99.21019613742828,\n",
-        "            19.397447957052933\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.21019613742828,\n",
-        "            19.39772119262215\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.20986354351044,\n",
-        "            19.39772119262215\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.20986354351044,\n",
-        "            19.397447957052933\n",
-        "        ],\n",
-        "        [\n",
-        "            -99.21019613742828,\n",
-        "            19.397447957052933\n",
-        "        ]\n",
-        "      ]\n",
-        "    ]\n",
-        "}\n",
-        "# Python Annotation\n",
-        "bbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_text_geo\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=-99.21019613742828, y=19.397447957052933), # Top left\n",
-        "        end=lb_types.Point(x=-99.20986354351044, y=19.39772119262215), # Bottom right\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"free_text_geo\",\n",
-        "            value=lb_types.Text(answer=\"sample text\")\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_with_free_text_subclass_ndjson = {\n",
-        "    \"name\":\"bbox_text_geo\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"free_text_geo\",\n",
-        "        \"confidence\": 0.5,\n",
-        "        \"answer\": \"sample text\"\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "        'top': coord_object_text[\"coordinates\"][0][1][1],\n",
-        "        'left': coord_object_text[\"coordinates\"][0][1][0],\n",
-        "        'height': coord_object_text[\"coordinates\"][0][3][1] - coord_object_text[\"coordinates\"][0][1][1],\n",
-        "        'width': coord_object_text[\"coordinates\"][0][3][0] - coord_object_text[\"coordinates\"][0][1][0]\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Classification - Checklist (multi-choice) #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question_geo\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\", confidence = 0.5),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\", confidence = 0.5),\n",
-        "        lb_types.ClassificationAnswer(name = \"third_checklist_answer\", confidence = 0.5)\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_prediction_ndjson = {\n",
-        "  'name': 'checklist_question_geo',\n",
-        "  'answer': [\n",
-        "    {'name': 'first_checklist_answer', \"confidence\": 0.5},\n",
-        "    {'name': 'second_checklist_answer', \"confidence\": 0.5},\n",
-        "    {'name': 'third_checklist_answer', \"confidence\": 0.5},\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "########## Classification - Radio and Checklist (with subclassifications)  ##########\n",
-        "\n",
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5,\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.2\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "# NDJSON\n",
-        "nested_radio_prediction_ndjson= {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"confidence\": 0.2,\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\", \"confidence\": 0.3}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5,\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\":0.5,\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\", \"confidence\": 0.5}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\n",
-        "bottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n",
-        "\n",
-        "epsg = lb_types.EPSG.EPSG4326\n",
-        "bounds = lb_types.TiledBounds(epsg=epsg, bounds=[top_left_bound, bottom_right_bound])\n",
-        "global_key = \"mexico_city\"\n",
-        "\n",
-        "tile_layer = lb_types.TileLayer(\n",
-        "    url=\"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n",
-        ")\n",
-        "\n",
-        "tiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n",
-        "                                  tile_bounds=bounds,\n",
-        "                                  zoom_levels=[17, 23])\n",
-        "\n",
-        "asset = {\n",
-        "    \"row_data\": tiled_image_data.asdict(),\n",
-        "    \"global_key\": global_key,\n",
-        "    \"media_type\": \"TMS_GEO\"\n",
-        "}\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"geo_demo_dataset\")\n",
-        "task= dataset.create_data_rows([asset])\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n",
-        "        lb.Tool(\n",
-        "          tool=lb.Tool.Type.BBOX,\n",
-        "          name=\"bbox_checklist_geo\",\n",
-        "          classifications=[\n",
-        "                lb.Classification(\n",
-        "                    class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                    name=\"checklist_class_name\",\n",
-        "                    options=[\n",
-        "                      lb.Option(value=\"first_checklist_answer\")\n",
-        "                    ]\n",
-        "                ),\n",
-        "            ]\n",
-        "          ),\n",
-        "        lb.Tool(\n",
-        "          tool=lb.Tool.Type.BBOX,\n",
-        "          name=\"bbox_text_geo\",\n",
-        "          classifications=[\n",
-        "                lb.Classification(\n",
-        "                    class_type=lb.Classification.Type.TEXT,\n",
-        "                    name=\"free_text_geo\"\n",
-        "                ),\n",
-        "            ]\n",
-        "          )\n",
-        "      ],\n",
-        "      classifications = [\n",
-        "          lb.Classification(\n",
-        "              class_type=lb.Classification.Type.CHECKLIST,\n",
-        "              name=\"checklist_question_geo\",\n",
-        "              options=[\n",
-        "                  lb.Option(value=\"first_checklist_answer\"),\n",
-        "                  lb.Option(value=\"second_checklist_answer\"),\n",
-        "                  lb.Option(value=\"third_checklist_answer\")\n",
-        "              ]\n",
-        "          ),\n",
-        "          lb.Classification(\n",
-        "              class_type=lb.Classification.Type.RADIO,\n",
-        "              name=\"radio_question_geo\",\n",
-        "              options=[\n",
-        "                  lb.Option(value=\"first_radio_answer\")\n",
-        "              ]\n",
-        "          ),\n",
-        "\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.RADIO,\n",
-        "          name=\"nested_radio_question\",\n",
-        "          options=[\n",
-        "            lb.Option(value=\"first_radio_answer\",\n",
-        "              options=[\n",
-        "                  lb.Classification(\n",
-        "                    class_type=lb.Classification.Type.RADIO,\n",
-        "                    name=\"sub_radio_question\",\n",
-        "                    options=[\n",
-        "                      lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                    ]\n",
-        "                ),\n",
-        "              ]\n",
-        "            ),\n",
-        "          ],\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.CHECKLIST,\n",
-        "          name=\"nested_checklist_question\",\n",
-        "          options=[\n",
-        "              lb.Option(\"first_checklist_answer\",\n",
-        "                options=[\n",
-        "                  lb.Classification(\n",
-        "                      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                      name=\"sub_checklist_question\",\n",
-        "                      options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "                  )\n",
-        "              ]\n",
-        "            )\n",
-        "          ]\n",
-        "      )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Geospatial Annotations\", ontology_builder.asdict(), media_type=lb.MediaType.Geospatial_Tile)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"geospatial_model_run_\" + str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5. Create the predictions payload\n",
-        "\n",
-        "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
-        "\n",
-        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        " ## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n",
-        "\n",
-        "hsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\n",
-        "mask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\n",
-        "kernel = np.ones((15, 20), np.uint8)\n",
-        "mask = cv2.erode(mask, kernel)\n",
-        "mask = cv2.dilate(mask, kernel)\n",
-        "mask_annotation = lb_types.MaskData.from_2D_arr(mask)\n",
-        "mask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\n",
-        "h, w, _ = tiled_image_data.value.shape\n",
-        "pixel_bounds = lb_types.TiledBounds(epsg=lb_types.EPSG.SIMPLEPIXEL,\n",
-        "                          bounds=[lb_types.Point(x=0, y=0),\n",
-        "                                  lb_types.Point(x=w, y=h)])\n",
-        "transformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n",
-        "    src_epsg=pixel_bounds.epsg,\n",
-        "    pixel_bounds=pixel_bounds,\n",
-        "    geo_bounds=tiled_image_data.tile_bounds,\n",
-        "    zoom=23)\n",
-        "pixel_polygons = mask_data.shapely.simplify(3)\n",
-        "list_of_polygons = [transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms]\n",
-        "polygon_prediction_two = lb_types.ObjectAnnotation(value=list_of_polygons[0], name=\"polygon_geo_2\", confidence=0.5)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels =[]\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "         data={\n",
-        "            \"global_key\": global_key,\n",
-        "            \"tile_layer\": tile_layer,\n",
-        "            \"tile_bounds\":bounds,\n",
-        "            \"zoom_levels\": [12, 20]\n",
-        "        },\n",
-        "        annotations = [\n",
-        "            point_prediction,\n",
-        "            polyline_prediction,\n",
-        "            polygon_prediction,\n",
-        "            bbox_prediction,\n",
-        "            radio_prediction,\n",
-        "            bbox_with_checklist_subclass,\n",
-        "            bbox_with_free_text_subclass,\n",
-        "            checklist_prediction,\n",
-        "            polygon_prediction_two,\n",
-        "            nested_checklist_prediction,\n",
-        "            nested_radio_prediction\n",
-        "        ]\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# If using NDJSON"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson = []\n",
-        "for prediction in [\n",
-        "    radio_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    bbox_with_free_text_subclass_ndjson,\n",
-        "    bbox_with_checklist_subclass_ndjson,\n",
-        "    bbox_prediction_ndjson,\n",
-        "    point_prediction_ndjson,\n",
-        "    polyline_prediction_ndjson,\n",
-        "    polygon_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson\n",
-        "]:\n",
-        "  prediction.update({\n",
-        "      'dataRow': {'globalKey': global_key},\n",
-        "  })\n",
-        "  label_ndjson.append(prediction)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6. Upload the predictions payload to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=labels)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to the Model Run \n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.1. Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"geospatial_prediction_demo\",\n",
-        "                                    media_type=lb.MediaType.Geospatial_Tile)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.2. Create a batch to send to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_geospatial_prediction_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # A list of data rows or data row ids\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Point #######\n",
-        "\n",
-        "# Python Annotation\n",
-        "point_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"point_geo\",\n",
-        "  value = lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n",
-        ")\n",
-        "\n",
-        "####### Polyline #######\n",
-        "line_points = []\n",
-        "line_points_ndjson = []\n",
-        "\n",
-        "for sub in coords:\n",
-        "  line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n",
-        "  line_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
-        "\n",
-        "# Python Annotation\n",
-        "polyline_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline_geo\",\n",
-        "  value = lb_types.Line(points=line_points),\n",
-        ")\n",
-        "\n",
-        "\n",
-        "\n",
-        "polygon_points = []\n",
-        "polygon_points_ndjson = []\n",
-        "\n",
-        "for sub in coords_polygon:\n",
-        "  polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n",
-        "  polygon_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
-        "\n",
-        "# Python Annotation\n",
-        "polygon_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon_geo\",\n",
-        "  value = lb_types.Polygon(points=polygon_points),\n",
-        ")\n",
-        "\n",
-        "\n",
-        "\n",
-        "bbox_top_left = lb_types.Point(x= -99.20746564865112, y=19.39799442829336)\n",
-        "bbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"bbox_geo\",\n",
-        "  value = lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right)\n",
-        ")\n",
-        "\n",
-        "# Python Annotation\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question_geo\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_radio_answer\"))\n",
-        ")\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_checklist_geo\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=-99.210266, y=19.39540372195134), # Top left\n",
-        "        end=lb_types.Point(x=-99.20621067903966, y=19.396901), # Bottom right\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"checklist_class_name\",\n",
-        "            value=lb_types.Checklist(\n",
-        "                answer=[lb_types.ClassificationAnswer(name=\"first_checklist_answer\")]\n",
-        "            )\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "bbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_text_geo\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=-99.21019613742828, y=19.397447957052933), # Top left\n",
-        "        end=lb_types.Point(x=-99.20986354351044, y=19.39772119262215), # Bottom right\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "            name=\"free_text_geo\",\n",
-        "            value=lb_types.Text(answer=\"sample text\")\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question_geo\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"third_checklist_answer\")\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.4. Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels =[]\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "        data=lb_types.TiledImageData(\n",
-        "            global_key=global_key,\n",
-        "            tile_layer=tile_layer,\n",
-        "            tile_bounds=bounds,\n",
-        "            zoom_levels=[12, 20]\n",
-        "        ),\n",
-        "        annotations = [\n",
-        "            point_annotation,\n",
-        "            polyline_annotation,\n",
-        "            polygon_annotation,\n",
-        "            bbox_annotation,\n",
-        "            radio_annotation,\n",
-        "            bbox_with_checklist_subclass,\n",
-        "            bbox_with_free_text_subclass,\n",
-        "            checklist_annotation,\n",
-        "            nested_checklist_annotation,\n",
-        "            nested_radio_annotation\n",
-        "        ]\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.5. Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"geospatial_annotations_import_\" + str(uuid.uuid4()),\n",
-        "    labels=labels)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.6. Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Optional deletions for cleanup \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#upload_job\n",
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Geospatial Prediction Import \n",
+                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for tiled imagery assets.\n",
+                "\n",
+                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+                "\n",
+                "**Supported annotations that can be uploaded through the SDK**\n",
+                "- Bounding box\n",
+                "- Point\n",
+                "- Polygons \n",
+                "- Polyline\n",
+                "- Free form text classifications\n",
+                "- Classification - radio\n",
+                "- Classification - checklist\n",
+                "\n",
+                "**NOT** supported:\n",
+                "- Segmentation masks\n",
+                "\n",
+                "\n",
+                "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q 'labelbox[data]'",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import os\n\nimport uuid\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API Key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions\n",
+                "- Each cell shows the python annotation and the NDJson annotation for each annotation type."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "####### Point #######\n\n# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    confidence=0.4,\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point_geo\",\n    \"confidence\": 0.4,\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    confidence=0.5,\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"confidence\": 0.5,\n    \"line\": line_points_ndjson,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_prediction_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"confidence\": 0.5,\n    \"polygon\": polygon_points_ndjson,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"confidence\": 0.5,\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"third_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.2)),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.2,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.3\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(name=\"geospatial_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5. Create the predictions payload\n",
+                "\n",
+                "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
+                "\n",
+                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=23,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_prediction_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\",\n                                                   confidence=0.5)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            bbox_prediction,\n            radio_prediction,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_prediction,\n            polygon_prediction_two,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# If using NDJSON"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson = []\nfor prediction in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        polygon_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    prediction.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(prediction)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6. Upload the predictions payload to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(name=\"prediction_upload_job\" +\n                                                  str(uuid.uuid4()),\n                                                  predictions=labels)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to the Model Run \n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.1. Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"geospatial_prediction_demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.2. Create a batch to send to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_geospatial_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key],  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n####### Polyline #######\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.4. Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data=lb_types.TiledImageData(\n            global_key=global_key,\n            tile_layer=tile_layer,\n            tile_bounds=bounds,\n            zoom_levels=[12, 20],\n        ),\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.5. Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"geospatial_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.6. Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Optional deletions for cleanup \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# upload_job\n# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/html_predictions.ipynb b/examples/prediction_upload/html_predictions.ipynb
index 7148cf06f..f5b4bb94f 100644
--- a/examples/prediction_upload/html_predictions.ipynb
+++ b/examples/prediction_upload/html_predictions.ipynb
@@ -1,698 +1,339 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# HTML Prediction Import\n",
-        "\n",
-        "This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for HTML assets.\n",
-        "\n",
-        "**Supported predictions**\n",
-        "- Radio Classification \n",
-        "- Checklist Classification\n",
-        "- free-text Classification\n",
-        "\n",
-        "**Not supported:**\n",
-        "- Bounding Box\n",
-        "- Polygon\n",
-        "- Point\n",
-        "- Polyline\n",
-        "- Masks\n",
-        "- NER\n",
-        "\n",
-        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid\n",
-        "import numpy as np"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API Key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########### Radio Classification ###########\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"radio_question\", # Should match the name in the ontology\n",
-        "  value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\", confidence=0.5))\n",
-        ")\n",
-        "\n",
-        "radio_prediction_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\"}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Nested Classifications ######\n",
-        "\n",
-        "# Python annotation\n",
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5,# Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\", confidence=0.5)\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\", \"confidence\": 0.5 }\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\", \"confidence\": 0.5 }\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Checklist ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(\n",
-        "        answer = [\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"first_checklist_answer\",\n",
-        "                confidence=0.5\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"second_checklist_answer\",\n",
-        "                confidence=0.5\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"third_checklist_answer\",\n",
-        "                confidence=0.5\n",
-        "            )\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_prediction_ndjson = {\n",
-        "  \"name\": \"checklist_question\",\n",
-        "  \"answer\": [\n",
-        "    {\"name\": \"first_checklist_answer\", \"confidence\": 0.5}\n",
-        "  ]\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification Free-Form text  ##########\n",
-        "## Text classifications do not support confidence values\n",
-        "# Python annotation\n",
-        "text_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name = \"free_text\",\n",
-        "    value = lb_types.Text(answer=\"sample text\", confidence=0.5)\n",
-        ")\n",
-        "\n",
-        "#  NDJSON\n",
-        "text_prediction_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "  \"confidence\": 0.5\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as batch to the project\n",
-        "global_key =\"sample_html_2.html\"\n",
-        "\n",
-        "test_img_url = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "dataset = client.create_dataset(\n",
-        "    name=\"html prediction demo dataset\",\n",
-        "    iam_integration=None # Removing this argument will default to the organziation's default iam integration\n",
-        ")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names should match the name field in your annotations to ensure the correct feature schemas are matched.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup the ontology and link the tools created above.\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",  # name matching the tool used in the annotation\n",
-        "      options=[lb.Option(value=\"first_radio_answer\")]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"nested_radio_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\",\n",
-        "          options=[\n",
-        "              lb.Classification(\n",
-        "                class_type=lb.Classification.Type.RADIO,\n",
-        "                name=\"sub_radio_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                ]\n",
-        "            ),\n",
-        "          ]\n",
-        "        )\n",
-        "      ],\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\"),\n",
-        "        lb.Option(value=\"third_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "     lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\"\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology HTML Predictions\", ontology_builder.asdict(), media_type=lb.MediaType.Html)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"HTML_model_run_\" + str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5. Create the predictions payload\n",
-        "\n",
-        "Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-        "\n",
-        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label for predictions\n",
-        "label_prediction = []\n",
-        "label_prediction.append(\n",
-        "  lb_types.Label(\n",
-        "    data=lb_types.HTMLData(global_key=global_key),\n",
-        "    annotations = [\n",
-        "      radio_prediction,\n",
-        "      checklist_prediction,\n",
-        "      text_prediction,\n",
-        "      nested_checklist_prediction,\n",
-        "      nested_radio_prediction\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "If using NDJSON: "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "label_prediction_ndjson = []\n",
-        "for annot in [\n",
-        "    radio_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    text_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson\n",
-        "]:\n",
-        "  annot.update({\n",
-        "      \"dataRow\": {\"globalKey\": global_key},\n",
-        "  })\n",
-        "  label_prediction_ndjson.append(annot)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6. Upload the predictions payload to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_prediction)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to the Model Run \n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.1. Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"HTML prediction import demo\",\n",
-        "                                    media_type=lb.MediaType.Html)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.2. Create a batch to send to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_prediction_html\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######  Annotations ######\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"radio_question\",\n",
-        "  value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\")\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(\n",
-        "        answer = [\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"first_checklist_answer\",\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"second_checklist_answer\",\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"third_checklist_answer\",\n",
-        "            )\n",
-        "    ])\n",
-        ")\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name = \"free_text\",\n",
-        "    value = lb_types.Text(answer=\"sample text\")\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.4. Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "    data={\"global_key\" : global_key },\n",
-        "    annotations=[\n",
-        "      text_annotation,\n",
-        "      checklist_annotation,\n",
-        "      radio_annotation,\n",
-        "      nested_checklist_annotation,\n",
-        "      nested_radio_annotation\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.5. Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"html_annotation_import\" + str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.6 Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Optional deletions for cleanup \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# HTML Prediction Import\n",
+                "\n",
+                "This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for HTML assets.\n",
+                "\n",
+                "**Supported predictions**\n",
+                "- Radio Classification \n",
+                "- Checklist Classification\n",
+                "- free-text Classification\n",
+                "\n",
+                "**Not supported:**\n",
+                "- Bounding Box\n",
+                "- Polygon\n",
+                "- Point\n",
+                "- Polyline\n",
+                "- Masks\n",
+                "- NER\n",
+                "\n",
+                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport numpy as np",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API Key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########### Radio Classification ###########\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",  # Should match the name in the ontology\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "#### Nested Classifications ######\n\n# Python annotation\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification Free-Form text  ##########\n## Text classifications do not support confidence values\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as batch to the project\nglobal_key = \"sample_html_2.html\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"html prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names should match the name field in your annotations to ensure the correct feature schemas are matched.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\n            \"radio_question\",  # name matching the tool used in the annotation\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ])\n\nontology = client.create_ontology(\n    \"Ontology HTML Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(name=\"HTML_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5. Create the predictions payload\n",
+                "\n",
+                "Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+                "\n",
+                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.HTMLData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            checklist_prediction,\n            text_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "If using NDJSON: "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_prediction_ndjson = []\nfor annot in [\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_prediction_ndjson.append(annot)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6. Upload the predictions payload to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to the Model Run \n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.1. Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"HTML prediction import demo\",\n                                media_type=lb.MediaType.Html)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.2. Create a batch to send to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_prediction_html\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "######  Annotations ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.4. Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.5. Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"html_annotation_import\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.6 Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Optional deletions for cleanup \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/image_predictions.ipynb b/examples/prediction_upload/image_predictions.ipynb
index f48ad559e..b0ce9a97f 100644
--- a/examples/prediction_upload/image_predictions.ipynb
+++ b/examples/prediction_upload/image_predictions.ipynb
@@ -1,1150 +1,473 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Image Prediction Import\n",
-        "\n",
-        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for image assets. \n",
-        "\n",
-        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-        "\n",
-        "**Supported annotations that can be uploaded through the SDK**\n",
-        "\n",
-        "- Bounding box \n",
-        "- Polygon\n",
-        "- Point\n",
-        "- Polyline \n",
-        "- Raster Segmentation\n",
-        "- Classification free-text\n",
-        "- Classification - radio\n",
-        "- Classification - checklist\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "* Notes:\n",
-        "    * If you are importing more than 1,000 mask predictions at a time, consider submitting separate jobs, as they can take longer than other prediction types to import.\n",
-        "    * After the execution of this notebook a complete Model Run with predictions will be created in your organization. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import uuid\n",
-        "import requests\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API Key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY = \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Radio (single-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"second_radio_answer\", confidence=0.5))\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_prediction_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"second_radio_answer\", \"confidence\": 0.5}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Nested radio and checklist"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5,\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "    \"name\": \"nested_radio_question\",\n",
-        "    \"answer\": {\n",
-        "        \"name\": \"first_radio_answer\",\n",
-        "        \"confidence\": 0.5,\n",
-        "        \"classifications\": [{\n",
-        "            \"name\": \"sub_radio_question\",\n",
-        "            \"answer\": {\n",
-        "                \"name\": \"first_sub_radio_answer\",\n",
-        "                \"confidence\": 0.5\n",
-        "            }\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5,\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "    \"name\": \"nested_checklist_question\",\n",
-        "    \"answer\": [{\n",
-        "        \"name\": \"first_checklist_answer\",\n",
-        "        \"confidence\": 0.5,\n",
-        "        \"classifications\": [{\n",
-        "            \"name\": \"sub_checklist_question\",\n",
-        "            \"answer\": {\n",
-        "                \"name\": \"first_sub_checklist_answer\",\n",
-        "                \"confidence\": 0.5\n",
-        "            }\n",
-        "        }]\n",
-        "    }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Checklist (multi-choice)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotations\n",
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        " )\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_prediction_ndjson = {\n",
-        "  \"name\": \"checklist_question\",\n",
-        "  \"answer\": [\n",
-        "    {\"name\": \"first_checklist_answer\" , \"confidence\": 0.5},\n",
-        "    {\"name\": \"second_checklist_answer\", \"confidence\": 0.5}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding Box"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "bbox_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"bounding_box\",\n",
-        "  confidence=0.5,\n",
-        "  value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=1915, y=1307),  # x= left + width , y = top + height\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "#NDJSON\n",
-        "bbox_prediction_ndjson = {\n",
-        "  \"name\": \"bounding_box\",\n",
-        "  \"confidence\": 0.5,\n",
-        "  \"bbox\": {\n",
-        "          \"top\": 977,\n",
-        "          \"left\": 1690,\n",
-        "          \"height\": 330,\n",
-        "          \"width\": 225\n",
-        "      }\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Bounding box with nested classification "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Bounding box with nested classification #######\n",
-        "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    confidence=0.5,\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_sub_radio_answer\", confidence=0.5))\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "\n",
-        "## NDJSON\n",
-        "bbox_with_radio_subclass_prediction_ndjson = {\n",
-        "    \"name\": \"bbox_with_radio_subclass\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"classifications\": [{\n",
-        "        \"name\": \"sub_radio_question\",\n",
-        "        \"answer\":\n",
-        "            { \"name\":\"first_sub_radio_answer\", \"confidence\": 0.5}\n",
-        "\n",
-        "    }],\n",
-        "    \"bbox\": {\n",
-        "          \"top\": 933,\n",
-        "          \"left\": 541,\n",
-        "          \"height\": 191,\n",
-        "          \"width\": 330\n",
-        "        }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polygon"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Anotation\n",
-        "polygon_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon\",\n",
-        "  confidence = 0.5,\n",
-        "  value=lb_types.Polygon(\n",
-        "        points=[lb_types.Point(x=1489.581,y=183.934), lb_types.Point(x=2278.306,y=256.885), lb_types.Point(x=2428.197,y=200.437), lb_types.Point(x=2560.0,y=335.419),\n",
-        "                lb_types.Point(x=2557.386,y=503.165), lb_types.Point(x=2320.596,y=503.103), lb_types.Point(x=2156.083, y=628.943), lb_types.Point(x=2161.111,y=785.519),\n",
-        "                lb_types.Point(x=2002.115, y=894.647), lb_types.Point(x=1838.456,y=877.874), lb_types.Point(x=1436.53,y=874.636), lb_types.Point(x=1411.403,y=758.579),\n",
-        "                lb_types.Point(x=1353.853,y=751.74), lb_types.Point(x=1345.264, y=453.461), lb_types.Point(x=1426.011,y=421.129)]\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "\n",
-        "polygon_prediction_ndjson = {\n",
-        "  \"name\": \"polygon\",\n",
-        "  \"confidence\": 0.5,\n",
-        "  \"polygon\": [\n",
-        "    {\"x\": 1489.581, \"y\": 183.934},\n",
-        "    {\"x\": 2278.306, \"y\": 256.885},\n",
-        "    {\"x\": 2428.197, \"y\": 200.437},\n",
-        "    {\"x\": 2560.0, \"y\": 335.419},\n",
-        "    {\"x\": 2557.386, \"y\": 503.165},\n",
-        "    {\"x\": 2320.596, \"y\": 503.103},\n",
-        "    {\"x\": 2156.083, \"y\": 628.943},\n",
-        "    {\"x\": 2161.111, \"y\": 785.519},\n",
-        "    {\"x\": 2002.115, \"y\": 894.647},\n",
-        "    {\"x\": 1838.456, \"y\": 877.874},\n",
-        "    {\"x\": 1436.53, \"y\": 874.636},\n",
-        "    {\"x\": 1411.403, \"y\": 758.579},\n",
-        "    {\"x\": 1353.853, \"y\": 751.74},\n",
-        "    {\"x\": 1345.264, \"y\": 453.461},\n",
-        "    {\"x\": 1426.011, \"y\": 421.129},\n",
-        "    {\"x\": 1489.581, \"y\": 183.934}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Classification: Free-form text"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python annotation\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",\n",
-        "  value=lb_types.Text(answer=\"sample text\", confidence=0.5)\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "text_annotation_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "  \"confidence\": 0.5\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Segmentation mask"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Raster Segmentation (Byte string array)\n",
-        "url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\n",
-        "response = requests.get(url)\n",
-        "\n",
-        "mask_data = lb.types.MaskData(im_bytes=response.content) # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\n",
-        "mask_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"mask\",\n",
-        "    value=lb_types.Mask(\n",
-        "        mask=mask_data,\n",
-        "        color=(255, 255, 255))\n",
-        ")\n",
-        "\n",
-        "# NDJSON using instanceURI, bytes array is not fully supported.\n",
-        "mask_prediction_ndjson = {\n",
-        "  \"name\": \"mask\",\n",
-        "  \"classifications\": [],\n",
-        "  \"mask\": {\"instanceURI\": url,\n",
-        "  \"colorRGB\": (255, 255, 255)}\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Segmentation mask with nested classification"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "url_2 = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg_with_subclass.png\"\n",
-        "response_2 = requests.get(url_2)\n",
-        "mask_data_2 = lb_types.MaskData(im_bytes=response_2.content)\n",
-        "\n",
-        "# Python annotation\n",
-        "mask_with_text_subclass_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"mask_with_text_subclass\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Mask(\n",
-        "    mask=mask_data_2,\n",
-        "    color=(255, 255, 255)),\n",
-        "  classifications=[\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_free_text\",\n",
-        "      value=lb_types.Text(answer=\"free text answer\")\n",
-        "    )]\n",
-        ")\n",
-        "\n",
-        "# NDJSON using instanceURI, bytes array is not fully supported.\n",
-        "mask_with_text_subclass_prediction_ndjson = {\n",
-        "  \"name\": \"mask_with_text_subclass\",\n",
-        "  \"mask\": {\"instanceURI\": url_2,\n",
-        "  \"colorRGB\": (255, 255, 255)},\n",
-        "  \"classifications\":[{\n",
-        "    \"name\": \"sub_free_text\",\n",
-        "    \"answer\": \"free text answer\"\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Point"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "point_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"point\",\n",
-        "  confidence=0.5,\n",
-        "  value = lb_types.Point(x=1166.606, y=1441.768),\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "point_prediction_ndjson = {\n",
-        "  \"name\": \"point\",\n",
-        "  \"confidence\": 0.5,\n",
-        "  \"classifications\": [],\n",
-        "  \"point\": {\"x\": 1166.606, \"y\": 1441.768}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Polyline"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "\n",
-        "polyline_prediction = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline\",\n",
-        "  confidence=0.5,\n",
-        "  value=lb_types.Line(\n",
-        "        points=[lb_types.Point(x=2534.353, y=249.471), lb_types.Point(x=2429.492, y=182.092), lb_types.Point(x=2294.322, y=221.962), lb_types.Point(x=2224.491, y=180.463), lb_types.Point(x=2136.123, y=204.716),\n",
-        "                lb_types.Point(x=1712.247, y=173.949), lb_types.Point(x=1703.838, y=84.438), lb_types.Point(x=1579.772, y=82.61), lb_types.Point(x=1583.442, y=167.552),\n",
-        "                lb_types.Point(x=1478.869, y=164.903), lb_types.Point(x=1418.941, y=318.149), lb_types.Point(x=1243.128, y=400.815), lb_types.Point(x=1022.067, y=319.007),\n",
-        "                lb_types.Point(x=892.367, y=379.216), lb_types.Point(x=670.273, y=364.408), lb_types.Point(x=613.114, y=288.16), lb_types.Point(x=377.559, y=238.251),\n",
-        "                lb_types.Point(x=368.087, y=185.064), lb_types.Point(x=246.557, y=167.286), lb_types.Point(x=236.648, y=285.61), lb_types.Point(x=90.929, y=326.412)]\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "polyline_prediction_ndjson = {\n",
-        "  \"name\": \"polyline\",\n",
-        "  \"confidence\":0.5,\n",
-        "  \"classifications\": [],\n",
-        "  \"line\": [\n",
-        "    {\"x\": 2534.353, \"y\": 249.471},\n",
-        "    {\"x\": 2429.492, \"y\": 182.092},\n",
-        "    {\"x\": 2294.322, \"y\": 221.962},\n",
-        "    {\"x\": 2224.491, \"y\": 180.463},\n",
-        "    {\"x\": 2136.123, \"y\": 204.716},\n",
-        "    {\"x\": 1712.247, \"y\": 173.949},\n",
-        "    {\"x\": 1703.838, \"y\": 84.438},\n",
-        "    {\"x\": 1579.772, \"y\": 82.61},\n",
-        "    {\"x\": 1583.442, \"y\": 167.552},\n",
-        "    {\"x\": 1478.869, \"y\": 164.903},\n",
-        "    {\"x\": 1418.941, \"y\": 318.149},\n",
-        "    {\"x\": 1243.128, \"y\": 400.815},\n",
-        "    {\"x\": 1022.067, \"y\": 319.007},\n",
-        "    {\"x\": 892.367, \"y\": 379.216},\n",
-        "    {\"x\": 670.273, \"y\": 364.408},\n",
-        "    {\"x\": 613.114, \"y\": 288.16},\n",
-        "    {\"x\": 377.559, \"y\": 238.251},\n",
-        "    {\"x\": 368.087, \"y\": 185.064},\n",
-        "    {\"x\": 246.557, \"y\": 167.286},\n",
-        "    {\"x\": 236.648, \"y\": 285.61},\n",
-        "    {\"x\": 90.929, \"y\": 326.412}\n",
-        "  ]\n",
-        "}\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as batch to the project\n",
-        "global_key = \"2560px-Kitano_Street_Kobe01s.jpeg\"\n",
-        "test_img_url = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "dataset = client.create_dataset(name=\"image_prediction_demo\")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\"\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ]\n",
-        "            )\n",
-        "          ]\n",
-        "        ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    ),\n",
-        "  ],\n",
-        "  tools=[ # List of tools\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.BBOX,\n",
-        "      name=\"bounding_box\"),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.BBOX,\n",
-        "      name=\"bbox_with_radio_subclass\",\n",
-        "      classifications=[\n",
-        "            lb.Classification(\n",
-        "                class_type=lb.Classification.Type.RADIO,\n",
-        "                name=\"sub_radio_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                ]\n",
-        "              ),\n",
-        "        ]\n",
-        "      ),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.POLYGON,\n",
-        "      name=\"polygon\"),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.RASTER_SEGMENTATION,\n",
-        "      name=\"mask\"),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.RASTER_SEGMENTATION,\n",
-        "      name=\"mask_with_text_subclass\",\n",
-        "      classifications=[\n",
-        "          lb.Classification(\n",
-        "              class_type=lb.Classification.Type.TEXT,\n",
-        "              name=\"sub_free_text\")\n",
-        "          ]\n",
-        "      ),\n",
-        " \t  lb.Tool(\n",
-        "      tool=lb.Tool.Type.POINT,\n",
-        "      name=\"point\"),\n",
-        "    lb.Tool(\n",
-        "      tool=lb.Tool.Type.LINE,\n",
-        "      name=\"polyline\")]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Image Prediction Import Demo\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Image\n",
-        "                                  )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"image_model_run_\" + str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5. Create the predictions payload\n",
-        "\n",
-        "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
-        "\n",
-        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label for predictions\n",
-        "label_prediction = []\n",
-        "label_prediction.append(lb_types.Label(\n",
-        "    data=lb_types.ImageData(global_key=global_key),\n",
-        "    annotations = [\n",
-        "      radio_prediction,\n",
-        "      nested_radio_prediction,\n",
-        "      checklist_prediction,\n",
-        "      nested_checklist_prediction,\n",
-        "      bbox_prediction,\n",
-        "      bbox_with_radio_subclass_prediction,\n",
-        "      polyline_prediction,\n",
-        "      polygon_prediction,\n",
-        "      mask_prediction,\n",
-        "      mask_with_text_subclass_prediction,\n",
-        "      point_prediction,\n",
-        "      text_annotation\n",
-        "      ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "If using NDJSON:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_prediction_ndjson = []\n",
-        "\n",
-        "for annot in [\n",
-        "    radio_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    bbox_prediction_ndjson,\n",
-        "    bbox_with_radio_subclass_prediction_ndjson,\n",
-        "    polygon_prediction_ndjson,\n",
-        "    mask_prediction_ndjson,\n",
-        "    mask_with_text_subclass_prediction_ndjson,\n",
-        "    point_prediction_ndjson,\n",
-        "    polyline_prediction_ndjson,\n",
-        "    text_annotation_ndjson,\n",
-        "    nested_radio_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson\n",
-        "]:\n",
-        "  annot.update({\n",
-        "      \"dataRow\": {\"globalKey\": global_key}\n",
-        "  })\n",
-        "  label_prediction_ndjson.append(annot)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6. Upload the predictions payload to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_prediction)\n",
-        "\n",
-        "# Errors will appear for prediction uploads that failed.\n",
-        "print(\"Errors:\",  upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to a model run\n",
-        "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations. \n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.1. Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"Image Prediction Demo\",\n",
-        "                                    media_type=lb.MediaType.Image)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.2. Create a batch to send to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_predictions_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########### Annotations ###########\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-        "        name=\"second_radio_answer\"))\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(answer=[\n",
-        "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n",
-        "    ])\n",
-        ")\n",
-        "\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"bounding_box\",\n",
-        "  value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=1915, y=1307),  # x= left + width , y = top + height\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n",
-        "        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"first_sub_radio_answer\", confidence=0.5))\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "polygon_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polygon\",\n",
-        "  value=lb_types.Polygon(\n",
-        "        points=[lb_types.Point(x=1489.581,y=183.934), lb_types.Point(x=2278.306,y=256.885), lb_types.Point(x=2428.197,y=200.437), lb_types.Point(x=2560.0,y=335.419),\n",
-        "                lb_types.Point(x=2557.386,y=503.165), lb_types.Point(x=2320.596,y=503.103), lb_types.Point(x=2156.083, y=628.943), lb_types.Point(x=2161.111,y=785.519),\n",
-        "                lb_types.Point(x=2002.115, y=894.647), lb_types.Point(x=1838.456,y=877.874), lb_types.Point(x=1436.53,y=874.636), lb_types.Point(x=1411.403,y=758.579),\n",
-        "                lb_types.Point(x=1353.853,y=751.74), lb_types.Point(x=1345.264, y=453.461), lb_types.Point(x=1426.011,y=421.129)]\n",
-        "    ),\n",
-        ")\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"free_text\",\n",
-        "    value=lb_types.Text(answer=\"sample text\")\n",
-        ")\n",
-        "\n",
-        "mask_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"mask\",\n",
-        "    value=lb_types.Mask(\n",
-        "        mask=mask_data,\n",
-        "        color=(255, 255, 255))\n",
-        ")\n",
-        "\n",
-        "mask_with_text_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"mask_with_text_subclass\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Mask(\n",
-        "    mask=mask_data_2,\n",
-        "    color=(255, 255, 255)),\n",
-        "  classifications=[\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_free_text\",\n",
-        "      value=lb_types.Text(answer=\"free text answer\")\n",
-        "    )]\n",
-        ")\n",
-        "\n",
-        "point_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"point\",\n",
-        "  value = lb_types.Point(x=1166.606, y=1441.768),\n",
-        ")\n",
-        "\n",
-        "polyline_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"polyline\",\n",
-        "  value=lb_types.Line(\n",
-        "        points=[lb_types.Point(x=2534.353, y=249.471), lb_types.Point(x=2429.492, y=182.092), lb_types.Point(x=2294.322, y=221.962), lb_types.Point(x=2224.491, y=180.463), lb_types.Point(x=2136.123, y=204.716),\n",
-        "                lb_types.Point(x=1712.247, y=173.949), lb_types.Point(x=1703.838, y=84.438), lb_types.Point(x=1579.772, y=82.61), lb_types.Point(x=1583.442, y=167.552),\n",
-        "                lb_types.Point(x=1478.869, y=164.903), lb_types.Point(x=1418.941, y=318.149), lb_types.Point(x=1243.128, y=400.815), lb_types.Point(x=1022.067, y=319.007),\n",
-        "                lb_types.Point(x=892.367, y=379.216), lb_types.Point(x=670.273, y=364.408), lb_types.Point(x=613.114, y=288.16), lb_types.Point(x=377.559, y=238.251),\n",
-        "                lb_types.Point(x=368.087, y=185.064), lb_types.Point(x=246.557, y=167.286), lb_types.Point(x=236.648, y=285.61), lb_types.Point(x=90.929, y=326.412)]\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.4. Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n",
-        "label = []\n",
-        "annotations = [\n",
-        "    radio_annotation,\n",
-        "    nested_radio_annotation,\n",
-        "    checklist_annotation,\n",
-        "    nested_checklist_annotation,\n",
-        "    text_annotation,\n",
-        "    bbox_annotation,\n",
-        "    bbox_with_radio_subclass_annotation,\n",
-        "    polygon_annotation,\n",
-        "    mask_annotation,\n",
-        "    mask_with_text_subclass_annotation,\n",
-        "    point_annotation,\n",
-        "    polyline_annotation\n",
-        "]\n",
-        "label.append(\n",
-        "    lb_types.Label(\n",
-        "        data={\"global_key\" : global_key },\n",
-        "        annotations=annotations))"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.5. Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"annotation_import_\" + str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.6 Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the annotations from the project and add them to the model\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Optional deletions for cleanup \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Image Prediction Import\n",
+                "\n",
+                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for image assets. \n",
+                "\n",
+                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+                "\n",
+                "**Supported annotations that can be uploaded through the SDK**\n",
+                "\n",
+                "- Bounding box \n",
+                "- Polygon\n",
+                "- Point\n",
+                "- Polyline \n",
+                "- Raster Segmentation\n",
+                "- Classification free-text\n",
+                "- Classification - radio\n",
+                "- Classification - checklist\n",
+                "\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Notes:\n",
+                "    * If you are importing more than 1,000 mask predictions at a time, consider submitting separate jobs, as they can take longer than other prediction types to import.\n",
+                "    * After the execution of this notebook a complete Model Run with predictions will be created in your organization. "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API Key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Radio (single-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Nested radio and checklist"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Checklist (multi-choice)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotations\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding Box"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Bounding box with nested classification "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "####### Bounding box with nested classification #######\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\",\n            \"confidence\": 0.5\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polygon"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\n# NDJSON\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Classification: Free-form text"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Segmentation mask"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "### Raster Segmentation (Byte string array)\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\nresponse = requests.get(url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nmask_prediction = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_prediction_ndjson = {\n    \"name\": \"mask\",\n    \"classifications\": [],\n    \"mask\": {\n        \"instanceURI\": url,\n        \"colorRGB\": (255, 255, 255)\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Segmentation mask with nested classification"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "url_2 = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg_with_subclass.png\"\nresponse_2 = requests.get(url_2)\nmask_data_2 = lb_types.MaskData(im_bytes=response_2.content)\n\n# Python annotation\nmask_with_text_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_with_text_subclass_prediction_ndjson = {\n    \"name\":\n        \"mask_with_text_subclass\",\n    \"mask\": {\n        \"instanceURI\": url_2,\n        \"colorRGB\": (255, 255, 255)\n    },\n    \"classifications\": [{\n        \"name\": \"sub_free_text\",\n        \"answer\": \"free text answer\"\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Point"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Polyline"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\n\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s.jpeg\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(name=\"image_prediction_demo\")\ntask = dataset.create_data_rows([test_img_url])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(name=\"image_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5. Create the predictions payload\n",
+                "\n",
+                "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
+                "\n",
+                "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            mask_prediction,\n            mask_with_text_subclass_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "If using NDJSON:"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        mask_prediction_ndjson,\n        mask_with_text_subclass_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6. Upload the predictions payload to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to a model run\n",
+                "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations. \n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.1. Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Image Prediction Demo\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.2. Create a batch to send to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nmask_annotation = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\nmask_with_text_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.4. Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    mask_annotation,\n    mask_with_text_subclass_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.5. Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.6 Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the annotations from the project and add them to the model\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Optional deletions for cleanup \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/pdf_predictions.ipynb b/examples/prediction_upload/pdf_predictions.ipynb
index 665498879..a07642a63 100644
--- a/examples/prediction_upload/pdf_predictions.ipynb
+++ b/examples/prediction_upload/pdf_predictions.ipynb
@@ -1,1170 +1,422 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 2,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# PDF Prediction Import "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "*Annotation types*\n",
-        "- Checklist classification (including nested classifications)\n",
-        "- Radio classifications (including nested classifications)\n",
-        "- Free text classifications\n",
-        "- Bounding box\n",
-        "- Entities\n",
-        "- Relationships (only supported for MAL imports)\n",
-        "\n",
-        "\n",
-        "*NDJson*\n",
-        "- Checklist classification (including nested classifications)\n",
-        "- Radio classifications (including nested classifications)\n",
-        "- Free text classifications\n",
-        "- Bounding box \n",
-        "- Entities \n",
-        "- Relationships (only supported for MAL imports)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        " !pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import uuid\n",
-        "import json\n",
-        "import requests\n",
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API key"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY= \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Entity ##########\n",
-        "\n",
-        "# Annotation Types\n",
-        "entities_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"named_entity\",\n",
-        "    confidence=0.5,\n",
-        "    value= lb_types.DocumentEntity(\n",
-        "        name=\"named_entity\",\n",
-        "        textSelections=[\n",
-        "            lb_types.DocumentTextSelection(\n",
-        "                token_ids=[],\n",
-        "                group_id=\"\",\n",
-        "                page=1\n",
-        "            )\n",
-        "        ]\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "entities_prediction_ndjson = {\n",
-        "    \"name\": \"named_entity\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"textSelections\": [\n",
-        "        {\n",
-        "            \"tokenIds\": [\n",
-        "                \"<UUID>\",\n",
-        "            ],\n",
-        "            \"groupId\": \"<UUID>\",\n",
-        "            \"page\": 1,\n",
-        "        }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########### Radio Classification #########\n",
-        "\n",
-        "# Annotation types\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer =\n",
-        "        lb_types.ClassificationAnswer(name = \"first_radio_answer\", confidence=0.5)\n",
-        "    )\n",
-        ")\n",
-        "# NDJSON\n",
-        "radio_prediction_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\", \"confidence\":0.5}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############ Checklist Classification ###########\n",
-        "\n",
-        "# Annotation types\n",
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\", confidence=0.5),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\", confidence=0.5)\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_prediction_ndjson = {\n",
-        "  \"name\": \"checklist_question\",\n",
-        "  \"answer\": [\n",
-        "    {\"name\": \"first_checklist_answer\", \"confidence\":0.5},\n",
-        "    {\"name\": \"second_checklist_answer\", \"confidence\":0.5}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############ Bounding Box ###########\n",
-        "\n",
-        "bbox_dim_1 = {\n",
-        "            \"top\": 135.3,\n",
-        "            \"left\": 102.771,\n",
-        "            \"height\": 109.843,\n",
-        "            \"width\": 415.8\n",
-        "      }\n",
-        "bbox_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=bbox_dim_1[\"left\"], y=bbox_dim_1[\"top\"]),  # x = left, y = top\n",
-        "        end=lb_types.Point(x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"], y=bbox_dim_1[\"top\"]+ bbox_dim_1[\"height\"]),  # x= left + width , y = top + height\n",
-        "        page=0,\n",
-        "        unit=lb_types.RectangleUnit.POINTS\n",
-        "        )\n",
-        "    )\n",
-        "\n",
-        "bbox_prediction_ndjson = {\n",
-        "  \"name\": \"bounding_box\",\n",
-        "  \"bbox\": bbox_dim_1,\n",
-        "  \"page\": 0,\n",
-        "  \"unit\": \"POINTS\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# ############ global nested classifications ###########\n",
-        "\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5 # Confidence scores should be added to the answer\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\n",
-        "            \"name\": \"first_sub_checklist_answer\",\n",
-        "            \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "          }\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}\n",
-        "\n",
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5 # Confidence scores should be added to the answer\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"confidence\": 0.5,\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\",\n",
-        "                     \"confidence\": 0.5}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "############## Classification Free-form text ##############\n",
-        "\n",
-        "text_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",  # must match your ontology feature\"s name\n",
-        "  value=lb_types.Text(answer=\"sample text\", confidence=0.5)\n",
-        ")\n",
-        "\n",
-        "\n",
-        "text_prediction_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "  \"confidence\": 0.5\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######### BBOX with nested classifications #########\n",
-        "\n",
-        "bbox_dim = {\n",
-        "        \"top\": 226.757,\n",
-        "        \"left\": 317.271,\n",
-        "        \"height\": 194.229,\n",
-        "        \"width\": 249.386\n",
-        "    }\n",
-        "\n",
-        "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "    confidence=0.5,\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=bbox_dim[\"left\"], y=bbox_dim[\"top\"]), # x = left, y = top\n",
-        "        end=lb_types.Point(x=bbox_dim[\"left\"] + bbox_dim[\"width\"], y=bbox_dim[\"top\"] + bbox_dim[\"height\"]), # x= left + width , y = top + height\n",
-        "        unit=lb_types.RectangleUnit.POINTS,\n",
-        "        page=1\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(\n",
-        "          answer=lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_radio_answer\",\n",
-        "            confidence=0.5,\n",
-        "            classifications=[\n",
-        "              lb_types.ClassificationAnnotation(\n",
-        "                name=\"second_sub_radio_question\",\n",
-        "                value=lb_types.Radio(\n",
-        "                  answer=lb_types.ClassificationAnswer(\n",
-        "                    name=\"second_sub_radio_answer\",\n",
-        "                    confidence=0.5\n",
-        "                  )\n",
-        "                )\n",
-        "              )\n",
-        "            ]\n",
-        "          )\n",
-        "          )\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "bbox_with_radio_subclass_prediction_ndjson = {\n",
-        "  \"name\": \"bbox_with_radio_subclass\",\n",
-        "  \"classifications\": [\n",
-        "    {\n",
-        "      \"name\": \"sub_radio_question\",\n",
-        "      \"answer\": {\n",
-        "          \"name\": \"first_sub_radio_answer\",\n",
-        "          \"confidence\": 0.5,\n",
-        "          \"classifications\": [\n",
-        "              {\n",
-        "                  \"name\": \"second_sub_radio_question\",\n",
-        "                  \"answer\": {\n",
-        "                      \"name\": \"second_sub_radio_answer\", \"confidence\": 0.5}\n",
-        "               }\n",
-        "            ]\n",
-        "        }\n",
-        "    }\n",
-        "  ],\n",
-        "  \"bbox\":bbox_dim,\n",
-        "  \"page\": 1,\n",
-        "  \"unit\": \"POINTS\"\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "############ NER with nested classifications ########\n",
-        "\n",
-        "\n",
-        "ner_with_checklist_subclass_prediction = lb_types.ObjectAnnotation(\n",
-        "  name=\"ner_with_checklist_subclass\",\n",
-        " confidence=0.5,\n",
-        "  value=lb_types.DocumentEntity(\n",
-        "    name=\"ner_with_checklist_subclass\",\n",
-        "    text_selections=[\n",
-        "      lb_types.DocumentTextSelection(\n",
-        "        token_ids=[],\n",
-        "        group_id=\"\",\n",
-        "        page=1\n",
-        "      )\n",
-        "    ]\n",
-        "  ),\n",
-        "  classifications=[\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_checklist_question\",\n",
-        "      value=lb_types.Checklist(\n",
-        "      answer=[lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\", confidence=0.5)]\n",
-        "      )\n",
-        "    )\n",
-        "  ]\n",
-        ")\n",
-        "\n",
-        "\n",
-        "ner_with_checklist_subclass_prediction_ndjson = {\n",
-        "  \"name\": \"ner_with_checklist_subclass\",\n",
-        "  \"classifications\":[\n",
-        "    {\n",
-        "      \"name\": \"sub_checklist_question\",\n",
-        "      \"answer\": [{\"name\": \"first_sub_checklist_answer\", \"confidence\":0.5 }]\n",
-        "    }\n",
-        "  ],\n",
-        "  \"textSelections\": [\n",
-        "      {\n",
-        "          \"tokenIds\": [\n",
-        "              \"<UUID>\"\n",
-        "          ],\n",
-        "          \"groupId\": \"<UUID>\",\n",
-        "          \"page\": 1\n",
-        "      }\n",
-        "  ]\n",
-        "}\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
-        "\n",
-        "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
-        "- The document must have no more than 15 pages.\n",
-        "- The file size should not exceed 20 MB.\n",
-        "\n",
-        "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
-        "\n",
-        "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
-        "\n",
-        "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "global_key = \"0801.3483.pdf\"\n",
-        "img_url = {\n",
-        "    \"row_data\": {\n",
-        "      \"pdf_url\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n",
-        "    },\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "\n",
-        "\n",
-        "dataset = client.create_dataset(name=\"pdf_demo_dataset\")\n",
-        "task = dataset.create_data_rows([img_url])\n",
-        "task.wait_till_done()\n",
-        "print(f\"Failed data rows: {task.failed_data_rows}\")\n",
-        "print(f\"Errors: {task.errors}\")\n",
-        "\n",
-        "if task.errors:\n",
-        "    for error in task.errors:\n",
-        "        if 'Duplicate global key' in error['message'] and dataset.row_count == 0:\n",
-        "            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n",
-        "            print(f\"Deleting empty dataset: {dataset}\")\n",
-        "            dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup the ontology and link the tools created above.\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL,\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\"),\n",
-        "        lb.Option(value=\"second_radio_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL,\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "        class_type=lb.Classification.Type.RADIO,\n",
-        "        name=\"nested_radio_question\",\n",
-        "        scope = lb.Classification.Scope.GLOBAL,\n",
-        "        options=[\n",
-        "            lb.Option(\"first_radio_answer\",\n",
-        "                options=[\n",
-        "                    lb.Classification(\n",
-        "                        class_type=lb.Classification.Type.RADIO,\n",
-        "                        name=\"sub_radio_question\",\n",
-        "                        options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                    )\n",
-        "                ])\n",
-        "          ]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      scope = lb.Classification.Scope.GLOBAL,\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ])\n",
-        "      ]\n",
-        "    ),\n",
-        "  ],\n",
-        "  tools=[ # List of Tool objects\n",
-        "    lb.Tool( tool=lb.Tool.Type.BBOX,name=\"bounding_box\"),\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n",
-        "    lb.Tool(tool=lb.Tool.Type.NER,\n",
-        "            name=\"ner_with_checklist_subclass\",\n",
-        "            classifications=[\n",
-        "              lb.Classification(\n",
-        "                class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                name=\"sub_checklist_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_checklist_answer\")\n",
-        "                ]\n",
-        "              )\n",
-        "          ]),\n",
-        "    lb.Tool( tool=lb.Tool.Type.BBOX,\n",
-        "            name=\"bbox_with_radio_subclass\",\n",
-        "            classifications=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.RADIO,\n",
-        "                  name=\"sub_radio_question\",\n",
-        "                  options=[\n",
-        "                    lb.Option(\n",
-        "                      value=\"first_sub_radio_answer\" ,\n",
-        "                      options=[\n",
-        "                        lb.Classification(\n",
-        "                          class_type=lb.Classification.Type.RADIO,\n",
-        "                          name=\"second_sub_radio_question\",\n",
-        "                          options=[lb.Option(\"second_sub_radio_answer\")]\n",
-        "                        )]\n",
-        "                    )]\n",
-        "                )]\n",
-        "      )]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Document Annotation Import Demo\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Document)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"PDF_model_run_\"+ str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5: Create the predictions payload\n",
-        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-        "\n",
-        "The resulting payload should have exactly the same content for annotations that are supported by both"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "To extract the generated text layer url we first need to export the data row"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "client.enable_experimental = True\n",
-        "task = lb.DataRow.export(client=client,global_keys=[global_key])\n",
-        "task.wait_till_done()\n",
-        "stream = task.get_stream()\n",
-        "\n",
-        "text_layer = \"\"\n",
-        "for output in stream:\n",
-        "    output_json = json.loads(output.json_str)\n",
-        "    text_layer = output_json['media_attributes']['text_layer_url']\n",
-        "print(text_layer)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Helper method\n",
-        "def update_text_selections(annotation, group_id, list_tokens, page):\n",
-        "  return annotation.update({\n",
-        "    \"textSelections\": [\n",
-        "      {\n",
-        "        \"groupId\": group_id,\n",
-        "        \"tokenIds\": list_tokens,\n",
-        "        \"page\": page\n",
-        "      }\n",
-        "    ]\n",
-        "  })\n",
-        "\n",
-        "\n",
-        "# Fetch the content of the text layer\n",
-        "res = requests.get(text_layer)\n",
-        "\n",
-        "# Phrases that we want to annotation obtained from the text layer url\n",
-        "content_phrases = [\"Metal-insulator (MI) transitions have been one of the\",\n",
-        "                   \"T. Sasaki, N. Yoneyama, and N. Kobayashi\"]\n",
-        "\n",
-        "# Parse the text layer\n",
-        "text_selections = []\n",
-        "text_selections_ner = []\n",
-        "\n",
-        "\n",
-        "for obj in json.loads(res.text):\n",
-        "  for group in obj[\"groups\"]:\n",
-        "    if group[\"content\"] == content_phrases[0]:\n",
-        "      list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n",
-        "      # build text selections for Python Annotation Types\n",
-        "      document_text_selection = lb_types.DocumentTextSelection(groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n",
-        "      text_selections.append(document_text_selection)\n",
-        "      # build text selection for the NDJson annotations\n",
-        "      update_text_selections(annotation=entities_prediction_ndjson,\n",
-        "                             group_id=group[\"id\"], # id representing group of words\n",
-        "                             list_tokens=list_tokens, # ids representing individual words from the group\n",
-        "                             page=1)\n",
-        "    if group[\"content\"] == content_phrases[1]:\n",
-        "      list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n",
-        "      # build text selections for Python Annotation Types\n",
-        "      ner_text_selection = lb_types.DocumentTextSelection(groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n",
-        "      text_selections_ner.append(ner_text_selection)\n",
-        "      # build text selection for the NDJson annotations\n",
-        "      update_text_selections(annotation=ner_with_checklist_subclass_prediction_ndjson,\n",
-        "                             group_id=group[\"id\"], # id representing group of words\n",
-        "                             list_tokens=list_tokens_2, # ids representing individual words from the group\n",
-        "                             page=1)\n",
-        "\n",
-        "\n",
-        "#re-write the entity annotation with text selections\n",
-        "entities_prediction_document_entity = lb_types.DocumentEntity(name=\"named_entity\",confidence=0.5, textSelections = text_selections)\n",
-        "entities_prediction = lb_types.ObjectAnnotation(name=\"named_entity\",value=entities_prediction_document_entity)\n",
-        "\n",
-        "\n",
-        "# re-write the entity annotation + subclassification with text selections\n",
-        "classifications = [\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_checklist_question\",\n",
-        "      value=lb_types.Checklist(\n",
-        "      answer=[lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\", confidence=0.5)]\n",
-        "      )\n",
-        "    )\n",
-        "  ]\n",
-        "ner_annotation_with_subclass = lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\",confidence=0.5, textSelections= text_selections_ner)\n",
-        "ner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(name=\"ner_with_checklist_subclass\",\n",
-        "                                                                   confidence=0.5,\n",
-        "                                                                   value=ner_annotation_with_subclass,\n",
-        "                                                                   classifications=classifications)\n",
-        "\n",
-        "# Final NDJSON and python annotations\n",
-        "print(f\"entities_annotations_ndjson={entities_prediction_ndjson}\")\n",
-        "print(f\"entities_annotation={entities_prediction}\")\n",
-        "print(f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_prediction_ndjson}\")\n",
-        "print(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Python annotation \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_predictions = []\n",
-        "\n",
-        "label_predictions.append(\n",
-        "    lb_types.Label(\n",
-        "        data=lb_types.DocumentData(\n",
-        "            global_key=global_key),\n",
-        "        annotations = [\n",
-        "            entities_prediction,\n",
-        "            checklist_prediction,\n",
-        "            nested_checklist_prediction,\n",
-        "            text_prediction,\n",
-        "            radio_prediction,\n",
-        "            nested_radio_prediction,\n",
-        "            bbox_prediction,\n",
-        "            bbox_with_radio_subclass_prediction,\n",
-        "            ner_with_checklist_subclass_prediction\n",
-        "        ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "If using NDJSON: "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "\n",
-        "label_predictions_ndjson = []\n",
-        "for annot in [\n",
-        "    entities_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson,\n",
-        "    text_prediction_ndjson,\n",
-        "    radio_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson,\n",
-        "    bbox_prediction_ndjson,\n",
-        "    bbox_with_radio_subclass_prediction_ndjson,\n",
-        "    ner_with_checklist_subclass_prediction_ndjson\n",
-        "  ]:\n",
-        "  annot.update({\n",
-        "      \"dataRow\": {\"globalKey\": global_key},\n",
-        "  })\n",
-        "  label_predictions_ndjson.append(annot)\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6: Upload the predictions payload to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_predictions)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to the Model Run\n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.1 Create a labelbox project \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project = client.create_project(name=\"Document Prediction Import Demo\",\n",
-        "                                    media_type=lb.MediaType.Document)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.2 Create a batch to send to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_text_prediction_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "entities_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"named_entity\",\n",
-        "    value= lb_types.DocumentEntity(\n",
-        "        name=\"named_entity\",\n",
-        "        textSelections=text_selections\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer =\n",
-        "        lb_types.ClassificationAnswer(name = \"first_radio_answer\")\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(answer = [\n",
-        "        lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n",
-        "        lb_types.ClassificationAnswer(name = \"second_checklist_answer\"),\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "bbox_dim_1 = {\n",
-        "            \"top\": 135.3,\n",
-        "            \"left\": 102.771,\n",
-        "            \"height\": 109.843,\n",
-        "            \"width\": 415.8\n",
-        "      }\n",
-        "bbox_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bounding_box\",  # must match your ontology feature\"s name\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=bbox_dim_1[\"left\"], y=bbox_dim_1[\"top\"]),  # x = left, y = top\n",
-        "        end=lb_types.Point(x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"], y=bbox_dim_1[\"top\"]+ bbox_dim_1[\"height\"]),  # x= left + width , y = top + height\n",
-        "        page=0,\n",
-        "        unit=lb_types.RectangleUnit.POINTS\n",
-        "        )\n",
-        "    )\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",\n",
-        "  value=lb_types.Text(answer=\"sample text\")\n",
-        ")\n",
-        "\n",
-        "bbox_dim = {\n",
-        "        \"top\": 226.757,\n",
-        "        \"left\": 317.271,\n",
-        "        \"height\": 194.229,\n",
-        "        \"width\": 249.386\n",
-        "    }\n",
-        "\n",
-        "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"bbox_with_radio_subclass\",\n",
-        "\n",
-        "    value=lb_types.DocumentRectangle(\n",
-        "        start=lb_types.Point(x=bbox_dim[\"left\"], y=bbox_dim[\"top\"]), # x = left, y = top\n",
-        "        end=lb_types.Point(x=bbox_dim[\"left\"] + bbox_dim[\"width\"], y=bbox_dim[\"top\"] + bbox_dim[\"height\"]), # x= left + width , y = top + height\n",
-        "        unit=lb_types.RectangleUnit.POINTS,\n",
-        "        page=1\n",
-        "    ),\n",
-        "    classifications=[\n",
-        "    \tlb_types.ClassificationAnnotation(\n",
-        "        \tname=\"sub_radio_question\",\n",
-        "      \t\tvalue=lb_types.Radio(\n",
-        "          answer=lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_radio_answer\",\n",
-        "\n",
-        "            classifications=[\n",
-        "              lb_types.ClassificationAnnotation(\n",
-        "                name=\"second_sub_radio_question\",\n",
-        "\n",
-        "                value=lb_types.Radio(\n",
-        "                  answer=lb_types.ClassificationAnswer(\n",
-        "                    name=\"second_sub_radio_answer\"\n",
-        "                  )\n",
-        "                )\n",
-        "              )\n",
-        "            ]\n",
-        "          )\n",
-        "          )\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n",
-        "  name=\"ner_with_checklist_subclass\",\n",
-        "  value=lb_types.DocumentEntity(\n",
-        "    name=\"ner_with_checklist_subclass\",\n",
-        "    text_selections=text_selections_ner\n",
-        "  ),\n",
-        "  classifications=[\n",
-        "    lb_types.ClassificationAnnotation(\n",
-        "      name=\"sub_checklist_question\",\n",
-        "      value=lb_types.Checklist(\n",
-        "      answer=[lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")]\n",
-        "      )\n",
-        "    )\n",
-        "  ]\n",
-        ")\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.4 Create the label object "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "labels = []\n",
-        "\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "        data={\"global_key\" : global_key },\n",
-        "        annotations = [\n",
-        "            entities_annotation,\n",
-        "            checklist_annotation,\n",
-        "            nested_checklist_annotation,\n",
-        "            text_annotation,\n",
-        "            radio_annotation,\n",
-        "            nested_radio_annotation,\n",
-        "            bbox_annotation,\n",
-        "            bbox_with_radio_subclass_annotation,\n",
-        "            ner_with_checklist_subclass_annotation\n",
-        "        ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.5 Upload annotations to the project using Label import\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"text_label_import_job\"+ str(uuid.uuid4()),\n",
-        "    labels=labels)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "7.6 Send the annotations to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Option deletions for cleanup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# PDF Prediction Import "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "*Annotation types*\n",
+                "- Checklist classification (including nested classifications)\n",
+                "- Radio classifications (including nested classifications)\n",
+                "- Free text classifications\n",
+                "- Bounding box\n",
+                "- Entities\n",
+                "- Relationships (only supported for MAL imports)\n",
+                "\n",
+                "\n",
+                "*NDJson*\n",
+                "- Checklist classification (including nested classifications)\n",
+                "- Radio classifications (including nested classifications)\n",
+                "- Free text classifications\n",
+                "- Bounding box \n",
+                "- Entities \n",
+                "- Relationships (only supported for MAL imports)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API key"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########## Entity ##########\n\n# Annotation Types\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"confidence\":\n        0.5,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########### Radio Classification #########\n\n# Annotation types\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############ Bounding Box ###########\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": bbox_dim_1,\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# ############ global nested classifications ###########\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############## Classification Free-form text ##############\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######### BBOX with nested classifications #########\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\",\n                                confidence=0.5,\n                            )),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\",\n                    \"confidence\": 0.5,\n                },\n            }],\n        },\n    }],\n    \"bbox\": bbox_dim,\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
+                "\n",
+                "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
+                "- The document must have no more than 15 pages.\n",
+                "- The file size should not exceed 20 MB.\n",
+                "\n",
+                "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
+                "\n",
+                "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
+                "\n",
+                "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "global_key = \"0801.3483.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(name=\"PDF_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5: Create the predictions payload\n",
+                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+                "\n",
+                "The resulting payload should have exactly the same content for annotations that are supported by both"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "To extract the generated text layer url we first need to export the data row"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n\n# re-write the entity annotation with text selections\nentities_prediction_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", confidence=0.5, textSelections=text_selections)\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_prediction_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    textSelections=text_selections_ner,\n)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_prediction_ndjson}\")\nprint(f\"entities_annotation={entities_prediction}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_prediction_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Python annotation \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_predictions = []\n\nlabel_predictions.append(\n    lb_types.Label(\n        data=lb_types.DocumentData(global_key=global_key),\n        annotations=[\n            entities_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            ner_with_checklist_subclass_prediction,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "If using NDJSON: "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_predictions_ndjson = []\nfor annot in [\n        entities_prediction_ndjson,\n        checklist_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        ner_with_checklist_subclass_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_predictions_ndjson.append(annot)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6: Upload the predictions payload to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to the Model Run\n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.1 Create a labelbox project \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(name=\"Document Prediction Import Demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.2 Create a batch to send to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "entities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(name=\"named_entity\",\n                                  textSelections=text_selections),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",)),\n            )\n        ],\n    )),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\",\n                                  text_selections=text_selections_ner),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.4 Create the label object "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.5 Upload annotations to the project using Label import\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "7.6 Send the annotations to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Option deletions for cleanup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/text_predictions.ipynb b/examples/prediction_upload/text_predictions.ipynb
index 96ec0445a..4d80894f9 100644
--- a/examples/prediction_upload/text_predictions.ipynb
+++ b/examples/prediction_upload/text_predictions.ipynb
@@ -1,744 +1,348 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Text Prediction Import\n",
-        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for text assets. \n",
-        "\n",
-        "Supported annotations that can be uploaded through the SDK: \n",
-        "\n",
-        "* Entity\n",
-        "* Classification radio \n",
-        "* Classification checklist \n",
-        "* Classification free-form text \n",
-        "\n",
-        "**Not** supported:\n",
-        "* Segmentation mask\n",
-        "* Polygon\n",
-        "* Bounding box \n",
-        "* Polyline\n",
-        "* Point \n",
-        "\n",
-        "\n",
-        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API Key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY= \"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Entities ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "named_entity = lb_types.TextEntity(start=10, end=20)\n",
-        "entities_prediction = lb_types.ObjectAnnotation(value=named_entity, name = \"named_entity\", confidence=0.5)\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "entities_prediction_ndjson = {\n",
-        "    \"name\": \"named_entity\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"location\": {\n",
-        "        \"start\": 10,\n",
-        "        \"end\": 20\n",
-        "    }\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Radio (single choice ) ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer =\n",
-        "        lb_types.ClassificationAnswer(name = \"first_radio_answer\", confidence=0.5)\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "# NDJSON\n",
-        "radio_prediction_ndjson = {\n",
-        "  \"name\": \"radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\", \"confidence\": 0.5}\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n",
-        "\n",
-        "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_radio_prediction_ndjson= {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\n",
-        "      \"name\": \"first_radio_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\": [{\n",
-        "          \"name\":\"sub_radio_question\",\n",
-        "          \"answer\": { \"name\" : \"first_sub_radio_answer\",\n",
-        "                     \"confidence\": 0.5}\n",
-        "        }]\n",
-        "    }\n",
-        "}\n",
-        "\n",
-        "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5, # Confidence scores should be added to the answer\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5,\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5, # Confidence scores should be added to the answer\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\",\n",
-        "                     \"confidence\": 0.5}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Checklist ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(\n",
-        "        answer = [\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"first_checklist_answer\",\n",
-        "                confidence=0.5\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"second_checklist_answer\",\n",
-        "                confidence=0.5\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"third_checklist_answer\",\n",
-        "                confidence=0.5\n",
-        "            )\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "checklist_prediction_ndjson = {\n",
-        "  \"name\": \"checklist_question\",\n",
-        "  \"answer\": [\n",
-        "    {\"name\": \"first_checklist_answer\", \"confidence\": 0.5}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classification Free-Form text  ##########\n",
-        "\n",
-        "# Python annotation\n",
-        "text_prediction = lb_types.ClassificationAnnotation(\n",
-        "    name = \"free_text\",\n",
-        "    value = lb_types.Text(answer=\"sample text\", confidence=0.5)\n",
-        ")\n",
-        "\n",
-        "#  NDJSON\n",
-        "text_prediction_ndjson = {\n",
-        "  \"name\": \"free_text\",\n",
-        "  \"answer\": \"sample text\",\n",
-        "  \"confidence\": 0.5\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as batch to the project\n",
-        "global_key = \"lorem-ipsum.txt\"\n",
-        "test_img_url = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "dataset = client.create_dataset(\n",
-        "    name=\"text prediction demo dataset\",\n",
-        "    iam_integration=None # Removing this argument will default to the organziation's default iam integration\n",
-        "    )\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors:\",task.errors)\n",
-        "print(\"Failed data rows:\", task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup the ontology and link the tools created above.\n",
-        "\n",
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "  classifications=[ # List of Classification objects\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"radio_question\",\n",
-        "      options=[lb.Option(value=\"first_radio_answer\")]\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.RADIO,\n",
-        "      name=\"nested_radio_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_radio_answer\",\n",
-        "          options=[\n",
-        "              lb.Classification(\n",
-        "                class_type=lb.Classification.Type.RADIO,\n",
-        "                name=\"sub_radio_question\",\n",
-        "                options=[\n",
-        "                  lb.Option(value=\"first_sub_radio_answer\")\n",
-        "                ]\n",
-        "            ),\n",
-        "          ]\n",
-        "        )\n",
-        "      ],\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"checklist_question\",\n",
-        "      options=[\n",
-        "        lb.Option(value=\"first_checklist_answer\"),\n",
-        "        lb.Option(value=\"second_checklist_answer\"),\n",
-        "        lb.Option(value=\"third_checklist_answer\")\n",
-        "      ]\n",
-        "    ),\n",
-        "     lb.Classification(\n",
-        "      class_type=lb.Classification.Type.TEXT,\n",
-        "      name=\"free_text\"\n",
-        "    ),\n",
-        "    lb.Classification(\n",
-        "      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "      name=\"nested_checklist_question\",\n",
-        "      options=[\n",
-        "          lb.Option(\"first_checklist_answer\",\n",
-        "            options=[\n",
-        "              lb.Classification(\n",
-        "                  class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                  name=\"sub_checklist_question\",\n",
-        "                  options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "              )\n",
-        "          ]\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  ],\n",
-        "  tools=[ # List of Tool objects\n",
-        "         lb.Tool(tool=lb.Tool.Type.NER,\n",
-        "              name=\"named_entity\")\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Text Predictions\", ontology_builder.asdict() , media_type=lb.MediaType.Text)\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"text_model_run_\"+ str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5. Create the predictions payload\n",
-        "\n",
-        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-        "\n",
-        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-        "\n",
-        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label for predictions\n",
-        "label_predictions = []\n",
-        "label_predictions.append(\n",
-        "  lb_types.Label(\n",
-        "    data={\"global_key\" : global_key },\n",
-        "    annotations = [\n",
-        "      entities_prediction,\n",
-        "      nested_radio_prediction,\n",
-        "      radio_prediction,\n",
-        "      checklist_prediction,\n",
-        "      nested_checklist_prediction,\n",
-        "      text_prediction,\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "If using NDJSON: "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_ndjson_predictions= []\n",
-        "for annot in [\n",
-        "    entities_prediction_ndjson,\n",
-        "    radio_prediction_ndjson,\n",
-        "    checklist_prediction_ndjson,\n",
-        "    text_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson\n",
-        "  ]:\n",
-        "  annot.update({\n",
-        "      \"dataRow\": {\"globalKey\": global_key}\n",
-        "  })\n",
-        "  label_ndjson_predictions.append(annot)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6. Upload the predictions payload to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_predictions)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to the Model Run \n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.1. Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"Text Prediction Import Demo\",\n",
-        "                                    media_type=lb.MediaType.Text)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.2. Create a batch to send to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_text_prediction_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "named_entity = lb_types.TextEntity(start=10, end=20)\n",
-        "entities_annotation = lb_types.ObjectAnnotation(value=named_entity, name = \"named_entity\")\n",
-        "\n",
-        "\n",
-        "radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_question\",\n",
-        "    value=lb_types.Radio(answer =\n",
-        "        lb_types.ClassificationAnswer(name = \"first_radio_answer\")\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")\n",
-        "\n",
-        "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name=\"checklist_question\",\n",
-        "    value=lb_types.Checklist(\n",
-        "        answer = [\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"first_checklist_answer\"\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"second_checklist_answer\"\n",
-        "            ),\n",
-        "            lb_types.ClassificationAnswer(\n",
-        "                name = \"third_checklist_answer\"\n",
-        "            )\n",
-        "    ])\n",
-        "  )\n",
-        "\n",
-        "text_annotation = lb_types.ClassificationAnnotation(\n",
-        "    name = \"free_text\",\n",
-        "    value = lb_types.Text(answer=\"sample text\")\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.4. Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n",
-        "# Create a Label for predictions\n",
-        "label = []\n",
-        "label.append(\n",
-        "  lb_types.Label(\n",
-        "    data=lb_types.TextData(global_key=global_key),\n",
-        "    annotations = [\n",
-        "      entities_annotation,\n",
-        "      nested_radio_annotation,\n",
-        "      radio_annotation,\n",
-        "      checklist_annotation,\n",
-        "      nested_checklist_annotation,\n",
-        "      text_annotation,\n",
-        "    ]\n",
-        "  )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.5. Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"text_label_import_job\"+ str(uuid.uuid4()),\n",
-        "    labels=label)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.6 Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Optional deletions for cleanup \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Text Prediction Import\n",
+                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for text assets. \n",
+                "\n",
+                "Supported annotations that can be uploaded through the SDK: \n",
+                "\n",
+                "* Entity\n",
+                "* Classification radio \n",
+                "* Classification checklist \n",
+                "* Classification free-form text \n",
+                "\n",
+                "**Not** supported:\n",
+                "* Segmentation mask\n",
+                "* Polygon\n",
+                "* Bounding box \n",
+                "* Polyline\n",
+                "* Point \n",
+                "\n",
+                "\n",
+                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API Key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nentities_prediction = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\",\n                                                confidence=0.5)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\": \"named_entity\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 10,\n        \"end\": 20\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as batch to the project\nglobal_key = \"lorem-ipsum.txt\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"text prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\")\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Text Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Text,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(name=\"text_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5. Create the predictions payload\n",
+                "\n",
+                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+                "\n",
+                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+                "\n",
+                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label for predictions\nlabel_predictions = []\nlabel_predictions.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_prediction,\n            nested_radio_prediction,\n            radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "If using NDJSON: "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_ndjson_predictions = []\nfor annot in [\n        entities_prediction_ndjson,\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson_predictions.append(annot)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6. Upload the predictions payload to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to the Model Run \n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.1. Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Text Prediction Import Demo\",\n                                media_type=lb.MediaType.Text)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.2. Create a batch to send to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "named_entity = lb_types.TextEntity(start=10, end=20)\nentities_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\")\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.4. Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n# Create a Label for predictions\nlabel = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.TextData(global_key=global_key),\n        annotations=[\n            entities_annotation,\n            nested_radio_annotation,\n            radio_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n        ],\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.5. Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.6 Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Optional deletions for cleanup \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/video_predictions.ipynb b/examples/prediction_upload/video_predictions.ipynb
index 46e57a2bc..0899ee332 100644
--- a/examples/prediction_upload/video_predictions.ipynb
+++ b/examples/prediction_upload/video_predictions.ipynb
@@ -1,1444 +1,378 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Video Prediction Import \n",
-        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for video assets.\n",
-        "\n",
-        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-        "\n",
-        "**Supported annotations that can be uploaded through the SDK**\n",
-        "- Bounding box\n",
-        "- Point\n",
-        "- Polyline\n",
-        "- Classification - radio\n",
-        "- Classification - checklist\n",
-        "- Classification - free text\n",
-        "- Nested classifications \n",
-        "\n",
-        "**NOT** supported:\n",
-        "- Polygons [not supported in video editor or model]\n",
-        "- Raster segmentation masks [not supported in model]\n",
-        "- Vector segmentation masks [not supported in video editor]\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "!pip install -q \"labelbox[data]\""
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "import labelbox as lb\n",
-        "import labelbox.types as lb_types\n",
-        "import uuid"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Replace with your API Key \n",
-        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "API_KEY=\"\"\n",
-        "client = lb.Client(API_KEY)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Supported Predictions\n",
-        "- Confidence scores are currently not supported for segment or frame annotations, which are required for bounding box, point, and line for video assets. For this tutorial, only the radio and checklist annotations will have confidence scores."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Bounding box (frame specific) ###########\n",
-        "\n",
-        "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation\n",
-        "\n",
-        "# bbox dimensions\n",
-        "bbox_dm = {\n",
-        "  \"top\":617,\n",
-        "  \"left\":1371,\n",
-        "  \"height\":419,\n",
-        "  \"width\":505\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "bbox_prediction = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=13,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=15,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]),\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=19,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]),\n",
-        "      )\n",
-        "  )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "# NDJSON\n",
-        "bbox_prediction_ndjson = {\n",
-        "    \"name\" : \"bbox_video\",\n",
-        "    \"segments\" : [{\n",
-        "        \"keyframes\" : [\n",
-        "            {\n",
-        "              \"frame\": 13,\n",
-        "              \"bbox\" : bbox_dm\n",
-        "           },\n",
-        "           {\n",
-        "              \"frame\": 15,\n",
-        "              \"bbox\" : bbox_dm\n",
-        "           },\n",
-        "           {\n",
-        "              \"frame\": 19,\n",
-        "              \"bbox\" : bbox_dm\n",
-        "           }\n",
-        "        ]\n",
-        "      }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Point ########\n",
-        "# Confidence score is not supported for VideoObjectAnnotation\n",
-        "# Python Annotation\n",
-        "point_prediction = [\n",
-        "    lb_types.VideoObjectAnnotation(\n",
-        "        name = \"point_video\",\n",
-        "        keyframe=True,\n",
-        "        frame=17,\n",
-        "        value = lb_types.Point(x=660.134, y=407.926),\n",
-        "        )\n",
-        "]\n",
-        "\n",
-        "# NDJSON\n",
-        "point_prediction_ndjson = {\n",
-        "    \"name\": \"point_video\",\n",
-        "    \"confidence\": 0.5,\n",
-        "    \"segments\": [{\n",
-        "        \"keyframes\": [{\n",
-        "            \"frame\": 17,\n",
-        "            \"point\" : {\n",
-        "                \"x\": 660.134 ,\n",
-        "                \"y\": 407.926\n",
-        "            }\n",
-        "        }]\n",
-        "    }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Polyline (frame specific) ########\n",
-        "# confidence scores are not supported in polyline annotations\n",
-        "\n",
-        "# Python Annotation\n",
-        "polyline_prediction = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=5,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=12,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=20,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=24,\n",
-        "    segment_index=1,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=45,\n",
-        "    segment_index=1,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  )\n",
-        "\n",
-        "]\n",
-        "\n",
-        "# NDJSON\n",
-        "polyline_prediction_ndjson = {\n",
-        "  \"name\": \"line_video_frame\",\n",
-        "  \"segments\": [\n",
-        "      {\n",
-        "        \"keyframes\": [\n",
-        "          {\n",
-        "            \"frame\": 5,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 680,\n",
-        "              \"y\": 100\n",
-        "            },{\n",
-        "              \"x\": 100,\n",
-        "              \"y\": 190\n",
-        "            },{\n",
-        "              \"x\": 190,\n",
-        "              \"y\": 220\n",
-        "            }]\n",
-        "          },\n",
-        "          {\n",
-        "            \"frame\": 12,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 680,\n",
-        "              \"y\": 280\n",
-        "            },{\n",
-        "              \"x\": 300,\n",
-        "              \"y\": 380\n",
-        "            },{\n",
-        "              \"x\": 400,\n",
-        "              \"y\": 460\n",
-        "            }]\n",
-        "          },\n",
-        "          {\n",
-        "            \"frame\": 20,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 680,\n",
-        "              \"y\": 180\n",
-        "            },{\n",
-        "              \"x\": 100,\n",
-        "              \"y\": 200\n",
-        "            },{\n",
-        "              \"x\": 200,\n",
-        "              \"y\": 260\n",
-        "            }]\n",
-        "          }\n",
-        "        ]\n",
-        "      },\n",
-        "      {\n",
-        "        \"keyframes\": [\n",
-        "          {\n",
-        "            \"frame\": 24,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 300,\n",
-        "              \"y\": 310\n",
-        "            },{\n",
-        "              \"x\": 330,\n",
-        "              \"y\": 430\n",
-        "            }]\n",
-        "          },\n",
-        "          {\n",
-        "            \"frame\": 45,\n",
-        "            \"line\": [{\n",
-        "              \"x\": 600,\n",
-        "              \"y\": 810\n",
-        "            },{\n",
-        "              \"x\": 900,\n",
-        "              \"y\": 930\n",
-        "            }]\n",
-        "          }\n",
-        "        ]\n",
-        "      }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######## Frame base classifications ########\n",
-        "\n",
-        "# Python Annotation\n",
-        "radio_prediction = [\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"radio_class\",\n",
-        "        frame=9,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(\n",
-        "          name = \"first_radio_answer\",\n",
-        "          confidence=0.5\n",
-        "          ))\n",
-        "    ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"radio_class\",\n",
-        "        frame=15,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(\n",
-        "          name = \"first_radio_answer\",\n",
-        "          confidence=0.5\n",
-        "          ))\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "checklist_prediction= [\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=29,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"first_checklist_answer\",\n",
-        "                    confidence=0.5\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=35,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"first_checklist_answer\",\n",
-        "                    confidence=0.5\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=39,\n",
-        "        segment_index=1,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"second_checklist_answer\",\n",
-        "                    confidence=0.5\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=45,\n",
-        "        segment_index=1,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"second_checklist_answer\",\n",
-        "                    confidence=0.5\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "## NDJSON\n",
-        "frame_radio_classification_prediction_ndjson = {\n",
-        "    \"name\": \"radio_class\",\n",
-        "    \"answer\": { \"name\": \"first_radio_answer\", \"frames\": [{\"start\": 9, \"end\": 15}]}\n",
-        "}\n",
-        "\n",
-        "## frame specific\n",
-        "frame_checklist_classification_prediction_ndjson = {\n",
-        "    \"name\": \"checklist_class\",\n",
-        "    \"answer\": [\n",
-        "        { \"name\": \"first_checklist_answer\" , \"frames\": [{\"start\": 29, \"end\": 35 }]},\n",
-        "        { \"name\": \"second_checklist_answer\", \"frames\": [{\"start\": 39, \"end\": 45 }]}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "####### Global Classifications #########\n",
-        "\n",
-        "# Python Annotation\n",
-        "## For global classifications use ClassificationAnnotation\n",
-        "global_radio_prediction = [lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_class_global\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(\n",
-        "      name = \"first_radio_answer\",\n",
-        "      confidence=0.5\n",
-        "      ))\n",
-        ")]\n",
-        "\n",
-        "global_checklist_prediction=[lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_class_global\",\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        " )]\n",
-        "\n",
-        "# NDJSON\n",
-        "global_radio_classification_ndjson = {\n",
-        "    \"name\": \"radio_class_global\",\n",
-        "    \"answer\": { \"name\": \"first_radio_answer\", \"confidence\": 0.5}\n",
-        "}\n",
-        "\n",
-        "\n",
-        "global_checklist_classification_ndjson = {\n",
-        "    \"name\": \"checklist_class_global\",\n",
-        "    \"answer\": [\n",
-        "        { \"name\": \"first_checklist_answer\" , \"confidence\": 0.5},\n",
-        "        { \"name\": \"second_checklist_answer\", \"confidence\": 0.5}\n",
-        "  ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Nested Global Classification ###########\n",
-        "\n",
-        "# Python Annotation\n",
-        "nested_radio_prediction =[lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      confidence=0.5 ,\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\",\n",
-        "              confidence=0.5\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")]\n",
-        "\n",
-        "# NDJSON\n",
-        "nested_radio_prediction_ndjson = {\n",
-        "  \"name\": \"nested_radio_question\",\n",
-        "  \"answer\": {\"name\": \"first_radio_answer\", \"confidence\": 0.5,\n",
-        "  \"classifications\" : [\n",
-        "    {\"name\": \"sub_radio_question\", \"answer\": {\"name\": \"first_sub_radio_answer\", \"confidence\": 0.5}}\n",
-        "   ]\n",
-        "  }\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "nested_checklist_prediction = [lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      confidence=0.5 ,\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\",\n",
-        "            confidence=0.5\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")]\n",
-        "\n",
-        "# NDJSON\n",
-        "nested_checklist_prediction_ndjson = {\n",
-        "  \"name\": \"nested_checklist_question\",\n",
-        "  \"answer\": [{\n",
-        "      \"name\": \"first_checklist_answer\",\n",
-        "      \"confidence\": 0.5,\n",
-        "      \"classifications\" : [\n",
-        "        {\n",
-        "          \"name\": \"sub_checklist_question\",\n",
-        "          \"answer\": {\"name\": \"first_sub_checklist_answer\", \"confidence\": 0.5}\n",
-        "        }\n",
-        "      ]\n",
-        "  }]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "########## Classifications under frame base tools ##########\n",
-        "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n",
-        "\n",
-        "# bounding box dimensions\n",
-        "bbox_dm2 = {\n",
-        "  \"top\": 146.0,\n",
-        "  \"left\": 98.0,\n",
-        "  \"height\": 382.0,\n",
-        "  \"width\": 341.0\n",
-        "}\n",
-        "\n",
-        "# Python Annotation\n",
-        "frame_bbox_with_checklist_subclass_prediction = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=10,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=11,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]),\n",
-        "      ),\n",
-        "    classifications=[\n",
-        "                lb_types.ClassificationAnnotation(\n",
-        "                    name='checklist_class',\n",
-        "                    value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(\n",
-        "                        name=\"first_checklist_answer\",\n",
-        "                        confidence=0.5\n",
-        "                    )])\n",
-        "                )\n",
-        "            ]\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=13,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]),\n",
-        "      ),\n",
-        "    classifications=[\n",
-        "              lb_types.ClassificationAnnotation(\n",
-        "                  name='checklist_class',\n",
-        "                  value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(\n",
-        "                      name=\"second_checklist_answer\",\n",
-        "                      confidence=0.5\n",
-        "                  )])\n",
-        "              )\n",
-        "          ]\n",
-        "  )\n",
-        "]\n",
-        "\n",
-        "frame_bbox_with_checklist_subclass_prediction_ndjson = {\n",
-        "    \"name\": \"bbox_class\",\n",
-        "    \"segments\": [{\n",
-        "        \"keyframes\": [\n",
-        "            {\n",
-        "            \"frame\": 10,\n",
-        "            \"bbox\": bbox_dm2\n",
-        "          },\n",
-        "          {\n",
-        "          \"frame\": 11,\n",
-        "            \"bbox\": bbox_dm2,\n",
-        "            \"classifications\": [\n",
-        "              {\n",
-        "                \"name\": \"bbox_radio\",\n",
-        "                \"answer\": [{\"name\": \"first_checklist_answer\", \"confidence\": 0.5}]\n",
-        "              }\n",
-        "            ]\n",
-        "          },\n",
-        "          {\n",
-        "          \"frame\": 13,\n",
-        "            \"bbox\": bbox_dm2,\n",
-        "            \"classifications\": [\n",
-        "              {\n",
-        "                \"name\": \"bbox_radio\",\n",
-        "                \"answer\": [{\"name\": \"second_checklist_answer\", \"confidence\": 0.5}]\n",
-        "              }\n",
-        "            ]\n",
-        "          }\n",
-        "        ]\n",
-        "      }\n",
-        "    ]\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "######### Free text classification ###########\n",
-        "text_prediction = [lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",  # must match your ontology feature's name\n",
-        "  value=lb_types.Text(answer=\"sample text\", confidence=0.5)\n",
-        ")]\n",
-        "\n",
-        "text_prediction_ndjson = {\n",
-        "  'name': 'free_text',\n",
-        "  'confidence': 0.5,\n",
-        "  'answer': 'sample text',\n",
-        "}"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 1: Import data rows into Catalog"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# send a sample image as batch to the project\n",
-        "global_key = \"sample-video-2.mp4\"\n",
-        "test_img_url = {\n",
-        "    \"row_data\": \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n",
-        "    \"global_key\": global_key\n",
-        "}\n",
-        "dataset = client.create_dataset(\n",
-        "    name=\"Video prediction demo\",\n",
-        "    iam_integration=None # Removing this argument will default to the organziation's default iam integration\n",
-        ")\n",
-        "task = dataset.create_data_rows([test_img_url])\n",
-        "task.wait_till_done()\n",
-        "print(\"Errors: \",task.errors)\n",
-        "print(\"Failed data rows: \",task.failed_data_rows)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 2: Create/select an Ontology for your model predictions\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "ontology_builder = lb.OntologyBuilder(\n",
-        "    tools=[\n",
-        "        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n",
-        "        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n",
-        "        lb.Tool(\n",
-        "          tool=lb.Tool.Type.BBOX, name=\"bbox_class\",\n",
-        "          classifications=[\n",
-        "            lb.Classification(\n",
-        "            class_type=lb.Classification.Type.CHECKLIST,\n",
-        "            name=\"checklist_class\",\n",
-        "            scope = lb.Classification.Scope.INDEX, ## defined scope for frame classifications\n",
-        "            options=[\n",
-        "                lb.Option(value=\"first_checklist_answer\"),\n",
-        "                lb.Option(value=\"second_checklist_answer\")\n",
-        "            ]\n",
-        "            )\n",
-        "          ]\n",
-        "        )\n",
-        "    ],\n",
-        "    classifications=[\n",
-        "        lb.Classification(\n",
-        "            class_type=lb.Classification.Type.CHECKLIST,\n",
-        "            name=\"checklist_class\",\n",
-        "            scope = lb.Classification.Scope.INDEX, ## defined scope for frame classifications\n",
-        "            options=[\n",
-        "                lb.Option(value=\"first_checklist_answer\"),\n",
-        "                lb.Option(value=\"second_checklist_answer\")\n",
-        "            ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "            class_type=lb.Classification.Type.RADIO,\n",
-        "            name=\"radio_class\",\n",
-        "            scope = lb.Classification.Scope.INDEX,\n",
-        "            options=[\n",
-        "                lb.Option(value=\"first_radio_answer\"),\n",
-        "                lb.Option(value=\"second_radio_answer\")\n",
-        "            ]\n",
-        "        ),\n",
-        "         lb.Classification(\n",
-        "              class_type=lb.Classification.Type.RADIO,\n",
-        "              name=\"nested_radio_question\",\n",
-        "              options=[\n",
-        "                  lb.Option(\"first_radio_answer\",\n",
-        "                        options=[\n",
-        "                            lb.Classification(\n",
-        "                                class_type=lb.Classification.Type.RADIO,\n",
-        "                                name=\"sub_radio_question\",\n",
-        "                                options=[lb.Option(\"first_sub_radio_answer\")]\n",
-        "                            )\n",
-        "                        ]\n",
-        "                  )\n",
-        "              ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.CHECKLIST,\n",
-        "          name=\"nested_checklist_question\",\n",
-        "          options=[\n",
-        "              lb.Option(\"first_checklist_answer\",\n",
-        "                options=[\n",
-        "                  lb.Classification(\n",
-        "                      class_type=lb.Classification.Type.CHECKLIST,\n",
-        "                      name=\"sub_checklist_question\",\n",
-        "                      options=[lb.Option(\"first_sub_checklist_answer\")]\n",
-        "                  )\n",
-        "              ]\n",
-        "            )\n",
-        "          ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.RADIO,\n",
-        "          name=\"radio_class_global\",\n",
-        "          options=[\n",
-        "                lb.Option(value=\"first_radio_answer\"),\n",
-        "                lb.Option(value=\"second_radio_answer\")\n",
-        "            ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.CHECKLIST,\n",
-        "          name=\"checklist_class_global\",\n",
-        "          options=[\n",
-        "                lb.Option(value=\"first_checklist_answer\"),\n",
-        "                lb.Option(value=\"second_checklist_answer\")\n",
-        "          ]\n",
-        "        ),\n",
-        "        lb.Classification(\n",
-        "          class_type=lb.Classification.Type.TEXT,\n",
-        "          name=\"free_text\"\n",
-        "        )\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ontology = client.create_ontology(\"Ontology Video Annotations\",\n",
-        "                                  ontology_builder.asdict(),\n",
-        "                                  media_type=lb.MediaType.Video\n",
-        "                                  )"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 3: Create a Model and Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# create Model\n",
-        "model = client.create_model(name=\"video_model_run_\" + str(uuid.uuid4()),\n",
-        "                            ontology_id=ontology.uid)\n",
-        "# create Model Run\n",
-        "model_run = model.create_model_run(\"iteration 1\")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 4: Send data rows to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "model_run.upsert_data_rows(global_keys=[global_key])"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 5. Create the predictions payload\n",
-        "\n",
-        "Create the annotations payload using the snippets of [code here](https://docs.labelbox.com/reference/import-video-annotations).\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Python Annotation Types"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "label_predictions = []\n",
-        "annotations_list = [\n",
-        "    point_prediction,\n",
-        "    bbox_prediction,\n",
-        "    polyline_prediction,\n",
-        "    checklist_prediction,\n",
-        "    radio_prediction,\n",
-        "    nested_radio_prediction,\n",
-        "    nested_checklist_prediction,\n",
-        "    frame_bbox_with_checklist_subclass_prediction,\n",
-        "    global_radio_prediction,\n",
-        "    global_checklist_prediction,\n",
-        "    text_prediction\n",
-        "      ]\n",
-        "\n",
-        "flatten_list_annotations = [ann for ann_sublist in annotations_list for ann in ann_sublist]\n",
-        "\n",
-        "label_predictions.append(\n",
-        "    lb_types.Label(\n",
-        "        data={\"global_key\" : global_key },\n",
-        "        annotations = flatten_list_annotations\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### NDJSON annotations"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n",
-        "label_prediction_ndjson = []\n",
-        "\n",
-        "for annotation in [\n",
-        "    point_prediction_ndjson,\n",
-        "    bbox_prediction_ndjson,\n",
-        "    polyline_prediction_ndjson,\n",
-        "    frame_checklist_classification_prediction_ndjson,\n",
-        "    frame_radio_classification_prediction_ndjson,\n",
-        "    nested_radio_prediction_ndjson,\n",
-        "    nested_checklist_prediction_ndjson,\n",
-        "    frame_bbox_with_checklist_subclass_prediction_ndjson,\n",
-        "    global_radio_classification_ndjson,\n",
-        "    global_checklist_classification_ndjson,\n",
-        "    text_prediction_ndjson\n",
-        "]:\n",
-        "  annotation.update({\n",
-        "      \"dataRow\": {\n",
-        "          \"globalKey\": global_key\n",
-        "      }\n",
-        "  })\n",
-        "  label_prediction_ndjson.append(annotation)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 6. Upload the predictions payload to the Model Run "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Upload the prediction label to the Model Run\n",
-        "upload_job_prediction = model_run.add_predictions(\n",
-        "    name=\"prediction_upload_job\"+str(uuid.uuid4()),\n",
-        "    predictions=label_predictions)\n",
-        "\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_prediction.errors)\n",
-        "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Step 7: Send annotations to the Model Run \n",
-        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.1. Create a labelbox project"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Labelbox project\n",
-        "project = client.create_project(name=\"video_prediction_demo\",\n",
-        "                                    media_type=lb.MediaType.Video)\n",
-        "project.setup_editor(ontology)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.2. Create a batch to send to the project "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "project.create_batch(\n",
-        "  \"batch_video_prediction_demo\", # Each batch in a project must have a unique name\n",
-        "  global_keys=[global_key], # A list of data rows, data row ids or global keys\n",
-        "  priority=5 # priority between 1(Highest) - 5(lowest)\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.3 Create the annotations payload"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Python Annotation\n",
-        "point_annotation = [\n",
-        "    lb_types.VideoObjectAnnotation(\n",
-        "        name = \"point_video\",\n",
-        "        keyframe=True,\n",
-        "        frame=17,\n",
-        "        value = lb_types.Point(x=660.134, y=407.926),\n",
-        "        )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "######## Polyline ########\n",
-        "\n",
-        "# Python Annotation\n",
-        "polyline_annotation = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=5,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=12,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=20,\n",
-        "    segment_index=0,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=24,\n",
-        "    segment_index=1,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"line_video_frame\",\n",
-        "    keyframe=True,\n",
-        "    frame=45,\n",
-        "    segment_index=1,\n",
-        "    value=lb_types.Line(\n",
-        "          points=[lb_types.Point(x=680, y=100), lb_types.Point(x=100, y=190)]\n",
-        "      )\n",
-        "  )\n",
-        "\n",
-        "]\n",
-        "\n",
-        "radio_annotation = [\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"radio_class\",\n",
-        "        frame=9,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        "    ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"radio_class\",\n",
-        "        frame=15,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        "    )\n",
-        "]\n",
-        "\n",
-        "checklist_annotation= [\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=29,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"first_checklist_answer\"\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=35,\n",
-        "        segment_index=0,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"first_checklist_answer\"\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=39,\n",
-        "        segment_index=1,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"second_checklist_answer\"\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        ),\n",
-        "    lb_types.VideoClassificationAnnotation(\n",
-        "        name=\"checklist_class\",\n",
-        "        frame=45,\n",
-        "        segment_index=1,\n",
-        "        value=lb_types.Checklist(\n",
-        "            answer = [\n",
-        "\n",
-        "                lb_types.ClassificationAnswer(\n",
-        "                    name = \"second_checklist_answer\"\n",
-        "                )\n",
-        "            ]\n",
-        "            )\n",
-        "        )\n",
-        "]\n",
-        "\n",
-        "global_radio_annotation = [lb_types.ClassificationAnnotation(\n",
-        "    name=\"radio_class_global\",\n",
-        "    value=lb_types.Radio(answer = lb_types.ClassificationAnswer(name = \"first_radio_answer\"))\n",
-        ")]\n",
-        "\n",
-        "global_checklist_annotation=[lb_types.ClassificationAnnotation(\n",
-        "  name=\"checklist_class_global\",\n",
-        "  value=lb_types.Checklist(\n",
-        "      answer = [\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"first_checklist_answer\"\n",
-        "        ),\n",
-        "        lb_types.ClassificationAnswer(\n",
-        "            name = \"second_checklist_answer\"\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        " )]\n",
-        "\n",
-        "nested_radio_annotation =[lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_radio_question\",\n",
-        "  value=lb_types.Radio(\n",
-        "    answer=lb_types.ClassificationAnswer(\n",
-        "      name=\"first_radio_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_radio_question\",\n",
-        "          value=lb_types.Radio(\n",
-        "            answer=lb_types.ClassificationAnswer(\n",
-        "              name=\"first_sub_radio_answer\"\n",
-        "            )\n",
-        "          )\n",
-        "        )\n",
-        "      ]\n",
-        "    )\n",
-        "  )\n",
-        ")]\n",
-        "\n",
-        "nested_checklist_annotation = [lb_types.ClassificationAnnotation(\n",
-        "  name=\"nested_checklist_question\",\n",
-        "  value=lb_types.Checklist(\n",
-        "    answer=[lb_types.ClassificationAnswer(\n",
-        "      name=\"first_checklist_answer\",\n",
-        "      classifications=[\n",
-        "        lb_types.ClassificationAnnotation(\n",
-        "          name=\"sub_checklist_question\",\n",
-        "          value=lb_types.Checklist(\n",
-        "            answer=[lb_types.ClassificationAnswer(\n",
-        "            name=\"first_sub_checklist_answer\"\n",
-        "          )]\n",
-        "        ))\n",
-        "      ]\n",
-        "    )]\n",
-        "  )\n",
-        ")]\n",
-        "\n",
-        "bbox_dm2 = {\n",
-        "  \"top\": 146.0,\n",
-        "  \"left\": 98.0,\n",
-        "  \"height\": 382.0,\n",
-        "  \"width\": 341.0\n",
-        "}\n",
-        "frame_bbox_with_checklist_subclass = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=10,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=11,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]), # x= left + width , y = top + height\n",
-        "      ),\n",
-        "    classifications=[\n",
-        "                lb_types.ClassificationAnnotation(\n",
-        "                    name=\"checklist_class\",\n",
-        "                    value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(\n",
-        "                        name=\"first_checklist_answer\"\n",
-        "                    )])\n",
-        "                )\n",
-        "            ]\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_class\",\n",
-        "    keyframe=True,\n",
-        "    frame=13,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"], y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"]), # x= left + width , y = top + height\n",
-        "      ),\n",
-        "    classifications=[\n",
-        "              lb_types.ClassificationAnnotation(\n",
-        "                  name=\"checklist_class\",\n",
-        "                  value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(\n",
-        "                      name=\"second_checklist_answer\"\n",
-        "                  )])\n",
-        "              )\n",
-        "          ]\n",
-        "  )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "bbox_dm = {\n",
-        "  \"top\":617,\n",
-        "  \"left\":1371,\n",
-        "  \"height\":419,\n",
-        "  \"width\":505\n",
-        "}\n",
-        "bbox_annotation = [\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=13,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]), # x = left, y = top\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]), # x= left + width , y = top + height\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=15,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]),\n",
-        "      )\n",
-        "  ),\n",
-        "  lb_types.VideoObjectAnnotation(\n",
-        "    name = \"bbox_video\",\n",
-        "    keyframe=True,\n",
-        "    frame=19,\n",
-        "    segment_index=0,\n",
-        "    value = lb_types.Rectangle(\n",
-        "          start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n",
-        "          end=lb_types.Point(x=bbox_dm[\"left\"] + bbox_dm[\"width\"], y=bbox_dm[\"top\"] + bbox_dm[\"height\"]),\n",
-        "      )\n",
-        "  )\n",
-        "]\n",
-        "\n",
-        "\n",
-        "text_annotation = [lb_types.ClassificationAnnotation(\n",
-        "  name=\"free_text\",  # must match your ontology feature's name\n",
-        "  value=lb_types.Text(answer=\"sample text\")\n",
-        ")]\n",
-        "\n"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.4. Create the label object"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n",
-        "\n",
-        "labels = []\n",
-        "annotations_list = [\n",
-        "          checklist_annotation,\n",
-        "          radio_annotation,\n",
-        "          bbox_annotation,\n",
-        "          frame_bbox_with_checklist_subclass,\n",
-        "          point_annotation,\n",
-        "          polyline_annotation,\n",
-        "          global_checklist_annotation,\n",
-        "          global_radio_annotation,\n",
-        "          nested_checklist_annotation,\n",
-        "          nested_radio_annotation,\n",
-        "          text_annotation\n",
-        "      ]\n",
-        "\n",
-        "flatten_list_annotations = [ann for ann_sublist in annotations_list for ann in ann_sublist]\n",
-        "\n",
-        "labels.append(\n",
-        "    lb_types.Label(\n",
-        "        data=lb_types.VideoData(global_key=global_key),\n",
-        "        annotations = flatten_list_annotations\n",
-        "    )\n",
-        ")"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.5. Upload annotations to the project using Label Import"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-        "    client = client,\n",
-        "    project_id = project.uid,\n",
-        "    name=\"video_annotations_import_\" + str(uuid.uuid4()),\n",
-        "    labels=labels)\n",
-        "\n",
-        "upload_job_annotation.wait_until_done()\n",
-        "# Errors will appear for annotation uploads that failed.\n",
-        "print(\"Errors:\", upload_job_annotation.errors)\n",
-        "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "##### 7.6. Send the annotations to the Model Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# get the labels id from the project\n",
-        "model_run.upsert_labels(project_id=project.uid)"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Optional deletions for cleanup \n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# project.delete()\n",
-        "# dataset.delete()"
-      ],
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Video Prediction Import \n",
+                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for video assets.\n",
+                "\n",
+                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+                "\n",
+                "**Supported annotations that can be uploaded through the SDK**\n",
+                "- Bounding box\n",
+                "- Point\n",
+                "- Polyline\n",
+                "- Classification - radio\n",
+                "- Classification - checklist\n",
+                "- Classification - free text\n",
+                "- Nested classifications \n",
+                "\n",
+                "**NOT** supported:\n",
+                "- Polygons [not supported in video editor or model]\n",
+                "- Raster segmentation masks [not supported in model]\n",
+                "- Vector segmentation masks [not supported in video editor]\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Replace with your API Key \n",
+                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Supported Predictions\n",
+                "- Confidence scores are currently not supported for segment or frame annotations, which are required for bounding box, point, and line for video assets. For this tutorial, only the radio and checklist annotations will have confidence scores."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "####### Bounding box (frame specific) ###########\n\n# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 15,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Point ########\n# Confidence score is not supported for VideoObjectAnnotation\n# Python Annotation\npoint_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"confidence\":\n        0.5,\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Polyline (frame specific) ########\n# confidence scores are not supported in polyline annotations\n\n# Python Annotation\npolyline_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        12,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 280\n                        },\n                        {\n                            \"x\": 300,\n                            \"y\": 380\n                        },\n                        {\n                            \"x\": 400,\n                            \"y\": 460\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######## Frame base classifications ########\n\n# Python Annotation\nradio_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n]\n\nchecklist_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_prediction_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n## frame specific\nframe_checklist_classification_prediction_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 39,\n                \"end\": 45\n            }],\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "####### Global Classifications #########\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    )\n]\n\nglobal_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}\n\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\", confidence=0.5)),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\",\n                                confidence=0.5,\n                            )\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "########## Classifications under frame base tools ##########\n# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                                  confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\", confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "######### Free text classification ###########\ntext_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n    )\n]\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"confidence\": 0.5,\n    \"answer\": \"sample text\",\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 1: Import data rows into Catalog"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# send a sample image as batch to the project\nglobal_key = \"sample-video-2.mp4\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"Video prediction demo\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors: \", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 2: Create/select an Ontology for your model predictions\n",
+                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.\n                    INDEX,  ## defined scope for frame classifications\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Video Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 3: Create a Model and Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# create Model\nmodel = client.create_model(name=\"video_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 4: Send data rows to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 5. Create the predictions payload\n",
+                "\n",
+                "Create the annotations payload using the snippets of [code here](https://docs.labelbox.com/reference/import-video-annotations).\n",
+                "\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Python Annotation Types"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "label_predictions = []\nannotations_list = [\n    point_prediction,\n    bbox_prediction,\n    polyline_prediction,\n    checklist_prediction,\n    radio_prediction,\n    nested_radio_prediction,\n    nested_checklist_prediction,\n    frame_bbox_with_checklist_subclass_prediction,\n    global_radio_prediction,\n    global_checklist_prediction,\n    text_prediction,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabel_predictions.append(\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=flatten_list_annotations))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### NDJSON annotations"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel_prediction_ndjson = []\n\nfor annotation in [\n        point_prediction_ndjson,\n        bbox_prediction_ndjson,\n        polyline_prediction_ndjson,\n        frame_checklist_classification_prediction_ndjson,\n        frame_radio_classification_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        frame_bbox_with_checklist_subclass_prediction_ndjson,\n        global_radio_classification_ndjson,\n        global_checklist_classification_ndjson,\n        text_prediction_ndjson,\n]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotation)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 6. Upload the predictions payload to the Model Run "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Step 7: Send annotations to the Model Run \n",
+                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.1. Create a labelbox project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"video_prediction_demo\",\n                                media_type=lb.MediaType.Video)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.2. Create a batch to send to the project "
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"batch_video_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # A list of data rows, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.3 Create the annotations payload"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n]\n\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\nframe_bbox_with_checklist_subclass = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.4. Create the label object"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n\nlabels = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabels.append(\n    lb_types.Label(\n        data=lb_types.VideoData(global_key=global_key),\n        annotations=flatten_list_annotations,\n    ))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.5. Upload annotations to the project using Label Import"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"video_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "##### 7.6. Send the annotations to the Model Run"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Optional deletions for cleanup \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
 }
\ No newline at end of file
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index 274b2ddfb..7937d5e67 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -48,6 +48,7 @@ def format_cell(source):
             return source.replace("!", "%")
     return FormatCode(source, style_config="google")[0]
 
+
 def format_file(file_name):
     with open(file_name, "r") as file:
         data = json.load(file)
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 04dccaddb..b96105b4f 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -102,9 +102,7 @@ def make_table(base: str = "") -> str:
     Returns:
         str: markdown string file
     """
-    link_dict = make_links_dict(
-        glob.glob("**/*.ipynb", recursive=True)
-    )
+    link_dict = make_links_dict(glob.glob("**/*.ipynb", recursive=True))
     generated_markdown = base
     for link_list in link_dict.values():
         pandas_dict = {"Notebook": [], "Github": [], "Google Colab": []}
@@ -133,10 +131,11 @@ def make_table(base: str = "") -> str:
         generated_markdown += f"{df.to_html(col_space={'Notebook':400}, index=False, escape=False, justify='left')}\n\n"
     return f"{generated_markdown.rstrip()}\n"
 
+
 def main(github: bool):
     """
     Args:
-        github (bool): if this is the readme for github. 
+        github (bool): if this is the readme for github.
     """
     if github:
         with open("./README.md", "w") as readme:
@@ -145,6 +144,6 @@ def main(github: bool):
         with open("./tmp.html", "w") as readme:
             readme.write(make_table())
 
+
 if __name__ == "__main__":
     main(True)
-

From 87d71eaebc72261e6b494817ebefd1f2323aa052 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:14:09 -0500
Subject: [PATCH 16/80] testing if readme is created

---
 examples/test.ipynb | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 examples/test.ipynb

diff --git a/examples/test.ipynb b/examples/test.ipynb
new file mode 100644
index 000000000..709d82cff
--- /dev/null
+++ b/examples/test.ipynb
@@ -0,0 +1,18 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From f1ef2b6a7fc928e0249f789a236f849b77132b05 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:15:26 +0000
Subject: [PATCH 17/80] :art: Cleaned

---
 examples/test.ipynb | 49 +++++++++++++++++++++++++++++----------------
 1 file changed, 32 insertions(+), 17 deletions(-)

diff --git a/examples/test.ipynb b/examples/test.ipynb
index 709d82cff..fadc8acdb 100644
--- a/examples/test.ipynb
+++ b/examples/test.ipynb
@@ -1,18 +1,33 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/test.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/test.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        }
+    ]
+}
\ No newline at end of file

From 4b523e35ddcebbefd49b98efe85c76239c103516 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:19:35 -0500
Subject: [PATCH 18/80] modified readme create to trigger

---
 .github/workflows/notebook.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/notebook.yml b/.github/workflows/notebook.yml
index 6fc4e8acd..fcca20651 100644
--- a/.github/workflows/notebook.yml
+++ b/.github/workflows/notebook.yml
@@ -57,7 +57,7 @@ jobs:
             - added|deleted: 'examples/**.ipynb'
   create:
     needs: changes
-    if: github.event.pull_request.merged == false && needs.changes.outputs.addedOrModified == true
+    if: github.event.pull_request.merged == false && ${{ needs.changes.outputs.addedOrModified == 'true' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From 43b7aa15d8ac8817d0c1ce6adb193126bca821eb Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:21:23 +0000
Subject: [PATCH 19/80] :memo: README updated

---
 examples/README.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/examples/README.md b/examples/README.md
index 47cca5044..e2a7fdf0e 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,6 +5,25 @@
 
 - Run in google colab, view the notebooks on github, or clone the repo and run locally
 
+<h2>Test.ipynb</h2>
+
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: left;">
+      <th style="min-width: 400px;">Notebook</th>
+      <th>Github</th>
+      <th>Google Colab</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Test</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+  </tbody>
+</table>
+
 <h2>Model Experiments</h2>
 
 <table border="1" class="dataframe">

From f9ac1963fa75810175af31742dbc7d294df703e2 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:26:42 -0500
Subject: [PATCH 20/80] removed test file to test

---
 .github/workflows/notebook.yml |  6 ------
 examples/test.ipynb            | 33 ---------------------------------
 2 files changed, 39 deletions(-)
 delete mode 100644 examples/test.ipynb

diff --git a/.github/workflows/notebook.yml b/.github/workflows/notebook.yml
index fcca20651..b80711d0f 100644
--- a/.github/workflows/notebook.yml
+++ b/.github/workflows/notebook.yml
@@ -24,9 +24,6 @@ jobs:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
       - uses: ./.github/actions/notebooks
-        with:
-          rye-version: ${{ vars.RYE_VERSION }}
-          python-version: ${{ inputs.python-version }}
       - name: Format
         working-directory: examples
         run: rye run clean
@@ -65,9 +62,6 @@ jobs:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
       - uses: ./.github/actions/notebooks
-        with:
-          rye-version: ${{ vars.RYE_VERSION }}
-          python-version: ${{ inputs.python-version }}
       - name: Create readme
         working-directory: examples
         run: rye run create-readme
diff --git a/examples/test.ipynb b/examples/test.ipynb
deleted file mode 100644
index fadc8acdb..000000000
--- a/examples/test.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/test.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/test.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        }
-    ]
-}
\ No newline at end of file

From 62780c5ecc6bbb4240379bbd8bc21d7a2487bcf2 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:28:27 +0000
Subject: [PATCH 21/80] :memo: README updated

---
 examples/README.md | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index e2a7fdf0e..47cca5044 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,25 +5,6 @@
 
 - Run in google colab, view the notebooks on github, or clone the repo and run locally
 
-<h2>Test.ipynb</h2>
-
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: left;">
-      <th style="min-width: 400px;">Notebook</th>
-      <th>Github</th>
-      <th>Google Colab</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td>Test</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-  </tbody>
-</table>
-
 <h2>Model Experiments</h2>
 
 <table border="1" class="dataframe">

From dc72142d0ec9c2f6e157c2d05f56545d3e69a04c Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:32:59 -0500
Subject: [PATCH 22/80] added test file

---
 .github/workflows/notebook.yml |  4 ++--
 examples/test.ipynb            | 18 ++++++++++++++++++
 2 files changed, 20 insertions(+), 2 deletions(-)
 create mode 100644 examples/test.ipynb

diff --git a/.github/workflows/notebook.yml b/.github/workflows/notebook.yml
index b80711d0f..45a98bfb0 100644
--- a/.github/workflows/notebook.yml
+++ b/.github/workflows/notebook.yml
@@ -1,4 +1,4 @@
-name: Labelbox Example Notebook Format
+name: Labelbox Example Notebook Workflow
 
 on:
   push:
@@ -54,7 +54,7 @@ jobs:
             - added|deleted: 'examples/**.ipynb'
   create:
     needs: changes
-    if: github.event.pull_request.merged == false && ${{ needs.changes.outputs.addedOrModified == 'true' }}
+    if: github.event.pull_request.merged == false &&  needs.changes.outputs.addedOrModified == true
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
diff --git a/examples/test.ipynb b/examples/test.ipynb
new file mode 100644
index 000000000..709d82cff
--- /dev/null
+++ b/examples/test.ipynb
@@ -0,0 +1,18 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 881445fb3067f357e0261c137d91c39351765e08 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:34:24 +0000
Subject: [PATCH 23/80] :art: Cleaned

---
 examples/test.ipynb | 49 +++++++++++++++++++++++++++++----------------
 1 file changed, 32 insertions(+), 17 deletions(-)

diff --git a/examples/test.ipynb b/examples/test.ipynb
index 709d82cff..fadc8acdb 100644
--- a/examples/test.ipynb
+++ b/examples/test.ipynb
@@ -1,18 +1,33 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/test.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/test.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        }
+    ]
+}
\ No newline at end of file

From 0c1d403a62ca6a42c978e22fa50157669b2add03 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:36:53 -0500
Subject: [PATCH 24/80] switched it to one conditional

---
 .github/workflows/notebook.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/notebook.yml b/.github/workflows/notebook.yml
index 45a98bfb0..b344e4cfb 100644
--- a/.github/workflows/notebook.yml
+++ b/.github/workflows/notebook.yml
@@ -54,7 +54,7 @@ jobs:
             - added|deleted: 'examples/**.ipynb'
   create:
     needs: changes
-    if: github.event.pull_request.merged == false &&  needs.changes.outputs.addedOrModified == true
+    if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From 223e63a9510bb7d4dc471676f63902bb1f5cb160 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:37:16 -0500
Subject: [PATCH 25/80] deleted test

---
 examples/test.ipynb | 18 ------------------
 1 file changed, 18 deletions(-)
 delete mode 100644 examples/test.ipynb

diff --git a/examples/test.ipynb b/examples/test.ipynb
deleted file mode 100644
index 709d82cff..000000000
--- a/examples/test.ipynb
+++ /dev/null
@@ -1,18 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From ca0180e348cd1fefa56fe8552efb40eec8f10675 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:42:39 -0500
Subject: [PATCH 26/80] added test

---
 examples/test.ipynb | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 examples/test.ipynb

diff --git a/examples/test.ipynb b/examples/test.ipynb
new file mode 100644
index 000000000..709d82cff
--- /dev/null
+++ b/examples/test.ipynb
@@ -0,0 +1,18 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 0ef466dc2990f43d3b114aa24154f3df6ab695a6 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:43:43 +0000
Subject: [PATCH 27/80] :art: Cleaned

---
 examples/test.ipynb | 49 +++++++++++++++++++++++++++++----------------
 1 file changed, 32 insertions(+), 17 deletions(-)

diff --git a/examples/test.ipynb b/examples/test.ipynb
index 709d82cff..fadc8acdb 100644
--- a/examples/test.ipynb
+++ b/examples/test.ipynb
@@ -1,18 +1,33 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/test.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/test.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        }
+    ]
+}
\ No newline at end of file

From ec4b69af8f21209a3f920d4d58621656d9abeb35 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:44:23 +0000
Subject: [PATCH 28/80] :memo: README updated

---
 examples/README.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/examples/README.md b/examples/README.md
index 47cca5044..e2a7fdf0e 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,6 +5,25 @@
 
 - Run in google colab, view the notebooks on github, or clone the repo and run locally
 
+<h2>Test.ipynb</h2>
+
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: left;">
+      <th style="min-width: 400px;">Notebook</th>
+      <th>Github</th>
+      <th>Google Colab</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Test</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+  </tbody>
+</table>
+
 <h2>Model Experiments</h2>
 
 <table border="1" class="dataframe">

From c5217cac6cba643c7cbd6f4dbdaf9a4d85fc594a Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:46:57 -0500
Subject: [PATCH 29/80] removed test

---
 examples/test.ipynb | 33 ---------------------------------
 1 file changed, 33 deletions(-)
 delete mode 100644 examples/test.ipynb

diff --git a/examples/test.ipynb b/examples/test.ipynb
deleted file mode 100644
index fadc8acdb..000000000
--- a/examples/test.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/test.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/test.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        }
-    ]
-}
\ No newline at end of file

From f98b31dd58c063fcbffba389cd055da31d9442c7 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 21:50:59 -0500
Subject: [PATCH 30/80] testing name change

---
 ...tion_support.ipynb => export_v1_to_v2_migration_support.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/basics/{export_v1_migration_support.ipynb => export_v1_to_v2_migration_support.ipynb} (100%)

diff --git a/examples/basics/export_v1_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
similarity index 100%
rename from examples/basics/export_v1_migration_support.ipynb
rename to examples/basics/export_v1_to_v2_migration_support.ipynb

From 3788a2564b93583467125a16dc7d1999d32149c5 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 02:52:08 +0000
Subject: [PATCH 31/80] :art: Cleaned

---
 examples/basics/export_v1_to_v2_migration_support.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
index 12100fad1..c56513435 100644
--- a/examples/basics/export_v1_to_v2_migration_support.ipynb
+++ b/examples/basics/export_v1_to_v2_migration_support.ipynb
@@ -19,12 +19,12 @@
             "metadata": {},
             "source": [
                 "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_migration_support.ipynb\" target=\"_blank\"><img",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
                 "</td>",
                 "\n",
                 "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_migration_support.ipynb\" target=\"_blank\"><img",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]

From b2416375953620a6617ced367b7bbcdb52f18d47 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 22:02:31 -0500
Subject: [PATCH 32/80] maded small change

---
 .github/workflows/notebook.yml                |   2 +-
 ...b => export_v1_v2_migration_support.ipynb} |   0
 examples/basics/projects.ipynb                | 936 +++++++++++-------
 3 files changed, 595 insertions(+), 343 deletions(-)
 rename examples/basics/{export_v1_to_v2_migration_support.ipynb => export_v1_v2_migration_support.ipynb} (100%)

diff --git a/.github/workflows/notebook.yml b/.github/workflows/notebook.yml
index b344e4cfb..950f4bea0 100644
--- a/.github/workflows/notebook.yml
+++ b/.github/workflows/notebook.yml
@@ -51,7 +51,7 @@ jobs:
       with:
         filters: |
           addedOrModified:
-            - added|deleted: 'examples/**.ipynb'
+            - added|deleted: 'examples/**/*.ipynb'
   create:
     needs: changes
     if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_v2_migration_support.ipynb
similarity index 100%
rename from examples/basics/export_v1_to_v2_migration_support.ipynb
rename to examples/basics/export_v1_v2_migration_support.ipynb
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index 53dbb3bc0..b52be961a 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -1,343 +1,595 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* A project can be thought of as a specific labeling task on a set of labels\n",
-                "* That set of labels is defined by the data rows attached to the project\n",
-                "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
-                "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
-                "* Also note that deprecated functions are not explained here."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a dataset with data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Add data rows to a project \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create tags and assign them to a project\n",
-                "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create a tag"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Assign the tag to a project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tags = project.update_project_resource_tags([tag.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get project tags"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tags = project.get_resource_tags()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Attach ontology and label data rows\n",
-                "\n",
-                "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create your ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Attach ontology to project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create labels and upload them to project as ground truths"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Move data rows in project to different task queues"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fetch project configuration"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Return number of labeled data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Number of labels:\", project.get_label_count())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Copy labels and data rows from one project to a different project\n",
-                "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Copy data rows and labels\n",
-                "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
-                "\n",
-                "##### Parameters\n",
-                "\n",
-                "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
-                "\n",
-                "* `source_project_id`\n",
-                "    - The id of the project were our data rows with labels will originate.\n",
-                "* `annotation_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Clean Up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
-}
\ No newline at end of file
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<td>\n",
+    "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+    "</td>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<td>\n",
+    "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
+    "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+    "</td>\n",
+    "\n",
+    "<td>\n",
+    "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
+    "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+    "</td>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Projects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* A project can be thought of as a specific labeling task on a set of labels\n",
+    "* That set of labels is defined by the data rows attached to the project\n",
+    "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
+    "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
+    "* Also note that deprecated functions are not explained here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q \"labelbox[data]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f013aca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import labelbox as lb\n",
+    "import labelbox.types as lb_types\n",
+    "from labelbox.schema.conflict_resolution_strategy import (\n",
+    "    ConflictResolutionStrategy,\n",
+    ")\n",
+    "import uuid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1b1e6d0",
+   "metadata": {},
+   "source": [
+    "# API Key and Client\n",
+    "Provide a valid api key below in order to properly connect to the Labelbox Client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8c21ba1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add your API key\n",
+    "API_KEY = \"\"\n",
+    "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
+    "client = lb.Client(api_key=API_KEY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26fdab35",
+   "metadata": {},
+   "source": [
+    "### Create a project\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad7704c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creates an empty project\n",
+    "project = client.create_project(\n",
+    "    name=\"my-test-project\",\n",
+    "    description=\"a description\",\n",
+    "    media_type=lb.MediaType.Image,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4c8b4cb",
+   "metadata": {},
+   "source": [
+    "### Create a dataset with data rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "479a8faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = client.create_dataset(name=\"project-demo-dataset\")\n",
+    "global_keys = []\n",
+    "uploads = []\n",
+    "# Generate data rows\n",
+    "for i in range(1, 9):\n",
+    "    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n",
+    "    uploads.append(\n",
+    "        {\n",
+    "            \"row_data\": f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
+    "            \"global_key\": gb_key,\n",
+    "        }\n",
+    "    )\n",
+    "    global_keys.append(gb_key)\n",
+    "\n",
+    "task = dataset.create_data_rows(uploads)\n",
+    "task.wait_till_done()\n",
+    "print(\"ERRORS: \", task.errors)\n",
+    "print(\"RESULT URL: \", task.result_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf3bbc4a",
+   "metadata": {},
+   "source": [
+    "### Add data rows to a project \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e353a8d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.create_batch(\n",
+    "    \"project-demo\",  # each batch in a project must have a unique name\n",
+    "    global_keys=global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n",
+    "    priority=1,  # priority between 1(highest) - 5(lowest)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a45d09f9",
+   "metadata": {},
+   "source": [
+    "### Create tags and assign them to a project\n",
+    "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67bc053c",
+   "metadata": {},
+   "source": [
+    "#### Create a tag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "692499c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the organization\n",
+    "organization = client.get_organization()\n",
+    "\n",
+    "tag = organization.create_resource_tag(\n",
+    "    {\"text\": \"new-tag-name\", \"color\": \"4ed2f9\"}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f177d88",
+   "metadata": {},
+   "source": [
+    "#### Assign the tag to a project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ea6032a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tags = project.update_project_resource_tags([tag.uid])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17d944e0",
+   "metadata": {},
+   "source": [
+    "#### Get project tags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7d54242",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tags = project.get_resource_tags()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e998622b",
+   "metadata": {},
+   "source": [
+    "### Attach ontology and label data rows\n",
+    "\n",
+    "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83c6a866",
+   "metadata": {},
+   "source": [
+    "#### Create your ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11f81301",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create normalized json with a radio classification\n",
+    "ontology_builder = lb.OntologyBuilder(\n",
+    "    classifications=[  # List of Classification objects\n",
+    "        lb.Classification(\n",
+    "            class_type=lb.Classification.Type.RADIO,\n",
+    "            name=\"radio_question\",\n",
+    "            options=[\n",
+    "                lb.Option(value=\"first_radio_answer\"),\n",
+    "                lb.Option(value=\"second_radio_answer\"),\n",
+    "            ],\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "# Creating an ontology\n",
+    "ontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fa96347",
+   "metadata": {},
+   "source": [
+    "#### Attach ontology to project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4558d0d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.setup_editor(ontology)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52e6c3eb",
+   "metadata": {},
+   "source": [
+    "### Create labels and upload them to project as ground truths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41addc22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create labels\n",
+    "labels = []\n",
+    "for global_key in global_keys:\n",
+    "    labels.append(\n",
+    "        lb_types.Label(\n",
+    "            data={\"global_key\": global_key},\n",
+    "            annotations=[\n",
+    "                # Create radio classification annotation for labels\n",
+    "                lb_types.ClassificationAnnotation(\n",
+    "                    name=\"radio_question\",\n",
+    "                    value=lb_types.Radio(\n",
+    "                        answer=lb_types.ClassificationAnswer(\n",
+    "                            name=\"second_radio_answer\"\n",
+    "                        )\n",
+    "                    ),\n",
+    "                )\n",
+    "            ],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# Upload labels for the data rows in project\n",
+    "upload_job = lb.LabelImport.create_from_objects(\n",
+    "    client=client,\n",
+    "    project_id=project.uid,\n",
+    "    name=\"label_import_job\" + str(uuid.uuid4()),\n",
+    "    labels=labels,\n",
+    ")\n",
+    "\n",
+    "upload_job.wait_until_done()\n",
+    "\n",
+    "print(f\"Errors: {upload_job.errors}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02ac1902",
+   "metadata": {},
+   "source": [
+    "### Move data rows in project to different task queues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b773f65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get list of task queues for project\n",
+    "task_queues = project.task_queues()\n",
+    "\n",
+    "for task_queue in task_queues:\n",
+    "    print(task_queue)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "435677cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.move_data_rows_to_task_queue(\n",
+    "    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n",
+    "    task_queue_id=task_queues[\n",
+    "        2\n",
+    "    ].uid,  # Passing None moves data rows to \"Done\" task queue\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0da86d0",
+   "metadata": {},
+   "source": [
+    "### Fetch project configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b5923d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note the project is not fully setup many of the fields will be empty.\n",
+    "print(\"Project is not setup yet:\", project.setup_complete is None)\n",
+    "print(\"Project name:\", project.name)\n",
+    "print(\"Project description:\", project.description)\n",
+    "print(\"Media Type:\", project.media_type)\n",
+    "batches = [b for b in project.batches()]\n",
+    "print(\"Project Batches\", batches)\n",
+    "print(\"Ontology:\", project.ontology())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d0dbd7e",
+   "metadata": {},
+   "source": [
+    "### Return number of labeled data rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0222cf49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Number of labels:\", project.get_label_count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90772b6a",
+   "metadata": {},
+   "source": [
+    "### Copy labels and data rows from one project to a different project\n",
+    "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37c8ff78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an empty destination project\n",
+    "destination_project = client.create_project(\n",
+    "    name=\"destination-test-project\",\n",
+    "    description=\"a description\",\n",
+    "    media_type=lb.MediaType.Image,\n",
+    ")\n",
+    "\n",
+    "# Create ontology and attach to destination project\n",
+    "destination_ontology_builder = lb.OntologyBuilder(\n",
+    "    classifications=[  # List of Classification objects\n",
+    "        lb.Classification(\n",
+    "            class_type=lb.Classification.Type.RADIO,\n",
+    "            name=\"destination_radio_question\",\n",
+    "            options=[\n",
+    "                lb.Option(value=\"destination_first_radio_answer\"),\n",
+    "                lb.Option(value=\"destination_second_radio_answer\"),\n",
+    "            ],\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "destination_ontology = client.create_ontology(\n",
+    "    \"dest-test-ontology\", ontology_builder.asdict()\n",
+    ")\n",
+    "\n",
+    "destination_project.setup_editor(destination_ontology)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057e787e",
+   "metadata": {},
+   "source": [
+    "#### Copy data rows and labels\n",
+    "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
+    "\n",
+    "##### Parameters\n",
+    "\n",
+    "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
+    "\n",
+    "* `source_project_id`\n",
+    "    - The id of the project were our data rows with labels will originate.\n",
+    "* `annotation_ontology_mapping`\n",
+    "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
+    "* `exclude_data_rows_in_project`\n",
+    "    - Excludes data rows that are already in the project. \n",
+    "* `override_existing_annotations_rule` \n",
+    "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
+    "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+    "        * Options include:\n",
+    "            * ConflictResolutionStrategy.KeepExisting\n",
+    "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+    "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+    "* `param batch_priority`\n",
+    "    - The priority of the batch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13676921",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get ontology dictionary to obtain featureSchemaIds\n",
+    "source_ontology_normalized = ontology.normalized\n",
+    "destination_ontology_normalized = destination_ontology.normalized\n",
+    "\n",
+    "ANNOTATION_ONTOLOGY_MAPPING = {\n",
+    "    source_ontology_normalized[\"classifications\"][0][\n",
+    "        \"featureSchemaId\"\n",
+    "    ]: destination_ontology_normalized[\"classifications\"][0][\n",
+    "        \"featureSchemaId\"\n",
+    "    ],  # Classification featureSchemaID\n",
+    "    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\n",
+    "        \"featureSchemaId\"\n",
+    "    ]: destination_ontology_normalized[\"classifications\"][0][\"options\"][0][\n",
+    "        \"featureSchemaId\"\n",
+    "    ],  # Different Classification Answer featureSchemaIDs\n",
+    "    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\n",
+    "        \"featureSchemaId\"\n",
+    "    ]: destination_ontology_normalized[\"classifications\"][0][\"options\"][1][\n",
+    "        \"featureSchemaId\"\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c753ec51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "send_to_annotate_params = {\n",
+    "    \"source_project_id\": project.uid,\n",
+    "    \"annotations_ontology_mapping\": ANNOTATION_ONTOLOGY_MAPPING,\n",
+    "    \"exclude_data_rows_in_project\": False,\n",
+    "    \"override_existing_annotations_rule\": ConflictResolutionStrategy.OverrideWithPredictions,\n",
+    "    \"batch_priority\": 5,\n",
+    "}\n",
+    "\n",
+    "# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\n",
+    "queue_id = [\n",
+    "    queue.uid\n",
+    "    for queue in destination_project.task_queues()\n",
+    "    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n",
+    "][0]\n",
+    "\n",
+    "task = client.send_to_annotate_from_catalog(\n",
+    "    destination_project_id=destination_project.uid,\n",
+    "    task_queue_id=queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n",
+    "    batch_name=\"Prediction Import Demo Batch\",\n",
+    "    data_rows=lb.GlobalKeys(\n",
+    "        global_keys  # Provide a list of global keys from source project\n",
+    "    ),\n",
+    "    params=send_to_annotate_params,\n",
+    ")\n",
+    "\n",
+    "task.wait_till_done()\n",
+    "\n",
+    "print(f\"Errors: {task.errors}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean Up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# project.delete()\n",
+    "# destination_project.delete()\n",
+    "# dataset.delete()\n",
+    "# client.delete_unused_ontology(destination_ontology.uid)\n",
+    "# client.delete_unused_ontology(ontology.uid)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 6cc6fb85570eba97807753f8b50b973853047889 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 03:03:34 +0000
Subject: [PATCH 33/80] :art: Cleaned

---
 .../export_v1_v2_migration_support.ipynb      |   4 +-
 examples/basics/projects.ipynb                | 936 +++++++-----------
 2 files changed, 344 insertions(+), 596 deletions(-)

diff --git a/examples/basics/export_v1_v2_migration_support.ipynb b/examples/basics/export_v1_v2_migration_support.ipynb
index c56513435..e52293ab6 100644
--- a/examples/basics/export_v1_v2_migration_support.ipynb
+++ b/examples/basics/export_v1_v2_migration_support.ipynb
@@ -19,12 +19,12 @@
             "metadata": {},
             "source": [
                 "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
                 "</td>",
                 "\n",
                 "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index b52be961a..53dbb3bc0 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -1,595 +1,343 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<td>\n",
-    "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-    "</td>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<td>\n",
-    "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
-    "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-    "</td>\n",
-    "\n",
-    "<td>\n",
-    "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
-    "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-    "</td>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Projects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* A project can be thought of as a specific labeling task on a set of labels\n",
-    "* That set of labels is defined by the data rows attached to the project\n",
-    "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
-    "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
-    "* Also note that deprecated functions are not explained here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q \"labelbox[data]\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5f013aca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import labelbox as lb\n",
-    "import labelbox.types as lb_types\n",
-    "from labelbox.schema.conflict_resolution_strategy import (\n",
-    "    ConflictResolutionStrategy,\n",
-    ")\n",
-    "import uuid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1b1e6d0",
-   "metadata": {},
-   "source": [
-    "# API Key and Client\n",
-    "Provide a valid api key below in order to properly connect to the Labelbox Client."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e8c21ba1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add your API key\n",
-    "API_KEY = \"\"\n",
-    "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-    "client = lb.Client(api_key=API_KEY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26fdab35",
-   "metadata": {},
-   "source": [
-    "### Create a project\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad7704c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creates an empty project\n",
-    "project = client.create_project(\n",
-    "    name=\"my-test-project\",\n",
-    "    description=\"a description\",\n",
-    "    media_type=lb.MediaType.Image,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4c8b4cb",
-   "metadata": {},
-   "source": [
-    "### Create a dataset with data rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "479a8faa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = client.create_dataset(name=\"project-demo-dataset\")\n",
-    "global_keys = []\n",
-    "uploads = []\n",
-    "# Generate data rows\n",
-    "for i in range(1, 9):\n",
-    "    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n",
-    "    uploads.append(\n",
-    "        {\n",
-    "            \"row_data\": f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-    "            \"global_key\": gb_key,\n",
-    "        }\n",
-    "    )\n",
-    "    global_keys.append(gb_key)\n",
-    "\n",
-    "task = dataset.create_data_rows(uploads)\n",
-    "task.wait_till_done()\n",
-    "print(\"ERRORS: \", task.errors)\n",
-    "print(\"RESULT URL: \", task.result_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf3bbc4a",
-   "metadata": {},
-   "source": [
-    "### Add data rows to a project \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e353a8d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.create_batch(\n",
-    "    \"project-demo\",  # each batch in a project must have a unique name\n",
-    "    global_keys=global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n",
-    "    priority=1,  # priority between 1(highest) - 5(lowest)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a45d09f9",
-   "metadata": {},
-   "source": [
-    "### Create tags and assign them to a project\n",
-    "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67bc053c",
-   "metadata": {},
-   "source": [
-    "#### Create a tag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "692499c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the organization\n",
-    "organization = client.get_organization()\n",
-    "\n",
-    "tag = organization.create_resource_tag(\n",
-    "    {\"text\": \"new-tag-name\", \"color\": \"4ed2f9\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f177d88",
-   "metadata": {},
-   "source": [
-    "#### Assign the tag to a project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ea6032a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tags = project.update_project_resource_tags([tag.uid])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17d944e0",
-   "metadata": {},
-   "source": [
-    "#### Get project tags"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7d54242",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tags = project.get_resource_tags()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e998622b",
-   "metadata": {},
-   "source": [
-    "### Attach ontology and label data rows\n",
-    "\n",
-    "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83c6a866",
-   "metadata": {},
-   "source": [
-    "#### Create your ontology"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11f81301",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create normalized json with a radio classification\n",
-    "ontology_builder = lb.OntologyBuilder(\n",
-    "    classifications=[  # List of Classification objects\n",
-    "        lb.Classification(\n",
-    "            class_type=lb.Classification.Type.RADIO,\n",
-    "            name=\"radio_question\",\n",
-    "            options=[\n",
-    "                lb.Option(value=\"first_radio_answer\"),\n",
-    "                lb.Option(value=\"second_radio_answer\"),\n",
-    "            ],\n",
-    "        ),\n",
-    "    ]\n",
-    ")\n",
-    "# Creating an ontology\n",
-    "ontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fa96347",
-   "metadata": {},
-   "source": [
-    "#### Attach ontology to project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4558d0d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.setup_editor(ontology)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52e6c3eb",
-   "metadata": {},
-   "source": [
-    "### Create labels and upload them to project as ground truths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "41addc22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create labels\n",
-    "labels = []\n",
-    "for global_key in global_keys:\n",
-    "    labels.append(\n",
-    "        lb_types.Label(\n",
-    "            data={\"global_key\": global_key},\n",
-    "            annotations=[\n",
-    "                # Create radio classification annotation for labels\n",
-    "                lb_types.ClassificationAnnotation(\n",
-    "                    name=\"radio_question\",\n",
-    "                    value=lb_types.Radio(\n",
-    "                        answer=lb_types.ClassificationAnswer(\n",
-    "                            name=\"second_radio_answer\"\n",
-    "                        )\n",
-    "                    ),\n",
-    "                )\n",
-    "            ],\n",
-    "        )\n",
-    "    )\n",
-    "\n",
-    "# Upload labels for the data rows in project\n",
-    "upload_job = lb.LabelImport.create_from_objects(\n",
-    "    client=client,\n",
-    "    project_id=project.uid,\n",
-    "    name=\"label_import_job\" + str(uuid.uuid4()),\n",
-    "    labels=labels,\n",
-    ")\n",
-    "\n",
-    "upload_job.wait_until_done()\n",
-    "\n",
-    "print(f\"Errors: {upload_job.errors}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02ac1902",
-   "metadata": {},
-   "source": [
-    "### Move data rows in project to different task queues"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b773f65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get list of task queues for project\n",
-    "task_queues = project.task_queues()\n",
-    "\n",
-    "for task_queue in task_queues:\n",
-    "    print(task_queue)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "435677cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.move_data_rows_to_task_queue(\n",
-    "    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n",
-    "    task_queue_id=task_queues[\n",
-    "        2\n",
-    "    ].uid,  # Passing None moves data rows to \"Done\" task queue\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b0da86d0",
-   "metadata": {},
-   "source": [
-    "### Fetch project configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5b5923d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Note the project is not fully setup many of the fields will be empty.\n",
-    "print(\"Project is not setup yet:\", project.setup_complete is None)\n",
-    "print(\"Project name:\", project.name)\n",
-    "print(\"Project description:\", project.description)\n",
-    "print(\"Media Type:\", project.media_type)\n",
-    "batches = [b for b in project.batches()]\n",
-    "print(\"Project Batches\", batches)\n",
-    "print(\"Ontology:\", project.ontology())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d0dbd7e",
-   "metadata": {},
-   "source": [
-    "### Return number of labeled data rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0222cf49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Number of labels:\", project.get_label_count())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90772b6a",
-   "metadata": {},
-   "source": [
-    "### Copy labels and data rows from one project to a different project\n",
-    "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37c8ff78",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create an empty destination project\n",
-    "destination_project = client.create_project(\n",
-    "    name=\"destination-test-project\",\n",
-    "    description=\"a description\",\n",
-    "    media_type=lb.MediaType.Image,\n",
-    ")\n",
-    "\n",
-    "# Create ontology and attach to destination project\n",
-    "destination_ontology_builder = lb.OntologyBuilder(\n",
-    "    classifications=[  # List of Classification objects\n",
-    "        lb.Classification(\n",
-    "            class_type=lb.Classification.Type.RADIO,\n",
-    "            name=\"destination_radio_question\",\n",
-    "            options=[\n",
-    "                lb.Option(value=\"destination_first_radio_answer\"),\n",
-    "                lb.Option(value=\"destination_second_radio_answer\"),\n",
-    "            ],\n",
-    "        ),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "destination_ontology = client.create_ontology(\n",
-    "    \"dest-test-ontology\", ontology_builder.asdict()\n",
-    ")\n",
-    "\n",
-    "destination_project.setup_editor(destination_ontology)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "057e787e",
-   "metadata": {},
-   "source": [
-    "#### Copy data rows and labels\n",
-    "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
-    "\n",
-    "##### Parameters\n",
-    "\n",
-    "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
-    "\n",
-    "* `source_project_id`\n",
-    "    - The id of the project were our data rows with labels will originate.\n",
-    "* `annotation_ontology_mapping`\n",
-    "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
-    "* `exclude_data_rows_in_project`\n",
-    "    - Excludes data rows that are already in the project. \n",
-    "* `override_existing_annotations_rule` \n",
-    "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
-    "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-    "        * Options include:\n",
-    "            * ConflictResolutionStrategy.KeepExisting\n",
-    "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-    "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-    "* `param batch_priority`\n",
-    "    - The priority of the batch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13676921",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get ontology dictionary to obtain featureSchemaIds\n",
-    "source_ontology_normalized = ontology.normalized\n",
-    "destination_ontology_normalized = destination_ontology.normalized\n",
-    "\n",
-    "ANNOTATION_ONTOLOGY_MAPPING = {\n",
-    "    source_ontology_normalized[\"classifications\"][0][\n",
-    "        \"featureSchemaId\"\n",
-    "    ]: destination_ontology_normalized[\"classifications\"][0][\n",
-    "        \"featureSchemaId\"\n",
-    "    ],  # Classification featureSchemaID\n",
-    "    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\n",
-    "        \"featureSchemaId\"\n",
-    "    ]: destination_ontology_normalized[\"classifications\"][0][\"options\"][0][\n",
-    "        \"featureSchemaId\"\n",
-    "    ],  # Different Classification Answer featureSchemaIDs\n",
-    "    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\n",
-    "        \"featureSchemaId\"\n",
-    "    ]: destination_ontology_normalized[\"classifications\"][0][\"options\"][1][\n",
-    "        \"featureSchemaId\"\n",
-    "    ],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c753ec51",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "send_to_annotate_params = {\n",
-    "    \"source_project_id\": project.uid,\n",
-    "    \"annotations_ontology_mapping\": ANNOTATION_ONTOLOGY_MAPPING,\n",
-    "    \"exclude_data_rows_in_project\": False,\n",
-    "    \"override_existing_annotations_rule\": ConflictResolutionStrategy.OverrideWithPredictions,\n",
-    "    \"batch_priority\": 5,\n",
-    "}\n",
-    "\n",
-    "# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\n",
-    "queue_id = [\n",
-    "    queue.uid\n",
-    "    for queue in destination_project.task_queues()\n",
-    "    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n",
-    "][0]\n",
-    "\n",
-    "task = client.send_to_annotate_from_catalog(\n",
-    "    destination_project_id=destination_project.uid,\n",
-    "    task_queue_id=queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n",
-    "    batch_name=\"Prediction Import Demo Batch\",\n",
-    "    data_rows=lb.GlobalKeys(\n",
-    "        global_keys  # Provide a list of global keys from source project\n",
-    "    ),\n",
-    "    params=send_to_annotate_params,\n",
-    ")\n",
-    "\n",
-    "task.wait_till_done()\n",
-    "\n",
-    "print(f\"Errors: {task.errors}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Clean Up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# project.delete()\n",
-    "# destination_project.delete()\n",
-    "# dataset.delete()\n",
-    "# client.delete_unused_ontology(destination_ontology.uid)\n",
-    "# client.delete_unused_ontology(ontology.uid)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Projects"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* A project can be thought of as a specific labeling task on a set of labels\n",
+                "* That set of labels is defined by the data rows attached to the project\n",
+                "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
+                "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
+                "* Also note that deprecated functions are not explained here."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# API Key and Client\n",
+                "Provide a valid api key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a project\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a dataset with data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Add data rows to a project \n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create tags and assign them to a project\n",
+                "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create a tag"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Assign the tag to a project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "tags = project.update_project_resource_tags([tag.uid])",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Get project tags"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "tags = project.get_resource_tags()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Attach ontology and label data rows\n",
+                "\n",
+                "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create your ontology"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Attach ontology to project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create labels and upload them to project as ground truths"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Move data rows in project to different task queues"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Fetch project configuration"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Return number of labeled data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "print(\"Number of labels:\", project.get_label_count())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Copy labels and data rows from one project to a different project\n",
+                "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Copy data rows and labels\n",
+                "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
+                "\n",
+                "##### Parameters\n",
+                "\n",
+                "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
+                "\n",
+                "* `source_project_id`\n",
+                "    - The id of the project were our data rows with labels will originate.\n",
+                "* `annotation_ontology_mapping`\n",
+                "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
+                "* `exclude_data_rows_in_project`\n",
+                "    - Excludes data rows that are already in the project. \n",
+                "* `override_existing_annotations_rule` \n",
+                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
+                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+                "        * Options include:\n",
+                "            * ConflictResolutionStrategy.KeepExisting\n",
+                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+                "* `param batch_priority`\n",
+                "    - The priority of the batch."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Clean Up"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
+}
\ No newline at end of file

From 7052c04dcc831444dcbd35bf0923d263a38aa16d Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 03:04:19 +0000
Subject: [PATCH 34/80] :memo: README updated

---
 examples/README.md | 29 +++++------------------------
 1 file changed, 5 insertions(+), 24 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index e2a7fdf0e..01d93e27c 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,25 +5,6 @@
 
 - Run in google colab, view the notebooks on github, or clone the repo and run locally
 
-<h2>Test.ipynb</h2>
-
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: left;">
-      <th style="min-width: 400px;">Notebook</th>
-      <th>Github</th>
-      <th>Google Colab</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td>Test</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-  </tbody>
-</table>
-
 <h2>Model Experiments</h2>
 
 <table border="1" class="dataframe">
@@ -117,6 +98,11 @@
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
+    <tr>
+      <td>Export V1 V2 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_v1_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>Data Rows</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
@@ -127,11 +113,6 @@
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>Export V1 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_v1_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
     <tr>
       <td>Projects</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>

From d500372173b9ddd5fdc40a82c3cc8817cb8318d6 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 22:05:38 -0500
Subject: [PATCH 35/80] modified file back

---
 ...tion_support.ipynb => export_v1_to_v2_migration_support.ipynb} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/basics/{export_v1_v2_migration_support.ipynb => export_v1_to_v2_migration_support.ipynb} (100%)

diff --git a/examples/basics/export_v1_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
similarity index 100%
rename from examples/basics/export_v1_v2_migration_support.ipynb
rename to examples/basics/export_v1_to_v2_migration_support.ipynb

From 77757bd22dd1fedb96a917ab0077a53ae58a5ac9 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 03:06:48 +0000
Subject: [PATCH 36/80] :art: Cleaned

---
 examples/basics/export_v1_to_v2_migration_support.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
index e52293ab6..c56513435 100644
--- a/examples/basics/export_v1_to_v2_migration_support.ipynb
+++ b/examples/basics/export_v1_to_v2_migration_support.ipynb
@@ -19,12 +19,12 @@
             "metadata": {},
             "source": [
                 "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_v2_migration_support.ipynb\" target=\"_blank\"><img",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
                 "</td>",
                 "\n",
                 "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_v2_migration_support.ipynb\" target=\"_blank\"><img",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]

From 053a104bba8c7cc86efff4cffdf19c069cb9bfa8 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 03:07:34 +0000
Subject: [PATCH 37/80] :memo: README updated

---
 examples/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index 01d93e27c..7830be241 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -99,9 +99,9 @@
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Export V1 V2 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_v1_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Export V1 to V2 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Data Rows</td>

From 8f64d92fdb11a81bfb68f995d9430ac529c23af6 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 22:16:32 -0500
Subject: [PATCH 38/80] modified scripts to ignore certain files

---
 examples/scripts/format_notebooks.py |  5 +----
 examples/scripts/generate_readme.py  |  6 +++++-
 examples/template.ipynb              | 18 ++++++++++++++++++
 3 files changed, 24 insertions(+), 5 deletions(-)
 create mode 100644 examples/template.ipynb

diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index 7937d5e67..732678dbd 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -8,8 +8,6 @@
 Script used to format notebooks. To mass change any links or headers cells change the variables below. Hooks into github action CI tools.
 """
 
-IGNORE = ["template.ipynb"]
-
 BANNER_CELL = {
     "cell_type": "markdown",
     "id": "db768cda",
@@ -45,7 +43,7 @@
 def format_cell(source):
     for line in source.split("\n"):
         if line.strip().startswith(("!", "%")):
-            return source.replace("!", "%")
+            return source.replace("!", "%").replace("'", '"')
     return FormatCode(source, style_config="google")[0]
 
 
@@ -80,7 +78,6 @@ def format_file(file_name):
     files = [
         file
         for file in glob.glob("**/*.ipynb", recursive=True)
-        if file.split("/")[-1] not in IGNORE
     ]
     with ProcessPoolExecutor(10) as pool:
         pool.map(format_file, files)
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index b96105b4f..837a15f39 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -8,6 +8,8 @@
 then dividing them to different tables based on directory name. Pandas is used to make the tables. Using inline HTML to support our doc page. 
 """
 
+IGNORE = ["template.ipynb"]
+
 SDK_EXAMPLE_HEADER = """
 # Labelbox SDK Examples\n
 - Learn how to use the SDK by following along\n
@@ -88,7 +90,9 @@ def make_links_dict(links: str):
     """
     link_dict = defaultdict(list)
     for link in links:
-        split_link = link.split("/")[0]
+        if link.split("/")[-1] in IGNORE:
+            continue
+        split_link = link .split("/")[0]
         link_dict[split_link].append(link)
     return link_dict
 
diff --git a/examples/template.ipynb b/examples/template.ipynb
new file mode 100644
index 000000000..709d82cff
--- /dev/null
+++ b/examples/template.ipynb
@@ -0,0 +1,18 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From a5ee97b6f407b0eea38dc90af694dfdd2f5553a6 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 22:17:31 -0500
Subject: [PATCH 39/80] renamed notebook workflow to original name

---
 .github/workflows/{notebook.yml => notebooks.yml} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .github/workflows/{notebook.yml => notebooks.yml} (100%)

diff --git a/.github/workflows/notebook.yml b/.github/workflows/notebooks.yml
similarity index 100%
rename from .github/workflows/notebook.yml
rename to .github/workflows/notebooks.yml

From 71e2962d7f45e5c6d273744ef413cac6ab6a492c Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 03:18:38 +0000
Subject: [PATCH 40/80] :art: Cleaned

---
 examples/annotation_import/audio.ipynb        |  2 +-
 examples/annotation_import/dicom.ipynb        |  2 +-
 examples/annotation_import/tiled.ipynb        |  2 +-
 .../classification_confusion_matrix.ipynb     |  2 +-
 examples/integrations/sam/meta_sam.ipynb      |  2 +-
 .../custom_metrics_demo.ipynb                 |  2 +-
 .../geospatial_predictions.ipynb              |  2 +-
 examples/project_configuration/webhooks.ipynb |  2 +-
 examples/scripts/format_notebooks.py          |  5 +-
 examples/scripts/generate_readme.py           |  2 +-
 examples/template.ipynb                       | 49 ++++++++++++-------
 11 files changed, 42 insertions(+), 30 deletions(-)

diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb
index 1ff7a91f3..babc5b9ce 100644
--- a/examples/annotation_import/audio.ipynb
+++ b/examples/annotation_import/audio.ipynb
@@ -76,7 +76,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q 'labelbox[data]'",
+            "source": "%pip install -q \"labelbox[data]\"",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/annotation_import/dicom.ipynb b/examples/annotation_import/dicom.ipynb
index 34771d7c9..2b9640955 100644
--- a/examples/annotation_import/dicom.ipynb
+++ b/examples/annotation_import/dicom.ipynb
@@ -57,7 +57,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q 'labelbox[data]'",
+            "source": "%pip install -q \"labelbox[data]\"",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 6a4f6fd09..5aa68ef0c 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -77,7 +77,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q 'labelbox[data]'",
+            "source": "%pip install -q \"labelbox[data]\"",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
index c1d7188ad..f21c8436f 100644
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ b/examples/extras/classification_confusion_matrix.ipynb
@@ -44,7 +44,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q 'labelbox[data]' scikit-learn matplotlib ipywidgets",
+            "source": "%pip install -q \"labelbox[data]\" scikit-learn matplotlib ipywidgets",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/integrations/sam/meta_sam.ipynb b/examples/integrations/sam/meta_sam.ipynb
index 848bcd84e..96c1ef9a0 100644
--- a/examples/integrations/sam/meta_sam.ipynb
+++ b/examples/integrations/sam/meta_sam.ipynb
@@ -39,7 +39,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q 'git+https://github.com/facebookresearch/segment-anything.git'",
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/model_experiments/custom_metrics_demo.ipynb b/examples/model_experiments/custom_metrics_demo.ipynb
index a9ad4ead7..e6de146b9 100644
--- a/examples/model_experiments/custom_metrics_demo.ipynb
+++ b/examples/model_experiments/custom_metrics_demo.ipynb
@@ -52,7 +52,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q 'labelbox[data]'",
+            "source": "%pip install -q \"labelbox[data]\"",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/prediction_upload/geospatial_predictions.ipynb b/examples/prediction_upload/geospatial_predictions.ipynb
index 43d83f4c9..f0125193f 100644
--- a/examples/prediction_upload/geospatial_predictions.ipynb
+++ b/examples/prediction_upload/geospatial_predictions.ipynb
@@ -63,7 +63,7 @@
         },
         {
             "metadata": {},
-            "source": "%pip install -q 'labelbox[data]'",
+            "source": "%pip install -q \"labelbox[data]\"",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index f69472e60..9032c24c7 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -62,7 +62,7 @@
         },
         {
             "metadata": {},
-            "source": "# If you don't want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
+            "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
             "cell_type": "code",
             "outputs": [],
             "execution_count": null
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index 732678dbd..f5d49a8d1 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -75,9 +75,6 @@ def format_file(file_name):
 
 
 if __name__ == "__main__":
-    files = [
-        file
-        for file in glob.glob("**/*.ipynb", recursive=True)
-    ]
+    files = [file for file in glob.glob("**/*.ipynb", recursive=True)]
     with ProcessPoolExecutor(10) as pool:
         pool.map(format_file, files)
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 837a15f39..2f3d3f695 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -92,7 +92,7 @@ def make_links_dict(links: str):
     for link in links:
         if link.split("/")[-1] in IGNORE:
             continue
-        split_link = link .split("/")[0]
+        split_link = link.split("/")[0]
         link_dict[split_link].append(link)
     return link_dict
 
diff --git a/examples/template.ipynb b/examples/template.ipynb
index 709d82cff..5f42be7ee 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -1,18 +1,33 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        }
+    ]
+}
\ No newline at end of file

From 94c0951f357aec523feee04d72484767aeb5832c Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 22:25:58 -0500
Subject: [PATCH 41/80] removed template for now

---
 examples/template.ipynb | 33 ---------------------------------
 1 file changed, 33 deletions(-)
 delete mode 100644 examples/template.ipynb

diff --git a/examples/template.ipynb b/examples/template.ipynb
deleted file mode 100644
index 5f42be7ee..000000000
--- a/examples/template.ipynb
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        }
-    ]
-}
\ No newline at end of file

From 2c9ccdb8c64190f6f1b07d9e7841a7e80376635c Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Sun, 19 May 2024 23:15:14 -0500
Subject: [PATCH 42/80] made scripts quieter

---
 examples/pyproject.toml              | 4 ++--
 examples/scripts/format_notebooks.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/pyproject.toml b/examples/pyproject.toml
index 90a640780..5c746f6b2 100644
--- a/examples/pyproject.toml
+++ b/examples/pyproject.toml
@@ -28,8 +28,8 @@ dev-dependencies = [
 
 [tool.rye.scripts]
 clean = { chain = ["clean:databooks", "clean:black", "clean:format"] }
-"clean:databooks" = 'databooks meta --no-rm-exec . --yes'
-"clean:black" = 'black --line-length 80 .'
+"clean:databooks" = 'databooks meta --rm-outs . --yes'
+"clean:black" = 'black --line-length 80 . --quiet'
 "clean:format" = { call = "scripts.format_notebooks" }
 "create-readme" = { call = "scripts.generate_readme:main(True)" }
 "create-doc-readme" = { call = "scripts.generate_readme:main(False)" }
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index f5d49a8d1..aafa0d6de 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -71,10 +71,10 @@ def format_file(file_name):
 
     with open(file_name, "w") as file:
         file.write(json.dumps(data, indent=4))
-    print("Formatted", file_name)
 
 
 if __name__ == "__main__":
     files = [file for file in glob.glob("**/*.ipynb", recursive=True)]
     with ProcessPoolExecutor(10) as pool:
         pool.map(format_file, files)
+    print("Cleaning Completed! 🍻 ")

From e801da6fad8614848de967086ab57e044f986d4c Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Mon, 20 May 2024 09:42:22 -0500
Subject: [PATCH 43/80] made contributing md

---
 examples/CONTRIBUTING.md                      |  58 ++
 examples/README.md                            | 348 ++++----
 .../conversational_LLM.ipynb                  | 563 +++++++++---
 examples/basics/basics.ipynb                  | 225 +++--
 examples/basics/batches.ipynb                 | 805 +++++++++++-------
 examples/basics/user_management.ipynb         | 239 ++++--
 .../conversational_LLM_predictions.ipynb      | 710 ++++++++++++---
 .../project_configuration/project_setup.ipynb | 202 +++--
 examples/project_configuration/webhooks.ipynb | 295 +++++--
 examples/scripts/generate_readme.py           |  30 +-
 examples/template.ipynb                       | 149 ++++
 11 files changed, 2612 insertions(+), 1012 deletions(-)
 create mode 100644 examples/CONTRIBUTING.md
 create mode 100644 examples/template.ipynb

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
new file mode 100644
index 000000000..6114782ac
--- /dev/null
+++ b/examples/CONTRIBUTING.md
@@ -0,0 +1,58 @@
+# Contribution Guide
+
+Thank you for expressing your interest in contributing to our notebook examples! To ensure that your contribution aligns with our guidelines, please carefully review the following guide.
+
+## Table of Contents
+
+- [General Guidelines](#general-guidelines)
+- [Branches and Tags](#branches-and-tags)
+- [Release Steps](#release-steps)
+- [Github Workflows](#github-workflows)
+- [General Prerequisites](#general-prerequisites)
+- [Styling Tools](#styling-tools)
+- [General Notebook Requirements](#general-notebook-requirements)
+
+## General Guidelines
+
+* Ensure that any modified notebooks run when edited.
+* Ensure that you update any relevant headers and comments within the code block you may add or change.
+
+## Branches and Tags
+
+* All development happens in feature branches ideally prefixed by contributor's initials. For example `fs/feature_name`.
+* Approved PRs are merged to the `develop` branch.
+* All releases align to a git tag.
+
+## Release Steps
+
+Your contributions will be released as soon as it is approved and merged into the `develop` branch. Please consult the [Labelbox](https://docs.labelbox.com/docs/contacting-customer-support) team for if you need a more specific time frame of when your contributions may be released.
+
+## Github Workflows
+
+* Github Branch Workflow
+  * When you push to a branch that contains files inside the examples directory, it will automatically reformat your notebook to match our given style and provide appropriate headers. Once this workflow is completed it will commit back to your branch which then you can then pull.
+  * If your push contains new notebooks or modifies the names of notebooks the readme will be updated to reflect the change with updated links
+
+## General Prerequisites
+
+[Rye](https://rye-up.com/) may be installed before contributing to the repository as it is the tool used to style our example notebooks. This could be used to avoid the github styling workflow. This is also the packaging tool used for the main SDK. The pyproject used for the example notebooks is a virtual package and does not get published. Rye is used for the sack of consistency.
+
+## Styling Tools
+
+Rye is setup in this directory to use a customs script that will run the notebooks through our formatting tools and create readmes.
+
+* `rye sync` in the examples folder to install the correct dev dependencies.
+* `rye run clean` runs a series of formatting tools.
+* `rye run create-readme` creates a readme based off our notebooks.
+
+# General Notebook Requirements
+
+Review our [template notebook](https://github.com/Labelbox/labelbox-python/blob/develop/examples/template.ipynb) for general overview on how notebooks should be structure. This notebook and section just serves as a guide and exception can be made. Here are our general requirements:
+ 
+1. Notebooks should start with a top header below the Labelbox and link icons with the title of the notebook as a main header "#" and a overview of what the notebook shows
+2. Use "labelbox[data]" over labelbox for installs to ensure you have the correct dependencies.
+3. Imports and installs should come after the main header under a Setup section
+4. Labelbox and other platform with clients API keys should be specified under a single section.
+5. Subsections need a second level header "##".
+6. Subsections includes an overview of the section does any links to our docs.
+7. The last cell should be a clean up section to delete any labelbox objects created.
diff --git a/examples/README.md b/examples/README.md
index 7830be241..8322e2845 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,7 +5,7 @@
 
 - Run in google colab, view the notebooks on github, or clone the repo and run locally
 
-<h2>Model Experiments</h2>
+<h2>Basics</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -17,43 +17,49 @@
   </thead>
   <tbody>
     <tr>
-      <td>Custom Metrics Demo</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Basics</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Model Slices</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_slices.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Batches</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Custom Metrics Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Custom Embeddings</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Model Predictions to Project</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Projects</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-  </tbody>
-</table>
-
-<h2>Foundry</h2>
-
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: left;">
-      <th style="min-width: 400px;">Notebook</th>
-      <th>Github</th>
-      <th>Google Colab</th>
+    <tr>
+      <td>User Management</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-  </thead>
-  <tbody>
     <tr>
-      <td>Object Detection</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/object_detection.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/object_detection.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Data Row Metadata</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_row_metadata.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Export V1 to V2 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Ontologies</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Data Rows</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
@@ -72,17 +78,17 @@
     <tr>
       <td>Composite Mask Export</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/composite_mask_export.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Export Data</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_data.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_data.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Basics</h2>
+<h2>Project Configuration</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -94,54 +100,24 @@
   </thead>
   <tbody>
     <tr>
-      <td>Ontologies</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Export V1 to V2 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Data Rows</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Batches</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Projects</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/projects.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Data Row Metadata</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_row_metadata.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/data_row_metadata.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Project Setup</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_setup.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Webhooks</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/webhooks.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>User Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Queue Management</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/queue_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Prediction Upload</h2>
+<h2>Annotation Import</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -153,68 +129,69 @@
   </thead>
   <tbody>
     <tr>
-      <td>PDF Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Import Labeled Dataset Image</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>HTML Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Audio</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Video</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Image Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Text</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Text Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Tiled</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Geospatial Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational LLM Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>PDF</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Video Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational LLM Data Generation</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-  </tbody>
-</table>
-
-<h2>Extras</h2>
-
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: left;">
-      <th style="min-width: 400px;">Notebook</th>
-      <th>Github</th>
-      <th>Google Colab</th>
+    <tr>
+      <td>DICOM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/dicom.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-  </thead>
-  <tbody>
     <tr>
-      <td>Classification Confusion Matrix</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/classification_confusion_matrix.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Image</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>HTML</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Conversational LLM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Project Configuration</h2>
+<h2>Integrations</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -226,24 +203,29 @@
   </thead>
   <tbody>
     <tr>
-      <td>Project Setup</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_setup.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_setup.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Meta SAM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Webhooks</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/webhooks.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/webhooks.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Meta SAM Video</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Queue Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/queue_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/queue_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Langchain</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/langchain/langchain.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Huggingface Custom Embeddings</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Integrations</h2>
+<h2>Model Experiments</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -255,29 +237,29 @@
   </thead>
   <tbody>
     <tr>
-      <td>Huggingface Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Model Slices</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Langchain</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/langchain/langchain.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/langchain/langchain.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Custom Metrics Basics</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Meta SAM Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Custom Metrics Demo</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Meta SAM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Model Predictions to Project</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
 
-<h2>Annotation Import</h2>
+<h2>Prediction Upload</h2>
 
 <table border="1" class="dataframe">
   <thead>
@@ -289,64 +271,82 @@
   </thead>
   <tbody>
     <tr>
-      <td>Conversational LLM Data Generation</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Text Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Text</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/text.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>HTML Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Audio</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational LLM Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Geospatial Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Import Labeled Dataset Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>PDF Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>PDF</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/pdf.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Video Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Image Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>DICOM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/dicom.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/dicom.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+  </tbody>
+</table>
+
+<h2>Foundry</h2>
+
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: left;">
+      <th style="min-width: 400px;">Notebook</th>
+      <th>Github</th>
+      <th>Google Colab</th>
     </tr>
+  </thead>
+  <tbody>
     <tr>
-      <td>Conversational LLM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Object Detection</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/object_detection.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>HTML</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/html.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+  </tbody>
+</table>
+
+<h2>Extras</h2>
+
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: left;">
+      <th style="min-width: 400px;">Notebook</th>
+      <th>Github</th>
+      <th>Google Colab</th>
     </tr>
+  </thead>
+  <tbody>
     <tr>
-      <td>Tiled</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Classification Confusion Matrix</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index d69df8f46..2516947e3 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,145 +15,279 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
                 "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install -q \"labelbox[data]\""
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Set up"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "import labelbox.types as lb_types\n",
+                "import uuid"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Replace with your API key"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "API_KEY = \"\"\n",
+                "client = lb.Client(api_key=API_KEY)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Entity "
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "ner_annotation = lb_types.ObjectAnnotation(\n",
+                "    name=\"ner\",\n",
+                "    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n",
+                ")\n",
+                "\n",
+                "ner_annotation_ndjson = {\n",
+                "    \"name\": \"ner\",\n",
+                "    \"location\": {\n",
+                "        \"start\": 0,\n",
+                "        \"end\": 8\n",
+                "    },\n",
+                "    \"messageId\": \"message-1\",\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "radio_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"Choose the best response\",\n",
+                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "        name=\"Response B\")),\n",
+                ")\n",
+                "\n",
+                "radio_annotation_ndjson = {\n",
+                "    \"name\": \"Choose the best response\",\n",
+                "    \"answer\": {\n",
+                "        \"name\": \"Response B\"\n",
+                "    },\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "text_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"Provide a reason for your choice\",\n",
+                "    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n",
+                ")\n",
+                "\n",
+                "text_annotation_ndjson = {\n",
+                "    \"name\": \"Provide a reason for your choice\",\n",
+                "    \"answer\": \"This is the more concise answer\",\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "checklist_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"checklist_convo\",  # must match your ontology feature\"s name\n",
+                "    value=lb_types.Checklist(answer=[\n",
+                "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
+                "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n",
+                "    ]),\n",
+                "    message_id=\"message-1\",  # Message specific annotation\n",
+                ")\n",
+                "\n",
+                "checklist_annotation_ndjson = {\n",
+                "    \"name\": \"checklist_convo\",\n",
+                "    \"answers\": [\n",
+                "        {\n",
+                "            \"name\": \"first_checklist_answer\"\n",
+                "        },\n",
+                "        {\n",
+                "            \"name\": \"second_checklist_answer\"\n",
+                "        },\n",
+                "    ],\n",
+                "    \"messageId\": \"message-1\",\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Message based\n",
+                "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"nested_checklist_question\",\n",
+                "    message_id=\"message-1\",\n",
+                "    value=lb_types.Checklist(answer=[\n",
+                "        lb_types.ClassificationAnswer(\n",
+                "            name=\"first_checklist_answer\",\n",
+                "            classifications=[\n",
+                "                lb_types.ClassificationAnnotation(\n",
+                "                    name=\"sub_checklist_question\",\n",
+                "                    value=lb_types.Checklist(answer=[\n",
+                "                        lb_types.ClassificationAnswer(\n",
+                "                            name=\"first_sub_checklist_answer\")\n",
+                "                    ]),\n",
+                "                )\n",
+                "            ],\n",
+                "        )\n",
+                "    ]),\n",
+                ")\n",
+                "# Message based\n",
+                "nested_checklist_annotation_ndjson = {\n",
+                "    \"name\":\n",
+                "        \"nested_checklist_question\",\n",
+                "    \"messageId\":\n",
+                "        \"message-1\",\n",
+                "    \"answer\": [{\n",
+                "        \"name\":\n",
+                "            \"first_checklist_answer\",\n",
+                "        \"classifications\": [{\n",
+                "            \"name\": \"sub_checklist_question\",\n",
+                "            \"answer\": {\n",
+                "                \"name\": \"first_sub_checklist_answer\",\n",
+                "            },\n",
+                "        }],\n",
+                "    }],\n",
+                "}\n",
+                "# Global\n",
+                "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"nested_radio_question\",\n",
+                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "        name=\"first_radio_answer\",\n",
+                "        classifications=[\n",
+                "            lb_types.ClassificationAnnotation(\n",
+                "                name=\"sub_radio_question\",\n",
+                "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "                    name=\"first_sub_radio_answer\")),\n",
+                "            )\n",
+                "        ],\n",
+                "    )),\n",
+                ")\n",
+                "# Global\n",
+                "nested_radio_annotation_ndjson = {\n",
+                "    \"name\": \"nested_radio_question\",\n",
+                "    \"answer\": {\n",
+                "        \"name\":\n",
+                "            \"first_radio_answer\",\n",
+                "        \"classifications\": [{\n",
+                "            \"name\": \"sub_radio_question\",\n",
+                "            \"answer\": {\n",
+                "                \"name\": \"first_sub_radio_answer\"\n",
+                "            },\n",
+                "        }],\n",
+                "    },\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
@@ -170,141 +301,357 @@
                 "  }\n",
                 "]\n",
                 "```\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### Example of row_data with model outputs"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "pairwise_shopping_2 = \"\"\"\n",
+                " {\n",
+                "  \"type\": \"application/vnd.labelbox.conversational\",\n",
+                "  \"version\": 1,\n",
+                "  \"messages\": [\n",
+                "    {\n",
+                "      \"messageId\": \"message-0\",\n",
+                "      \"timestampUsec\": 1530718491,\n",
+                "      \"content\": \"Hi! How can I help?\",\n",
+                "      \"user\": {\n",
+                "        \"userId\": \"Bot 002\",\n",
+                "        \"name\": \"Bot\"\n",
+                "      },\n",
+                "      \"align\": \"left\",\n",
+                "      \"canLabel\": false\n",
+                "    },\n",
+                "    {\n",
+                "      \"messageId\": \"message-1\",\n",
+                "      \"timestampUsec\": 1530718503,\n",
+                "      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n",
+                "      \"user\": {\n",
+                "        \"userId\": \"User 00686\",\n",
+                "        \"name\": \"User\"\n",
+                "      },\n",
+                "      \"align\": \"right\",\n",
+                "      \"canLabel\": true\n",
+                "    }\n",
+                "\n",
+                "  ],\n",
+                "  \"modelOutputs\": [\n",
+                "    {\n",
+                "      \"title\": \"Response A\",\n",
+                "      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n",
+                "      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n",
+                "    },\n",
+                "    {\n",
+                "      \"title\": \"Response B\",\n",
+                "      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n",
+                "      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n",
+                "    }\n",
+                "  ]\n",
+                "}\n",
+                "\"\"\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "global_key = \"pairwise_shooping_asset\"\n",
+                "\n",
+                "# Upload data rows\n",
+                "convo_data = {\n",
+                "    \"row_data\":\n",
+                "        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n",
+                "    \"global_key\":\n",
+                "        global_key,\n",
+                "}\n",
+                "\n",
+                "# Create a dataset\n",
+                "dataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n",
+                "# Create a datarows\n",
+                "task = dataset.create_data_rows([convo_data])\n",
+                "task.wait_till_done()\n",
+                "print(\"Errors:\", task.errors)\n",
+                "print(\"Failed data rows:\", task.failed_data_rows)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 2: Create/select an Ontology"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Create an ontology with relevant classifications\n",
+                "\n",
+                "ontology_builder = lb.OntologyBuilder(\n",
+                "    tools=[\n",
+                "        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n",
+                "    ],\n",
+                "    classifications=[\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.RADIO,\n",
+                "            scope=lb.Classification.Scope.GLOBAL,\n",
+                "            name=\"Choose the best response\",\n",
+                "            options=[\n",
+                "                lb.Option(value=\"Response A\"),\n",
+                "                lb.Option(value=\"Response B\"),\n",
+                "                lb.Option(value=\"Tie\"),\n",
+                "            ],\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.TEXT,\n",
+                "            name=\"Provide a reason for your choice\",\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.CHECKLIST,\n",
+                "            scope=lb.Classification.Scope.INDEX,\n",
+                "            name=\"checklist_convo\",\n",
+                "            options=[\n",
+                "                lb.Option(value=\"first_checklist_answer\"),\n",
+                "                lb.Option(value=\"second_checklist_answer\"),\n",
+                "            ],\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.CHECKLIST,\n",
+                "            name=\"nested_checklist_question\",\n",
+                "            scope=lb.Classification.Scope.INDEX,\n",
+                "            options=[\n",
+                "                lb.Option(\n",
+                "                    \"first_checklist_answer\",\n",
+                "                    options=[\n",
+                "                        lb.Classification(\n",
+                "                            class_type=lb.Classification.Type.CHECKLIST,\n",
+                "                            name=\"sub_checklist_question\",\n",
+                "                            options=[lb.Option(\"first_sub_checklist_answer\")],\n",
+                "                        )\n",
+                "                    ],\n",
+                "                )\n",
+                "            ],\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.RADIO,\n",
+                "            name=\"nested_radio_question\",\n",
+                "            scope=lb.Classification.Scope.GLOBAL,\n",
+                "            options=[\n",
+                "                lb.Option(\n",
+                "                    \"first_radio_answer\",\n",
+                "                    options=[\n",
+                "                        lb.Classification(\n",
+                "                            class_type=lb.Classification.Type.RADIO,\n",
+                "                            name=\"sub_radio_question\",\n",
+                "                            options=[lb.Option(\"first_sub_radio_answer\")],\n",
+                "                        )\n",
+                "                    ],\n",
+                "                )\n",
+                "            ],\n",
+                "        ),\n",
+                "    ],\n",
+                ")\n",
+                "\n",
+                "ontology = client.create_ontology(\n",
+                "    \"Pairwise comparison ontology\",\n",
+                "    ontology_builder.asdict(),\n",
+                "    media_type=lb.MediaType.Conversational,\n",
+                ")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 3: Create a labeling project"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Create Labelbox project\n",
+                "project = client.create_project(\n",
+                "    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n",
+                "    media_type=lb.MediaType.Conversational,\n",
+                ")\n",
+                "\n",
+                "# Setup your ontology\n",
+                "project.setup_editor(\n",
+                "    ontology)  # Connect your ontology and editor to your project"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Create a batch to send to your project\n",
+                "batch = project.create_batch(\n",
+                "    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n",
+                "    global_keys=[\n",
+                "        global_key\n",
+                "    ],  # Paginated collection of data row objects, list of data row ids or global keys\n",
+                "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
+                ")\n",
+                "\n",
+                "print(\"Batch: \", batch)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 5: Create the annotations payload"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "Python annotation"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "label = []\n",
+                "label.append(\n",
+                "    lb_types.Label(\n",
+                "        data={\"global_key\": global_key},\n",
+                "        annotations=[\n",
+                "            ner_annotation,\n",
+                "            text_annotation,\n",
+                "            checklist_annotation,\n",
+                "            radio_annotation,\n",
+                "            nested_radio_annotation,\n",
+                "            nested_checklist_annotation,\n",
+                "        ],\n",
+                "    ))"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "NDJSON annotation"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "label_ndjson = []\n",
+                "for annotations in [\n",
+                "        ner_annotation_ndjson,\n",
+                "        text_annotation_ndjson,\n",
+                "        checklist_annotation_ndjson,\n",
+                "        radio_annotation_ndjson,\n",
+                "        nested_checklist_annotation_ndjson,\n",
+                "        nested_radio_annotation_ndjson,\n",
+                "]:\n",
+                "    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n",
+                "    label_ndjson.append(annotations)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 6: Upload annotations to a project as pre-labels or complete labels "
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Model Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "upload_job = lb.MALPredictionImport.create_from_objects(\n",
+                "    client=client,\n",
+                "    project_id=project.uid,\n",
+                "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
+                "    predictions=label,\n",
+                ")\n",
+                "\n",
+                "upload_job.wait_until_done()\n",
+                "print(\"Errors:\", upload_job.errors)\n",
+                "print(\"Status of uploads: \", upload_job.statuses)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "## Label Import"
-            ],
-            "cell_type": "markdown"
+                "### Label Import"
+            ]
         },
         {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "upload_job = lb.LabelImport.create_from_objects(\n",
+                "    client=client,\n",
+                "    project_id=project.uid,\n",
+                "    name=\"label_import_job\" + str(uuid.uuid4()),\n",
+                "    labels=label,\n",
+                ")\n",
+                "\n",
+                "upload_job.wait_until_done()\n",
+                "print(\"Errors:\", upload_job.errors)\n",
+                "print(\"Status of uploads: \", upload_job.statuses)"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "env",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "name": "python",
+            "version": "3.10.13"
         }
-    ]
-}
\ No newline at end of file
+    },
+    "nbformat": 4,
+    "nbformat_minor": 0
+}
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index fb42578a2..94559690e 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,170 +15,262 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Basic project/datasets overview\n",
                 "\n",
                 "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
                 "\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install labelbox",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install \"labelbox[data]\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport random",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
+                "## API key and client\n",
+                "Provide a valid API key below in order to properly connect to the Labelbox Client."
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Add your API key\n",
+                "API_KEY = None\n",
+                "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
+                "client = lb.Client(api_key=API_KEY)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# For the purpose of this demo get a single project/dataset id from your organization\n",
+                "\n",
+                "# Get a single Project id\n",
+                "# get_projects returns a PaginatedCollection object, which is iterable.\n",
+                "project = next(client.get_projects())\n",
+                "project_id = project.uid\n",
+                "project_name = project.name\n",
+                "print(\"Project ID: \", project_id)\n",
+                "print(\"Project Name:\", project_name)\n",
+                "\n",
+                "print(\"-\" * 40)\n",
+                "\n",
+                "# Get a single dataset id\n",
+                "# get_datasets returns a PaginatedCollection object, which is iterable.\n",
+                "dataset = next(client.get_datasets())\n",
+                "dataset_id = dataset.uid\n",
+                "dataset_name = dataset.name\n",
+                "print(\"Dataset ID: \", dataset_id)\n",
+                "print(\"Dataset Name:\", dataset_name)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Fetch the project and dataset by using the IDs fetched in the previous cell\n",
+                "project = client.get_project(project_id)\n",
+                "dataset = client.get_dataset(dataset_id)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "print(\"Project: \", project)\n",
+                "print(\"Dataset: \", dataset)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Fields\n",
                 "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
                 "* These fields are attributes of the object"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "print(project.name)\nprint(dataset.name)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "print(project.name)\n",
+                "print(dataset.name)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "project.update(description=\"new description field\")\nprint(project.description)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "project.update(description=\"new description field\")\n",
+                "print(project.description)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Pagination\n",
                 "* Queries that return a list of database objects are return as a PaginatedCollection\n",
                 "* Limits the data that is being returned for better performance"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "labels_paginated_collection = project.labels()\n",
+                "print(\"Type of collection: \", type(labels_paginated_collection))\n",
+                "\n",
+                "# A paginated collection can be parsed by using list()\n",
+                "# list(paginated...) should be avoided for queries that could return more than a dozen results\n",
+                "print(\"Number of labels :\", len(list(labels_paginated_collection)))"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n",
+                "# Iterate over the paginated collection\n",
+                "try:\n",
+                "    single_label = next(project.labels())\n",
+                "    print(single_label)\n",
+                "except StopIteration:\n",
+                "    print(\"Project has no labels !\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Query parameters\n",
                 "* Query with the following conventions:\n",
                 "    * `DbObject.Field`"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n",
+                "\n",
+                "projects = client.get_projects(\n",
+                "    where=((lb.Project.name == project_name) &\n",
+                "           (lb.Project.description == \"new description field\")))\n",
+                "\n",
+                "# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n",
+                "# So even if there is one element returned it is in a paginatedCollection.\n",
+                "print(projects)\n",
+                "print(next(projects, None))\n",
+                "print(next(projects, None))\n",
+                "print(next(projects, None))\n",
+                "# We can see there is only one."
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Querying Limitations\n",
                 "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
                 "* The below query is not valid since get_project returns a project not a dataset\n",
                 ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Relationships between projects and batches/datasets\n",
                 "\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "sample_project_batches = project.batches()\n",
+                "\n",
+                "list(sample_project_batches)\n",
+                "\n",
+                "for b in sample_project_batches:\n",
+                "    print(f\" Name of project : {b.project().name}\")\n",
+                "    print(f\" Name of batches in project: {b.name}\")"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "name": "python",
+            "version": "3.12.3"
         }
-    ]
-}
\ No newline at end of file
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index 7645f498e..7e11f090a 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -1,301 +1,506 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Batches\n",
-                "https://docs.labelbox.com/docs/batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* A batch is collection of data rows.\n",
-                "* A data row cannot be part of more than one batch in a given project.\n",
-                "* Batches work for all data types, but there can only be one data type per project.\n",
-                "* Batches can not be shared between projects.\n",
-                "* Batches may have data rows from multiple datasets.\n",
-                "* Currently, only benchmarks quality settings is supported in batch projects\n",
-                "* You can set the priority for each batch."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\" -q",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create a dataset and data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup batch project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Select all data rows from the dataset\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Select a random sample\n",
-                "This method is useful if you have large datasets and only want to work with a handful of data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "sample = random.sample(global_keys, 4)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a batch\n",
-                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create multiple batches\n",
-                "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
-                "\n",
-                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
-                "\n",
-                "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
-                "\n",
-                "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create batches from a dataset\n",
-                "\n",
-                "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
-                "\n",
-                "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Manage Batches\n",
-                "Note: You can view your batch data through the **Data Rows** tab."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Export Batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create and Attach Ontology to Project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export from Project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Archive a batch"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up \n",
-                "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
-}
\ No newline at end of file
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<td>\n",
+    "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+    "</td>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<td>\n",
+    "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
+    "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+    "</td>\n",
+    "\n",
+    "<td>\n",
+    "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
+    "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+    "</td>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Batches\n",
+    "https://docs.labelbox.com/docs/batches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* A batch is collection of data rows.\n",
+    "* A data row cannot be part of more than one batch in a given project.\n",
+    "* Batches work for all data types, but there can only be one data type per project.\n",
+    "* Batches can not be shared between projects.\n",
+    "* Batches may have data rows from multiple datasets.\n",
+    "* Currently, only benchmarks quality settings is supported in batch projects\n",
+    "* You can set the priority for each batch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q \"labelbox[data]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import labelbox as lb\n",
+    "import random\n",
+    "import uuid\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API key and client\n",
+    "Provide a valid API key below in order to properly connect to the Labelbox Client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add your API key\n",
+    "API_KEY = None\n",
+    "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
+    "client = lb.Client(api_key=API_KEY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a dataset and data rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4734d69f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a dataset\n",
+    "dataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n",
+    "\n",
+    "uploads = []\n",
+    "# Generate data rows\n",
+    "for i in range(1, 9):\n",
+    "    uploads.append(\n",
+    "        {\n",
+    "            \"row_data\": f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
+    "            \"global_key\": \"TEST-ID-%id\" % uuid.uuid1(),\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "data_rows = dataset.create_data_rows(uploads)\n",
+    "data_rows.wait_till_done()\n",
+    "print(\"ERRORS: \", data_rows.errors)\n",
+    "print(\"RESULT URL: \", data_rows.result_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "299af781",
+   "metadata": {},
+   "source": [
+    "## Setup batch project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2adbc23d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = client.create_project(\n",
+    "    name=\"Demo-Batches-Project\", media_type=lb.MediaType.Image\n",
+    ")\n",
+    "print(\"Project Name: \", project.name, \"Project ID: \", project.uid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff7ae419",
+   "metadata": {},
+   "source": [
+    "## Create batches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fff65df2",
+   "metadata": {},
+   "source": [
+    "### Select all data rows from the dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "041cb1c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.enable_experimental = True\n",
+    "\n",
+    "export_task = dataset.export()\n",
+    "export_task.wait_till_done()\n",
+    "\n",
+    "data_rows = []\n",
+    "\n",
+    "\n",
+    "def json_stream_handler(output: lb.JsonConverterOutput):\n",
+    "    data_row = json.loads(output.json_str)\n",
+    "    data_rows.append(data_row)\n",
+    "\n",
+    "\n",
+    "if export_task.has_errors():\n",
+    "    export_task.get_stream(\n",
+    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.ERRORS\n",
+    "    ).start(stream_handler=lambda error: print(error))\n",
+    "\n",
+    "if export_task.has_result():\n",
+    "    export_json = export_task.get_stream(\n",
+    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT\n",
+    "    ).start(stream_handler=json_stream_handler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5286f399",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\n",
+    "print(\"Number of global keys:\", len(global_keys))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bb64625",
+   "metadata": {},
+   "source": [
+    "### Select a random sample\n",
+    "This method is useful if you have large datasets and only want to work with a handful of data rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc279d64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = random.sample(global_keys, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bf836b5",
+   "metadata": {},
+   "source": [
+    "### Create a batch\n",
+    "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab4cf24b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = project.create_batch(\n",
+    "    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n",
+    "    global_keys=sample,  # A list of data rows or data row ids\n",
+    "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
+    ")\n",
+    "# number of data rows in the batch\n",
+    "print(\"Number of data rows in batch: \", batch.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba61918d",
+   "metadata": {},
+   "source": [
+    "### Create multiple batches\n",
+    "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
+    "\n",
+    "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
+    "\n",
+    "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
+    "\n",
+    "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3bb23fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, we must create a second project so that we can re-use the data rows we already created.\n",
+    "second_project = client.create_project(\n",
+    "    name=\"Second-Demo-Batches-Project\", media_type=lb.MediaType.Image\n",
+    ")\n",
+    "print(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n",
+    "\n",
+    "# Then, use the method that will create multiple batches if necessary.\n",
+    "task = second_project.create_batches(\n",
+    "    name_prefix=\"demo-create-batches-\", global_keys=global_keys, priority=5\n",
+    ")\n",
+    "\n",
+    "print(\"Errors: \", task.errors())\n",
+    "print(\"Result: \", task.result())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54f2fc1f",
+   "metadata": {},
+   "source": [
+    "### Create batches from a dataset\n",
+    "\n",
+    "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
+    "\n",
+    "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "affa9fdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, we must create a third project so that we can re-use the data rows we already created.\n",
+    "third_project = client.create_project(\n",
+    "    name=\"Third-Demo-Batches-Project\", media_type=lb.MediaType.Image\n",
+    ")\n",
+    "print(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n",
+    "\n",
+    "# Then, use the method to create batches from a dataset.\n",
+    "task = third_project.create_batches_from_dataset(\n",
+    "    name_prefix=\"demo-batches-from-dataset-\", dataset_id=dataset.uid, priority=5\n",
+    ")\n",
+    "\n",
+    "print(\"Errors: \", task.errors())\n",
+    "print(\"Result: \", task.result())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95df8a59",
+   "metadata": {},
+   "source": [
+    "## Manage Batches\n",
+    "Note: You can view your batch data through the **Data Rows** tab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00b72b6d",
+   "metadata": {},
+   "source": [
+    "### Export Batches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e8a455f",
+   "metadata": {},
+   "source": [
+    "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a178c178",
+   "metadata": {},
+   "source": [
+    "#### Create and Attach Ontology to Project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "889d6798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classification_features = [\n",
+    "    lb.Classification(\n",
+    "        class_type=lb.Classification.Type.CHECKLIST,\n",
+    "        name=\"Quality Issues\",\n",
+    "        options=[\n",
+    "            lb.Option(value=\"blurry\", label=\"Blurry\"),\n",
+    "            lb.Option(value=\"distorted\", label=\"Distorted\"),\n",
+    "        ],\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "ontology_builder = lb.OntologyBuilder(\n",
+    "    tools=[], classifications=classification_features\n",
+    ")\n",
+    "\n",
+    "ontology = client.create_ontology(\n",
+    "    \"Ontology from new features\",\n",
+    "    ontology_builder.asdict(),\n",
+    "    media_type=lb.MediaType.Image,\n",
+    ")\n",
+    "\n",
+    "project.setup_editor(ontology)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c39e4efa",
+   "metadata": {},
+   "source": [
+    "#### Export from Project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1844ddcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.enable_experimental = True\n",
+    "\n",
+    "export_params = {\n",
+    "    \"attachments\": True,\n",
+    "    \"metadata_fields\": True,\n",
+    "    \"data_row_details\": True,\n",
+    "    \"project_details\": True,\n",
+    "    \"performance_details\": True,\n",
+    "    \"batch_ids\": [\n",
+    "        batch.uid\n",
+    "    ],  # Include batch ids if you only want to export specific batches, otherwise,\n",
+    "    # you can export all the data without using this parameter\n",
+    "}\n",
+    "filters = {}\n",
+    "\n",
+    "# A task is returned, this provides additional information about the status of your task, such as\n",
+    "# any errors encountered\n",
+    "export_task = project.export(params=export_params, filters=filters)\n",
+    "export_task.wait_till_done()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00431979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_rows = []\n",
+    "\n",
+    "\n",
+    "def json_stream_handler(output: lb.JsonConverterOutput):\n",
+    "    data_row = json.loads(output.json_str)\n",
+    "    data_rows.append(data_row)\n",
+    "\n",
+    "\n",
+    "if export_task.has_errors():\n",
+    "    export_task.get_stream(\n",
+    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.ERRORS\n",
+    "    ).start(stream_handler=lambda error: print(error))\n",
+    "\n",
+    "if export_task.has_result():\n",
+    "    export_json = export_task.get_stream(\n",
+    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT\n",
+    "    ).start(stream_handler=json_stream_handler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Export the data row iDs\n",
+    "data_rows = [dr for dr in data_rows]\n",
+    "print(\"Data rows in batch: \", data_rows)\n",
+    "\n",
+    "## List the batches in your project\n",
+    "for batch in project.batches():\n",
+    "    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Archive a batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Archiving a batch removes all queued data rows in the batch from the project\n",
+    "batch.remove_queued_data_rows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up \n",
+    "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delete Batch\n",
+    "# batch.delete()\n",
+    "\n",
+    "# Delete Project\n",
+    "# project.delete()\n",
+    "\n",
+    "# Delete DataSet\n",
+    "# dataset.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index 40d95cfee..28d67bab9 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,18 +15,19 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# User Management\n",
@@ -40,209 +38,288 @@
                 "    * assign users to projects\n",
                 "        * set / update / revoke project role\n",
                 "    * delete users from org"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install labelbox",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "f345bd18",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install \"labelbox[data]\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport os",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "4fd5adda",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "import os"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "6325834e",
             "metadata": {},
             "source": [
                 "* You have to specifically enable experimental features to use this functionality. Notice the \n",
                 "`enable_experimental = True`\n",
                 "    * enables users to send invites and checking the number of seats available via the sdk"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "72511d8c",
             "metadata": {},
             "source": [
                 "# API Key and Client\n",
                 "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "f9697fac",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Add your api key\n",
+                "API_KEY = None\n",
+                "client = lb.Client(api_key=API_KEY, enable_experimental=True)\n",
+                "organization = client.get_organization()"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "7c4d302f",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Please provide a dummy email here:\n",
+                "# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\n",
+                "DUMMY_EMAIL = \"SET THIS\"\n",
+                "# This should be set to an account that you wan't to change the permissions for.\n",
+                "# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\n",
+                "DUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\""
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "47f63fcf",
             "metadata": {},
             "source": [
                 "### Roles\n",
                 "* When inviting a new user to an organization, there are various roles to select from.\n",
                 "* All available roles to your org can be accessed via `client.get_roles()`"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "45b2cb42",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "roles = client.get_roles()\n",
+                "for name, role in roles.items():\n",
+                "    print(role.name, \":\", role.uid)"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "b532d716",
             "metadata": {},
             "source": [
                 "* Above we printed out all of the roles available to the current org.\n",
                 "* Notice the `NONE`. That is for project level roles"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "b664c049",
             "metadata": {},
             "source": [
                 "### Create\n",
                 "* Users are created by sending an invite\n",
                 "* An email will be sent to them and they will be asked to join your organization"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "c235a7ad",
             "metadata": {},
             "source": [
                 "#### Organization Level Permissions\n",
                 "* Invite a new labeler with labeling permissions on all projects"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "dea95f3d",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# First make sure that you have enough seats:\n",
+                "organization.invite_limit()"
+            ]
         },
         {
-            "metadata": {},
-            "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "0ad0c884",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])"
+            ]
         },
         {
-            "metadata": {},
-            "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "3d8e18b3",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "print(invite.created_at)\n",
+                "print(invite.organization_role_name)\n",
+                "print(invite.email)"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "6f5e5a21",
             "metadata": {},
             "source": [
                 "#### Project Level Permissions\n",
                 "* Invite a new labeler with labeling permissions specific to a set of projects\n",
                 "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "26073bce",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "project = client.create_project(name=\"test_user_management\",\n",
+                "                                media_type=lb.MediaType.Image)\n",
+                "project_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\n",
+                "invite = organization.invite_user(DUMMY_EMAIL,\n",
+                "                                  roles[\"NONE\"],\n",
+                "                                  project_roles=[project_role])"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Read\n",
                 "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
                 "* You are able to query for members once they have joined."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "users = list(organization.users())\nprint(users[0])",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "users = list(organization.users())\n",
+                "print(users[0])"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Update\n",
                 "* There is no update on invites. Instead you must delete and resend them\n",
                 "* You can update User roles"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n",
+                "\n",
+                "# Give the user organization level permissions\n",
+                "user.update_org_role(roles[\"LABELER\"])\n",
+                "print(user.org_role())\n",
+                "# Restore project level permissions\n",
+                "user.update_org_role(roles[\"NONE\"])\n",
+                "print(user.org_role())\n",
+                "# Make the user a labeler for the current project\n",
+                "user.upsert_project_role(project, roles[\"LABELER\"])\n",
+                "print(user.org_role())"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\n",
+                "user.remove_from_project(project)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Delete"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Invites can only be deleted from the ui at this time. \n",
                 "* Deleting invites can be done in the members tab of the web app."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Delete the User\n",
                 "* Make sure you want to remove the user from the org:\n",
                 "* `>>> organization.remove_user(user)`"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Cleanup\n",
                 "* We created an extra project. Let's delete it"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "project.delete()",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "project.delete()"
+            ]
         }
-    ]
-}
\ No newline at end of file
+    ],
+    "metadata": {
+        "language_info": {
+            "name": "python"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index a849ba4a5..8132fe656 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,146 +15,304 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# LLM pairwise comparison with Conversational text using Model\n",
                 "\n",
                 "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\" -q",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install -q \"labelbox[data]\""
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Set up"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "import labelbox.types as lb_types\n",
+                "import uuid"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Replace with your API Key"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "API_KEY = \"\"\n",
+                "client = lb.Client(api_key=API_KEY)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Entity"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "ner_prediction = lb_types.ObjectAnnotation(\n",
+                "    name=\"ner\",\n",
+                "    confidence=0.5,\n",
+                "    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n",
+                ")\n",
+                "\n",
+                "ner_prediction_ndjson = {\n",
+                "    \"name\": \"ner\",\n",
+                "    \"confidence\": 0.5,\n",
+                "    \"location\": {\n",
+                "        \"start\": 0,\n",
+                "        \"end\": 8\n",
+                "    },\n",
+                "    \"messageId\": \"message-1\",\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "radio_prediction = lb_types.ClassificationAnnotation(\n",
+                "    name=\"Choose the best response\",\n",
+                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n",
+                "                                                              confidence=0.5)),\n",
+                ")\n",
+                "\n",
+                "radio_prediction_ndjson = {\n",
+                "    \"name\": \"Choose the best response\",\n",
+                "    \"answer\": {\n",
+                "        \"name\": \"Response B\",\n",
+                "        \"confidence\": 0.5\n",
+                "    },\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "text_prediction = lb_types.ClassificationAnnotation(\n",
+                "    name=\"Provide a reason for your choice\",\n",
+                "    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n",
+                "                        confidence=0.5),\n",
+                ")\n",
+                "\n",
+                "text_prediction_ndjson = {\n",
+                "    \"name\": \"Provide a reason for your choice\",\n",
+                "    \"answer\": \"This is the more concise answer\",\n",
+                "    \"confidence\": 0.5,\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "checklist_prediction = lb_types.ClassificationAnnotation(\n",
+                "    name=\"checklist_convo\",  # must match your ontology feature\"s name\n",
+                "    value=lb_types.Checklist(answer=[\n",
+                "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n",
+                "                                      confidence=0.5),\n",
+                "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n",
+                "                                      confidence=0.5),\n",
+                "    ]),\n",
+                "    message_id=\"message-1\",  # Message specific annotation\n",
+                ")\n",
+                "\n",
+                "checklist_prediction_ndjson = {\n",
+                "    \"name\": \"checklist_convo\",\n",
+                "    \"answers\": [\n",
+                "        {\n",
+                "            \"name\": \"first_checklist_answer\",\n",
+                "            \"confidence\": 0.5\n",
+                "        },\n",
+                "        {\n",
+                "            \"name\": \"second_checklist_answer\",\n",
+                "            \"confidence\": 0.5\n",
+                "        },\n",
+                "    ],\n",
+                "    \"messageId\": \"message-1\",\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Message based\n",
+                "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
+                "    name=\"nested_checklist_question\",\n",
+                "    message_id=\"message-1\",\n",
+                "    value=lb_types.Checklist(answer=[\n",
+                "        lb_types.ClassificationAnswer(\n",
+                "            name=\"first_checklist_answer\",\n",
+                "            confidence=0.5,  # Confidence scores should be added to the answer\n",
+                "            classifications=[\n",
+                "                lb_types.ClassificationAnnotation(\n",
+                "                    name=\"sub_checklist_question\",\n",
+                "                    value=lb_types.Checklist(answer=[\n",
+                "                        lb_types.ClassificationAnswer(\n",
+                "                            name=\"first_sub_checklist_answer\",\n",
+                "                            confidence=\n",
+                "                            0.5,  # Confidence scores should be added to the answer\n",
+                "                        )\n",
+                "                    ]),\n",
+                "                )\n",
+                "            ],\n",
+                "        )\n",
+                "    ]),\n",
+                ")\n",
+                "# Message based\n",
+                "nested_checklist_prediction_ndjson = {\n",
+                "    \"name\":\n",
+                "        \"nested_checklist_question\",\n",
+                "    \"messageId\":\n",
+                "        \"message-1\",\n",
+                "    \"answer\": [{\n",
+                "        \"name\":\n",
+                "            \"first_checklist_answer\",\n",
+                "        \"confidence\":\n",
+                "            0.5,  # Confidence scores should be added to the answer\n",
+                "        \"classifications\": [{\n",
+                "            \"name\": \"sub_checklist_question\",\n",
+                "            \"answer\": {\n",
+                "                \"name\": \"first_sub_checklist_answer\",\n",
+                "                \"confidence\":\n",
+                "                    0.5,  # Confidence scores should be added to the answer\n",
+                "            },\n",
+                "        }],\n",
+                "    }],\n",
+                "}\n",
+                "# Global\n",
+                "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
+                "    name=\"nested_radio_question\",\n",
+                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "        name=\"first_radio_answer\",\n",
+                "        confidence=0.5,  # Confidence scores should be added to the answer\n",
+                "        classifications=[\n",
+                "            lb_types.ClassificationAnnotation(\n",
+                "                name=\"sub_radio_question\",\n",
+                "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "                    name=\"first_sub_radio_answer\",\n",
+                "                    confidence=\n",
+                "                    0.5,  # Confidence scores should be added to the answer\n",
+                "                )),\n",
+                "            )\n",
+                "        ],\n",
+                "    )),\n",
+                ")\n",
+                "# Global\n",
+                "nested_radio_prediction_ndjson = {\n",
+                "    \"name\": \"nested_radio_question\",\n",
+                "    \"answer\": {\n",
+                "        \"name\":\n",
+                "            \"first_radio_answer\",\n",
+                "        \"confidence\":\n",
+                "            0.5,\n",
+                "        \"classifications\": [{\n",
+                "            \"name\": \"sub_radio_question\",\n",
+                "            \"answer\": {\n",
+                "                \"name\": \"first_sub_radio_answer\",\n",
+                "                \"confidence\": 0.5\n",
+                "            },\n",
+                "        }],\n",
+                "    },\n",
+                "}"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
@@ -171,218 +326,503 @@
                 "  }\n",
                 "]\n",
                 "```\n"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### Example of row_data with model outputs"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "pairwise_shopping_2 = \"\"\"\n",
+                " {\n",
+                "  \"type\": \"application/vnd.labelbox.conversational\",\n",
+                "  \"version\": 1,\n",
+                "  \"messages\": [\n",
+                "    {\n",
+                "      \"messageId\": \"message-0\",\n",
+                "      \"timestampUsec\": 1530718491,\n",
+                "      \"content\": \"Hi! How can I help?\",\n",
+                "      \"user\": {\n",
+                "        \"userId\": \"Bot 002\",\n",
+                "        \"name\": \"Bot\"\n",
+                "      },\n",
+                "      \"align\": \"left\",\n",
+                "      \"canLabel\": false\n",
+                "    },\n",
+                "    {\n",
+                "      \"messageId\": \"message-1\",\n",
+                "      \"timestampUsec\": 1530718503,\n",
+                "      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n",
+                "      \"user\": {\n",
+                "        \"userId\": \"User 00686\",\n",
+                "        \"name\": \"User\"\n",
+                "      },\n",
+                "      \"align\": \"right\",\n",
+                "      \"canLabel\": true\n",
+                "    }\n",
+                "\n",
+                "  ],\n",
+                "  \"modelOutputs\": [\n",
+                "    {\n",
+                "      \"title\": \"Response A\",\n",
+                "      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n",
+                "      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n",
+                "    },\n",
+                "    {\n",
+                "      \"title\": \"Response B\",\n",
+                "      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n",
+                "      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n",
+                "    }\n",
+                "  ]\n",
+                "}\n",
+                "\"\"\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "global_key = \"pairwise_shooping_asset\"\n",
+                "convo_data = {\n",
+                "    \"row_data\":\n",
+                "        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n",
+                "    \"global_key\":\n",
+                "        global_key,\n",
+                "}\n",
+                "# Create a dataset\n",
+                "dataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n",
+                "# Create a datarows\n",
+                "task = dataset.create_data_rows([convo_data])\n",
+                "print(\"Errors:\", task.errors)\n",
+                "print(\"Failed data rows:\", task.failed_data_rows)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 2: Create/select an Ontology for your model predictions"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Create an ontology with relevant classifications\n",
+                "\n",
+                "ontology_builder = lb.OntologyBuilder(\n",
+                "    tools=[\n",
+                "        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n",
+                "    ],\n",
+                "    classifications=[\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.RADIO,\n",
+                "            scope=lb.Classification.Scope.GLOBAL,\n",
+                "            name=\"Choose the best response\",\n",
+                "            options=[\n",
+                "                lb.Option(value=\"Response A\"),\n",
+                "                lb.Option(value=\"Response B\"),\n",
+                "                lb.Option(value=\"Tie\"),\n",
+                "            ],\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.TEXT,\n",
+                "            name=\"Provide a reason for your choice\",\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.CHECKLIST,\n",
+                "            scope=lb.Classification.Scope.INDEX,\n",
+                "            name=\"checklist_convo\",\n",
+                "            options=[\n",
+                "                lb.Option(value=\"first_checklist_answer\"),\n",
+                "                lb.Option(value=\"second_checklist_answer\"),\n",
+                "            ],\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.CHECKLIST,\n",
+                "            name=\"nested_checklist_question\",\n",
+                "            scope=lb.Classification.Scope.INDEX,\n",
+                "            options=[\n",
+                "                lb.Option(\n",
+                "                    \"first_checklist_answer\",\n",
+                "                    options=[\n",
+                "                        lb.Classification(\n",
+                "                            class_type=lb.Classification.Type.CHECKLIST,\n",
+                "                            name=\"sub_checklist_question\",\n",
+                "                            options=[lb.Option(\"first_sub_checklist_answer\")],\n",
+                "                        )\n",
+                "                    ],\n",
+                "                )\n",
+                "            ],\n",
+                "        ),\n",
+                "        lb.Classification(\n",
+                "            class_type=lb.Classification.Type.RADIO,\n",
+                "            name=\"nested_radio_question\",\n",
+                "            scope=lb.Classification.Scope.GLOBAL,\n",
+                "            options=[\n",
+                "                lb.Option(\n",
+                "                    \"first_radio_answer\",\n",
+                "                    options=[\n",
+                "                        lb.Classification(\n",
+                "                            class_type=lb.Classification.Type.RADIO,\n",
+                "                            name=\"sub_radio_question\",\n",
+                "                            options=[lb.Option(\"first_sub_radio_answer\")],\n",
+                "                        )\n",
+                "                    ],\n",
+                "                )\n",
+                "            ],\n",
+                "        ),\n",
+                "    ],\n",
+                ")\n",
+                "\n",
+                "ontology = client.create_ontology(\n",
+                "    \"Pairwise comparison ontology\",\n",
+                "    ontology_builder.asdict(),\n",
+                "    media_type=lb.MediaType.Conversational,\n",
+                ")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# create model\n",
+                "model = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n",
+                "                            ontology_id=ontology.uid)\n",
+                "# create model run\n",
+                "model_run = model.create_model_run(\"iteration 1\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 4: Send data rows to the Model run"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "model_run.upsert_data_rows(global_keys=[global_key])"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Step 5: Create the predictions payload"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "label_prediction = []\n",
+                "label_prediction.append(\n",
+                "    lb_types.Label(\n",
+                "        data={\"global_key\": global_key},\n",
+                "        annotations=[\n",
+                "            ner_prediction,\n",
+                "            text_prediction,\n",
+                "            checklist_prediction,\n",
+                "            radio_prediction,\n",
+                "            nested_radio_prediction,\n",
+                "            nested_checklist_prediction,\n",
+                "        ],\n",
+                "    ))"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "Setup the payload with the annotations that were created in Step 1."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "label_ndjson = []\n",
+                "for annotations in [\n",
+                "        ner_prediction_ndjson,\n",
+                "        text_prediction_ndjson,\n",
+                "        checklist_prediction_ndjson,\n",
+                "        radio_prediction_ndjson,\n",
+                "        nested_checklist_prediction_ndjson,\n",
+                "        nested_radio_prediction_ndjson,\n",
+                "]:\n",
+                "    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n",
+                "    label_ndjson.append(annotations)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Upload the prediction label to the Model Run\n",
+                "upload_job_prediction = model_run.add_predictions(\n",
+                "    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n",
+                "    predictions=label_prediction,\n",
+                ")\n",
+                "\n",
+                "# Errors will appear for annotation uploads that failed.\n",
+                "print(\"Errors:\", upload_job_prediction.errors)\n",
+                "print(\"Status of uploads: \", upload_job_prediction.statuses)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 7: Send annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.1 Create a labelbox project"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "project = client.create_project(\n",
+                "    name=\"Conversational Human Evaluation Demo\",\n",
+                "    media_type=lb.MediaType.Conversational,\n",
+                ")\n",
+                "project.setup_editor(ontology)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.2 Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "project.create_batch(\n",
+                "    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n",
+                "    global_keys=[\n",
+                "        global_key\n",
+                "    ],  # Paginated collection of data row objects, list of data row ids or global keys\n",
+                "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
+                ")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "ner_annotation = lb_types.ObjectAnnotation(\n",
+                "    name=\"ner\",\n",
+                "    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n",
+                ")\n",
+                "\n",
+                "radio_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"Choose the best response\",\n",
+                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "        name=\"Response B\")),\n",
+                ")\n",
+                "\n",
+                "text_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"Provide a reason for your choice\",\n",
+                "    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n",
+                ")\n",
+                "\n",
+                "checklist_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"checklist_convo\",  # must match your ontology feature\"s name\n",
+                "    value=lb_types.Checklist(answer=[\n",
+                "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
+                "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n",
+                "    ]),\n",
+                "    message_id=\"message-1\",  # Message specific annotation\n",
+                ")\n",
+                "\n",
+                "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"nested_checklist_question\",\n",
+                "    message_id=\"message-1\",\n",
+                "    value=lb_types.Checklist(answer=[\n",
+                "        lb_types.ClassificationAnswer(\n",
+                "            name=\"first_checklist_answer\",\n",
+                "            classifications=[\n",
+                "                lb_types.ClassificationAnnotation(\n",
+                "                    name=\"sub_checklist_question\",\n",
+                "                    value=lb_types.Checklist(answer=[\n",
+                "                        lb_types.ClassificationAnswer(\n",
+                "                            name=\"first_sub_checklist_answer\")\n",
+                "                    ]),\n",
+                "                )\n",
+                "            ],\n",
+                "        )\n",
+                "    ]),\n",
+                ")\n",
+                "\n",
+                "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
+                "    name=\"nested_radio_question\",\n",
+                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "        name=\"first_radio_answer\",\n",
+                "        classifications=[\n",
+                "            lb_types.ClassificationAnnotation(\n",
+                "                name=\"sub_radio_question\",\n",
+                "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
+                "                    name=\"first_sub_radio_answer\")),\n",
+                "            )\n",
+                "        ],\n",
+                "    )),\n",
+                ")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.4 Create the label object"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "label_annotation = []\n",
+                "label_annotation.append(\n",
+                "    lb_types.Label(\n",
+                "        data=lb_types.ConversationData(global_key=global_key),\n",
+                "        annotations=[\n",
+                "            ner_annotation,\n",
+                "            text_annotation,\n",
+                "            checklist_annotation,\n",
+                "            radio_annotation,\n",
+                "            nested_radio_annotation,\n",
+                "            nested_checklist_annotation,\n",
+                "        ],\n",
+                "    ))"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.5 Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
+                "    client=client,\n",
+                "    project_id=project.uid,\n",
+                "    name=\"label_import_job\" + str(uuid.uuid4()),\n",
+                "    labels=label_annotation,\n",
+                ")\n",
+                "\n",
+                "upload_job_annotation.wait_until_done()\n",
+                "# Errors will appear for annotation uploads that failed.\n",
+                "print(\"Errors:\", upload_job_annotation.errors)\n",
+                "print(\"Status of uploads: \", upload_job_annotation.statuses)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# get the labels id from the project\n",
+                "model_run.upsert_labels(project_id=project.uid)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# project.delete()\n",
+                "# dataset.delete()"
+            ]
         }
-    ]
-}
\ No newline at end of file
+    ],
+    "metadata": {
+        "language_info": {
+            "name": "python"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 0
+}
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index 198e5bbdf..94d58218a 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,161 +15,242 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Project Setup\n",
                 "* This notebok describes how to create and configure a project\n",
                 "* This is the same as creating a new project in the editor and going through all of the steps."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
                 "    * An ontology must be set\n",
                 "    * A Batch must be created"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install labelbox[data]",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install \"labelbox[data]\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nimport uuid",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "from labelbox.schema.quality_mode import QualityMode"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# API Key and Client\n",
                 "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Add your api key\n",
+                "API_KEY = \"\"\n",
+                "client = lb.Client(api_key=API_KEY)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Create Dataset\n",
                 "* Create dataset and attach data\n",
                 "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "12e08acb",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "dataset = client.create_dataset(name=\"project_setup_demo\")\n",
+                "global_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n",
+                "## Example image\n",
+                "uploads = []\n",
+                "# Generate data rows\n",
+                "for i in range(1, 5):\n",
+                "    uploads.append({\n",
+                "        \"row_data\":\n",
+                "            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
+                "        \"global_key\":\n",
+                "            global_keys[i - 1],\n",
+                "    })\n",
+                "task = dataset.create_data_rows(uploads)\n",
+                "task.wait_till_done()\n",
+                "print(\"ERRORS: \", task.errors)\n",
+                "print(\"RESULT URL: \", task.result_url)"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "90252611",
             "metadata": {},
             "source": [
                 "### Create Or Select an Ontology\n",
                 "* Optionally create an ontology or select from an existing one.\n",
                 "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
                 "   "
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "e083711e",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Create\n",
+                "ontology_builder = lb.OntologyBuilder(\n",
+                "    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\n",
+                "ontology = client.create_ontology(\"project-setup-demo-ontology\",\n",
+                "                                  ontology_builder.asdict())\n",
+                "# Select existing ontology\n",
+                "# ontology = client.get_ontology(\"<ontology_id>\")\n",
+                "# ontology = existing_project.ontology()"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "5d57b9c2",
             "metadata": {},
             "source": [
                 "### Create Project and Setup the Editor\n",
                 "* Setting up a project will add an ontology and will enable labeling to begin\n",
                 "* Creating batches will add all data_rows belonging to the dataset to the queue."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "820689b9",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "batch_project = client.create_project(\n",
+                "    name=\"Project Setup Demo\",\n",
+                "    quality_mode=QualityMode.\n",
+                "    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n",
+                "    media_type=lb.MediaType.Image,\n",
+                ")\n",
+                "\n",
+                "batch_project.setup_editor(ontology)"
+            ]
         },
         {
+            "cell_type": "markdown",
+            "id": "1dc9fe33",
             "metadata": {},
             "source": [
                 "# Add data to your projects "
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
             "cell_type": "code",
+            "execution_count": null,
+            "id": "40d3a423",
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "## When creating a batch you can also setup the data rows priority\n",
+                "batch = batch_project.create_batch(\n",
+                "    \"batch-demo-4\",  # Each batch in a project must have a unique name\n",
+                "    global_keys=global_keys,  # A list of data rows or data row ids\n",
+                "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
+                "    consensus_settings={\n",
+                "        \"number_of_labels\": 2,\n",
+                "        \"coverage_percentage\": 1\n",
+                "    },\n",
+                ")\n",
+                "print(\"Batch: \", batch)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Review"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Note setup_complete will be None if it fails.\n",
+                "print(batch_project.setup_complete)\n",
+                "print(batch_project.ontology())\n",
+                "print([ds.name for ds in batch_project.batches()])"
+            ]
         },
         {
-            "metadata": {},
-            "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Cleanup"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# batch_project.delete()\n",
+                "# dataset_project.delete()\n",
+                "# dataset.delete()"
+            ]
+        }
+    ],
+    "metadata": {
+        "language_info": {
+            "name": "python"
         }
-    ]
-}
\ No newline at end of file
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index 9032c24c7..f77778ff1 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,77 +15,124 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Webhook Configuration"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "Webhooks are supported for the following events:\n",
                 "* label_created\n",
                 "* label_updated\n",
                 "* label_deleted"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install labelbox\n%pip install requests\n%pip install hmac\n%pip install hashlib\n%pip install flask\n%pip install Werkzeug",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install -q \"labelbox[data]\"\n",
+                "%pip install -q requests\n",
+                "%pip install -q hmac\n",
+                "%pip install -q hashlib\n",
+                "%pip install -q flask\n",
+                "%pip install -q Werkzeug"
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "from flask import Flask, request\n",
+                "import hmac\n",
+                "import hashlib\n",
+                "import threading\n",
+                "from werkzeug.serving import run_simple\n",
+                "import json\n",
+                "import requests\n",
+                "import os\n",
+                "from getpass import getpass\n",
+                "import socket"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# If you don\"t want to give google access to drive you can skip this cell\n",
+                "# and manually set `API_KEY` below.\n",
+                "\n",
+                "COLAB = \"google.colab\" in str(get_ipython())\n",
+                "if COLAB:\n",
+                "    %pip install colab-env -qU\n",
+                "    from colab_env import envvar_handler\n",
+                "\n",
+                "    envvar_handler.envload()\n",
+                "\n",
+                "API_KEY = os.environ.get(\"LABELBOX_API_KEY\")\n",
+                "if not os.environ.get(\"LABELBOX_API_KEY\"):\n",
+                "    API_KEY = getpass(\"Please enter your labelbox api key\")\n",
+                "    if COLAB:\n",
+                "        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Set this to a project that you want to use for the webhook\n",
+                "PROJECT_ID = \"\"\n",
+                "# Only update this if you have an on-prem deployment\n",
+                "ENDPOINT = \"https://api.labelbox.com/graphql\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# We are using port 3001 for this example.\n",
+                "# Feel free to set to whatever port you want\n",
+                "WH_PORT = 3001"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Configure NGROK (Optional)\n",
@@ -99,114 +143,211 @@
                 "2. Download ngrok and extract the zip file\n",
                 "3. Add ngrok to your path\n",
                 "4. Add the authtoken `ngrok authtoken <token>`"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "if not COLAB:\n",
+                "    os.system(f\"ngrok http {WH_PORT} &\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Configure server to receive requests"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# This can be any secret that matches your webhook config (we will set later)\n",
+                "secret = b\"example_secret\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "app = Flask(__name__)\n",
+                "\n",
+                "\n",
+                "@app.route(\"/\")\n",
+                "def hello_world():\n",
+                "    return \"Hello, World!\"\n",
+                "\n",
+                "\n",
+                "@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\n",
+                "def print_webhook_info():\n",
+                "    payload = request.data\n",
+                "    computed_signature = hmac.new(secret, msg=payload,\n",
+                "                                  digestmod=hashlib.sha1).hexdigest()\n",
+                "    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n",
+                "        print(\n",
+                "            \"Error: computed_signature does not match signature provided in the headers\"\n",
+                "        )\n",
+                "        return \"Error\", 500, 200\n",
+                "\n",
+                "    print(\"=========== New Webhook Delivery ============\")\n",
+                "    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n",
+                "    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n",
+                "    print(\"Payload: %s\" %\n",
+                "          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n",
+                "    return \"Success\"\n",
+                "\n",
+                "\n",
+                "thread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\n",
+                "thread.start()"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### Test server"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "print(requests.get(\"http://localhost:3001\").text)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "print(requests.get(\"http://localhost:3001\").text)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Create Webhook"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "- Set ip address if your ip is publicly accessible.\n",
                 "- Otherwise use the following to get ngrok public_url"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "if not COLAB:\n",
+                "    res = requests.get(\"http://localhost:4040/api/tunnels\")\n",
+                "    assert (res.status_code == 200\n",
+                "           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n",
+                "    tunnels = res.json()[\"tunnels\"]\n",
+                "    tunnel = [\n",
+                "        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n",
+                "    ]\n",
+                "    tunnel = tunnel[0]  # Should only be one..\n",
+                "    public_url = tunnel[\"public_url\"]\n",
+                "else:\n",
+                "    public_url = (\n",
+                "        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\n",
+                "print(public_url)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Set project to limit the scope to a single project\n",
+                "project = client.get_project(PROJECT_ID)\n",
+                "topics = {topic.value for topic in lb.Webhook.Topic}\n",
+                "# For Global Webhooks (Global = per workspace) project = None\n",
+                "webhook = lb.Webhook.create(\n",
+                "    client,\n",
+                "    topics=topics,\n",
+                "    url=public_url,\n",
+                "    secret=secret.decode(),\n",
+                "    project=project,\n",
+                ")"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Ok so we should be configured assuming everything is setup correctly.\n",
+                "# Go to the following url and make a new label to see if it works\n",
+                "print(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Update Webhook"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# url, topics, and status can all be updated\n",
+                "updated_url = f\"{public_url}/webhook-endpoint\"\n",
+                "print(updated_url)\n",
+                "webhook.update(url=updated_url)\n",
+                "# Go to the following url and try one last time.\n",
+                "# Any supported action should work (create, delete, or update a label)\n",
+                "print(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### List and delete all webhooks"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# DELETE:\n",
+                "webhook.update(status=lb.Webhook.Status.INACTIVE.value)\n",
+                "\n",
+                "# FETCH ALL WEBHOOKS:\n",
+                "org = client.get_organization()\n",
+                "webhooks = org.webhooks()\n",
+                "\n",
+                "# Run this to clear all.\n",
+                "# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n",
+                "# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n",
+                "# for webhook in webhooks:\n",
+                "#    print(webhook)\n",
+                "#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)"
+            ]
         }
-    ]
-}
\ No newline at end of file
+    ],
+    "metadata": {
+        "language_info": {
+            "name": "python"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 2f3d3f695..5988f9a6a 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -1,7 +1,7 @@
 import pandas
 import glob
 from collections import defaultdict
-import argparse
+from pprint import pprint
 
 """
 Script used to generate readme programmatically works by taking the links of all the notebooks
@@ -10,12 +10,20 @@
 
 IGNORE = ["template.ipynb"]
 
+ORDER = ["basics", "exports", "project_configuration", "annotation_import", "integrations", "model_experiments", "prediction_upload"]
+
 SDK_EXAMPLE_HEADER = """
 # Labelbox SDK Examples\n
 - Learn how to use the SDK by following along\n
 - Run in google colab, view the notebooks on github, or clone the repo and run locally\n
 """
 
+README_EXAMPLE_HEADER = """---
+title: Python tutorials
+---
+
+"""
+
 COLAB_TEMPLATE = "https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/{filename}"
 GITHUB_TEMPLATE = "https://github.com/Labelbox/labelbox-python/tree/develop/examples/{filename}"
 
@@ -89,15 +97,23 @@ def make_links_dict(links: str):
         defaultdict[list]: returns dict that is in pandas dataFrame format
     """
     link_dict = defaultdict(list)
+    extra_links = []
+    for section in ORDER:
+        link_dict[section] = []
     for link in links:
         if link.split("/")[-1] in IGNORE:
             continue
-        split_link = link.split("/")[0]
-        link_dict[split_link].append(link)
+        if link.split("/")[0] == "extras":
+            extra_links.append(link)
+        else:
+            split_link = link.split("/")[0]
+            link_dict[split_link].append(link)
+    link_dict["Extras"] = extra_links
+    pprint(link_dict)
     return link_dict
 
 
-def make_table(base: str = "") -> str:
+def make_table(base: str) -> str:
     """main function to make table
 
     Args:
@@ -125,7 +141,7 @@ def make_table(base: str = "") -> str:
             pandas_dict["Google Colab"].append(
                 make_link(
                     COLAB_TEMPLATE.format(
-                        filename="/".join(link.split("/")[1:])
+                        filename="/".join(link.split("/"))
                     ),
                     "https://colab.research.google.com/assets/colab-badge.svg",
                     "Colab",
@@ -145,8 +161,8 @@ def main(github: bool):
         with open("./README.md", "w") as readme:
             readme.write(make_table(SDK_EXAMPLE_HEADER))
     else:
-        with open("./tmp.html", "w") as readme:
-            readme.write(make_table())
+        with open("./tutorials.html", "w") as readme:
+            readme.write(make_table(README_EXAMPLE_HEADER))
 
 
 if __name__ == "__main__":
diff --git a/examples/template.ipynb b/examples/template.ipynb
new file mode 100644
index 000000000..59f344b48
--- /dev/null
+++ b/examples/template.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Labelbox Example SDK Template\n",
+    "This notebook serves as a template to guide develop of Labelbox example notebooks. Review notebook [CONTRIBUTING.md](https://github.com/Labelbox/labelbox-python/blob/develop/examples/CONTRIBUTING.md) for more detailed information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q \"labelbox[data]\"\n",
+    "# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n",
+    "# Include other installs in this block with similar format: \"%pip install -q <package name>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import labelbox as lb\n",
+    "# Include other imports in this block try grouping similar installs together"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API key and client\n",
+    "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add your API key\n",
+    "API_KEY = None\n",
+    "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
+    "client = lb.Client(api_key=API_KEY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Main section header\n",
+    "\n",
+    "* Include information on what section discusses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sub section header\n",
+    "\n",
+    "* Include information on what sub section discusses\n",
+    "* If parameters are needed specify they need included"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET_ID = None\n",
+    "# Inline Parameters should be all caps with a default value typically None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook section with steps\n",
+    "\n",
+    "* For long winded processes step headers should be included"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step \"step number\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Include information on step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### \"step number.sub step number\"\n",
+    "\n",
+    "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up\n",
+    "Uncomment and run the cell below to optionally delete Labelbox objects created"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# batch.delete()\n",
+    "# project.delete()\n",
+    "# dataset.delete()\n",
+    "\n",
+    "## Include other objects that were created"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 9a12e2cf5ed101776c7259e7cf5f22a9c7aaee49 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 14:43:30 +0000
Subject: [PATCH 44/80] :art: Cleaned

---
 .../conversational_LLM.ipynb                  | 561 +++---------
 examples/basics/basics.ipynb                  | 221 ++---
 examples/basics/batches.ipynb                 | 805 +++++++-----------
 examples/basics/user_management.ipynb         | 239 ++----
 .../conversational_LLM_predictions.ipynb      | 710 +++------------
 .../project_configuration/project_setup.ipynb | 202 ++---
 examples/project_configuration/webhooks.ipynb | 295 ++-----
 examples/scripts/generate_readme.py           |  14 +-
 examples/template.ipynb                       | 282 +++---
 9 files changed, 972 insertions(+), 2357 deletions(-)

diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index 2516947e3..9f0339fad 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,279 +18,145 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
                 "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install -q \"labelbox[data]\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Set up"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "import labelbox.types as lb_types\n",
-                "import uuid"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Replace with your API key"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "API_KEY = \"\"\n",
-                "client = lb.Client(api_key=API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Supported annotations for conversational text"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Entity "
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "ner_annotation = lb_types.ObjectAnnotation(\n",
-                "    name=\"ner\",\n",
-                "    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n",
-                ")\n",
-                "\n",
-                "ner_annotation_ndjson = {\n",
-                "    \"name\": \"ner\",\n",
-                "    \"location\": {\n",
-                "        \"start\": 0,\n",
-                "        \"end\": 8\n",
-                "    },\n",
-                "    \"messageId\": \"message-1\",\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Radio (single-choice)"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "radio_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"Choose the best response\",\n",
-                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "        name=\"Response B\")),\n",
-                ")\n",
-                "\n",
-                "radio_annotation_ndjson = {\n",
-                "    \"name\": \"Choose the best response\",\n",
-                "    \"answer\": {\n",
-                "        \"name\": \"Response B\"\n",
-                "    },\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Free-form text"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "text_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"Provide a reason for your choice\",\n",
-                "    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n",
-                ")\n",
-                "\n",
-                "text_annotation_ndjson = {\n",
-                "    \"name\": \"Provide a reason for your choice\",\n",
-                "    \"answer\": \"This is the more concise answer\",\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Checklist (multi-choice)"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"checklist_convo\",  # must match your ontology feature\"s name\n",
-                "    value=lb_types.Checklist(answer=[\n",
-                "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
-                "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n",
-                "    ]),\n",
-                "    message_id=\"message-1\",  # Message specific annotation\n",
-                ")\n",
-                "\n",
-                "checklist_annotation_ndjson = {\n",
-                "    \"name\": \"checklist_convo\",\n",
-                "    \"answers\": [\n",
-                "        {\n",
-                "            \"name\": \"first_checklist_answer\"\n",
-                "        },\n",
-                "        {\n",
-                "            \"name\": \"second_checklist_answer\"\n",
-                "        },\n",
-                "    ],\n",
-                "    \"messageId\": \"message-1\",\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Nested radio and checklist"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Message based\n",
-                "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"nested_checklist_question\",\n",
-                "    message_id=\"message-1\",\n",
-                "    value=lb_types.Checklist(answer=[\n",
-                "        lb_types.ClassificationAnswer(\n",
-                "            name=\"first_checklist_answer\",\n",
-                "            classifications=[\n",
-                "                lb_types.ClassificationAnnotation(\n",
-                "                    name=\"sub_checklist_question\",\n",
-                "                    value=lb_types.Checklist(answer=[\n",
-                "                        lb_types.ClassificationAnswer(\n",
-                "                            name=\"first_sub_checklist_answer\")\n",
-                "                    ]),\n",
-                "                )\n",
-                "            ],\n",
-                "        )\n",
-                "    ]),\n",
-                ")\n",
-                "# Message based\n",
-                "nested_checklist_annotation_ndjson = {\n",
-                "    \"name\":\n",
-                "        \"nested_checklist_question\",\n",
-                "    \"messageId\":\n",
-                "        \"message-1\",\n",
-                "    \"answer\": [{\n",
-                "        \"name\":\n",
-                "            \"first_checklist_answer\",\n",
-                "        \"classifications\": [{\n",
-                "            \"name\": \"sub_checklist_question\",\n",
-                "            \"answer\": {\n",
-                "                \"name\": \"first_sub_checklist_answer\",\n",
-                "            },\n",
-                "        }],\n",
-                "    }],\n",
-                "}\n",
-                "# Global\n",
-                "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"nested_radio_question\",\n",
-                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "        name=\"first_radio_answer\",\n",
-                "        classifications=[\n",
-                "            lb_types.ClassificationAnnotation(\n",
-                "                name=\"sub_radio_question\",\n",
-                "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "                    name=\"first_sub_radio_answer\")),\n",
-                "            )\n",
-                "        ],\n",
-                "    )),\n",
-                ")\n",
-                "# Global\n",
-                "nested_radio_annotation_ndjson = {\n",
-                "    \"name\": \"nested_radio_question\",\n",
-                "    \"answer\": {\n",
-                "        \"name\":\n",
-                "            \"first_radio_answer\",\n",
-                "        \"classifications\": [{\n",
-                "            \"name\": \"sub_radio_question\",\n",
-                "            \"answer\": {\n",
-                "                \"name\": \"first_sub_radio_answer\"\n",
-                "            },\n",
-                "        }],\n",
-                "    },\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
@@ -301,357 +170,141 @@
                 "  }\n",
                 "]\n",
                 "```\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### Example of row_data with model outputs"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "pairwise_shopping_2 = \"\"\"\n",
-                " {\n",
-                "  \"type\": \"application/vnd.labelbox.conversational\",\n",
-                "  \"version\": 1,\n",
-                "  \"messages\": [\n",
-                "    {\n",
-                "      \"messageId\": \"message-0\",\n",
-                "      \"timestampUsec\": 1530718491,\n",
-                "      \"content\": \"Hi! How can I help?\",\n",
-                "      \"user\": {\n",
-                "        \"userId\": \"Bot 002\",\n",
-                "        \"name\": \"Bot\"\n",
-                "      },\n",
-                "      \"align\": \"left\",\n",
-                "      \"canLabel\": false\n",
-                "    },\n",
-                "    {\n",
-                "      \"messageId\": \"message-1\",\n",
-                "      \"timestampUsec\": 1530718503,\n",
-                "      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n",
-                "      \"user\": {\n",
-                "        \"userId\": \"User 00686\",\n",
-                "        \"name\": \"User\"\n",
-                "      },\n",
-                "      \"align\": \"right\",\n",
-                "      \"canLabel\": true\n",
-                "    }\n",
-                "\n",
-                "  ],\n",
-                "  \"modelOutputs\": [\n",
-                "    {\n",
-                "      \"title\": \"Response A\",\n",
-                "      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n",
-                "      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n",
-                "    },\n",
-                "    {\n",
-                "      \"title\": \"Response B\",\n",
-                "      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n",
-                "      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n",
-                "    }\n",
-                "  ]\n",
-                "}\n",
-                "\"\"\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "global_key = \"pairwise_shooping_asset\"\n",
-                "\n",
-                "# Upload data rows\n",
-                "convo_data = {\n",
-                "    \"row_data\":\n",
-                "        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n",
-                "    \"global_key\":\n",
-                "        global_key,\n",
-                "}\n",
-                "\n",
-                "# Create a dataset\n",
-                "dataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n",
-                "# Create a datarows\n",
-                "task = dataset.create_data_rows([convo_data])\n",
-                "task.wait_till_done()\n",
-                "print(\"Errors:\", task.errors)\n",
-                "print(\"Failed data rows:\", task.failed_data_rows)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 2: Create/select an Ontology"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Create an ontology with relevant classifications\n",
-                "\n",
-                "ontology_builder = lb.OntologyBuilder(\n",
-                "    tools=[\n",
-                "        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n",
-                "    ],\n",
-                "    classifications=[\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.RADIO,\n",
-                "            scope=lb.Classification.Scope.GLOBAL,\n",
-                "            name=\"Choose the best response\",\n",
-                "            options=[\n",
-                "                lb.Option(value=\"Response A\"),\n",
-                "                lb.Option(value=\"Response B\"),\n",
-                "                lb.Option(value=\"Tie\"),\n",
-                "            ],\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.TEXT,\n",
-                "            name=\"Provide a reason for your choice\",\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.CHECKLIST,\n",
-                "            scope=lb.Classification.Scope.INDEX,\n",
-                "            name=\"checklist_convo\",\n",
-                "            options=[\n",
-                "                lb.Option(value=\"first_checklist_answer\"),\n",
-                "                lb.Option(value=\"second_checklist_answer\"),\n",
-                "            ],\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.CHECKLIST,\n",
-                "            name=\"nested_checklist_question\",\n",
-                "            scope=lb.Classification.Scope.INDEX,\n",
-                "            options=[\n",
-                "                lb.Option(\n",
-                "                    \"first_checklist_answer\",\n",
-                "                    options=[\n",
-                "                        lb.Classification(\n",
-                "                            class_type=lb.Classification.Type.CHECKLIST,\n",
-                "                            name=\"sub_checklist_question\",\n",
-                "                            options=[lb.Option(\"first_sub_checklist_answer\")],\n",
-                "                        )\n",
-                "                    ],\n",
-                "                )\n",
-                "            ],\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.RADIO,\n",
-                "            name=\"nested_radio_question\",\n",
-                "            scope=lb.Classification.Scope.GLOBAL,\n",
-                "            options=[\n",
-                "                lb.Option(\n",
-                "                    \"first_radio_answer\",\n",
-                "                    options=[\n",
-                "                        lb.Classification(\n",
-                "                            class_type=lb.Classification.Type.RADIO,\n",
-                "                            name=\"sub_radio_question\",\n",
-                "                            options=[lb.Option(\"first_sub_radio_answer\")],\n",
-                "                        )\n",
-                "                    ],\n",
-                "                )\n",
-                "            ],\n",
-                "        ),\n",
-                "    ],\n",
-                ")\n",
-                "\n",
-                "ontology = client.create_ontology(\n",
-                "    \"Pairwise comparison ontology\",\n",
-                "    ontology_builder.asdict(),\n",
-                "    media_type=lb.MediaType.Conversational,\n",
-                ")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 3: Create a labeling project"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Create Labelbox project\n",
-                "project = client.create_project(\n",
-                "    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n",
-                "    media_type=lb.MediaType.Conversational,\n",
-                ")\n",
-                "\n",
-                "# Setup your ontology\n",
-                "project.setup_editor(\n",
-                "    ontology)  # Connect your ontology and editor to your project"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 4: Send a batch of data rows to the project"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Create a batch to send to your project\n",
-                "batch = project.create_batch(\n",
-                "    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n",
-                "    global_keys=[\n",
-                "        global_key\n",
-                "    ],  # Paginated collection of data row objects, list of data row ids or global keys\n",
-                "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
-                ")\n",
-                "\n",
-                "print(\"Batch: \", batch)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 5: Create the annotations payload"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "Python annotation"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "label = []\n",
-                "label.append(\n",
-                "    lb_types.Label(\n",
-                "        data={\"global_key\": global_key},\n",
-                "        annotations=[\n",
-                "            ner_annotation,\n",
-                "            text_annotation,\n",
-                "            checklist_annotation,\n",
-                "            radio_annotation,\n",
-                "            nested_radio_annotation,\n",
-                "            nested_checklist_annotation,\n",
-                "        ],\n",
-                "    ))"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "NDJSON annotation"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "label_ndjson = []\n",
-                "for annotations in [\n",
-                "        ner_annotation_ndjson,\n",
-                "        text_annotation_ndjson,\n",
-                "        checklist_annotation_ndjson,\n",
-                "        radio_annotation_ndjson,\n",
-                "        nested_checklist_annotation_ndjson,\n",
-                "        nested_radio_annotation_ndjson,\n",
-                "]:\n",
-                "    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n",
-                "    label_ndjson.append(annotations)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 6: Upload annotations to a project as pre-labels or complete labels "
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Model Assisted Labeling (MAL)"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "upload_job = lb.MALPredictionImport.create_from_objects(\n",
-                "    client=client,\n",
-                "    project_id=project.uid,\n",
-                "    name=f\"mal_job-{str(uuid.uuid4())}\",\n",
-                "    predictions=label,\n",
-                ")\n",
-                "\n",
-                "upload_job.wait_until_done()\n",
-                "print(\"Errors:\", upload_job.errors)\n",
-                "print(\"Status of uploads: \", upload_job.statuses)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Label Import"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "upload_job = lb.LabelImport.create_from_objects(\n",
-                "    client=client,\n",
-                "    project_id=project.uid,\n",
-                "    name=\"label_import_job\" + str(uuid.uuid4()),\n",
-                "    labels=label,\n",
-                ")\n",
-                "\n",
-                "upload_job.wait_until_done()\n",
-                "print(\"Errors:\", upload_job.errors)\n",
-                "print(\"Status of uploads: \", upload_job.statuses)"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "env",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "name": "python",
-            "version": "3.10.13"
+            "execution_count": null
         }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 0
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index 94559690e..cb2dd6230 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,262 +18,170 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Basic project/datasets overview\n",
                 "\n",
                 "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
                 "\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install \"labelbox[data]\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install \"labelbox[data]\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## API key and client\n",
                 "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Add your API key\n",
-                "API_KEY = None\n",
-                "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-                "client = lb.Client(api_key=API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# For the purpose of this demo get a single project/dataset id from your organization\n",
-                "\n",
-                "# Get a single Project id\n",
-                "# get_projects returns a PaginatedCollection object, which is iterable.\n",
-                "project = next(client.get_projects())\n",
-                "project_id = project.uid\n",
-                "project_name = project.name\n",
-                "print(\"Project ID: \", project_id)\n",
-                "print(\"Project Name:\", project_name)\n",
-                "\n",
-                "print(\"-\" * 40)\n",
-                "\n",
-                "# Get a single dataset id\n",
-                "# get_datasets returns a PaginatedCollection object, which is iterable.\n",
-                "dataset = next(client.get_datasets())\n",
-                "dataset_id = dataset.uid\n",
-                "dataset_name = dataset.name\n",
-                "print(\"Dataset ID: \", dataset_id)\n",
-                "print(\"Dataset Name:\", dataset_name)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Fetch the project and dataset by using the IDs fetched in the previous cell\n",
-                "project = client.get_project(project_id)\n",
-                "dataset = client.get_dataset(dataset_id)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "print(\"Project: \", project)\n",
-                "print(\"Dataset: \", dataset)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Fields\n",
                 "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
                 "* These fields are attributes of the object"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "print(project.name)\nprint(dataset.name)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "print(project.name)\n",
-                "print(dataset.name)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "project.update(description=\"new description field\")\nprint(project.description)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "project.update(description=\"new description field\")\n",
-                "print(project.description)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Pagination\n",
                 "* Queries that return a list of database objects are return as a PaginatedCollection\n",
                 "* Limits the data that is being returned for better performance"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "labels_paginated_collection = project.labels()\n",
-                "print(\"Type of collection: \", type(labels_paginated_collection))\n",
-                "\n",
-                "# A paginated collection can be parsed by using list()\n",
-                "# list(paginated...) should be avoided for queries that could return more than a dozen results\n",
-                "print(\"Number of labels :\", len(list(labels_paginated_collection)))"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n",
-                "# Iterate over the paginated collection\n",
-                "try:\n",
-                "    single_label = next(project.labels())\n",
-                "    print(single_label)\n",
-                "except StopIteration:\n",
-                "    print(\"Project has no labels !\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Query parameters\n",
                 "* Query with the following conventions:\n",
                 "    * `DbObject.Field`"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n",
-                "\n",
-                "projects = client.get_projects(\n",
-                "    where=((lb.Project.name == project_name) &\n",
-                "           (lb.Project.description == \"new description field\")))\n",
-                "\n",
-                "# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n",
-                "# So even if there is one element returned it is in a paginatedCollection.\n",
-                "print(projects)\n",
-                "print(next(projects, None))\n",
-                "print(next(projects, None))\n",
-                "print(next(projects, None))\n",
-                "# We can see there is only one."
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Querying Limitations\n",
                 "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
                 "* The below query is not valid since get_project returns a project not a dataset\n",
                 ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Relationships between projects and batches/datasets\n",
                 "\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "sample_project_batches = project.batches()\n",
-                "\n",
-                "list(sample_project_batches)\n",
-                "\n",
-                "for b in sample_project_batches:\n",
-                "    print(f\" Name of project : {b.project().name}\")\n",
-                "    print(f\" Name of batches in project: {b.name}\")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "name": "python",
-            "version": "3.12.3"
+            "execution_count": null
         }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index 7e11f090a..dc62d4a7a 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -1,506 +1,301 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<td>\n",
-    "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
-    "</td>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<td>\n",
-    "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
-    "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-    "</td>\n",
-    "\n",
-    "<td>\n",
-    "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
-    "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-    "</td>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Batches\n",
-    "https://docs.labelbox.com/docs/batches"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* A batch is collection of data rows.\n",
-    "* A data row cannot be part of more than one batch in a given project.\n",
-    "* Batches work for all data types, but there can only be one data type per project.\n",
-    "* Batches can not be shared between projects.\n",
-    "* Batches may have data rows from multiple datasets.\n",
-    "* Currently, only benchmarks quality settings is supported in batch projects\n",
-    "* You can set the priority for each batch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q \"labelbox[data]\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import labelbox as lb\n",
-    "import random\n",
-    "import uuid\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API key and client\n",
-    "Provide a valid API key below in order to properly connect to the Labelbox Client."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add your API key\n",
-    "API_KEY = None\n",
-    "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-    "client = lb.Client(api_key=API_KEY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a dataset and data rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4734d69f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a dataset\n",
-    "dataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n",
-    "\n",
-    "uploads = []\n",
-    "# Generate data rows\n",
-    "for i in range(1, 9):\n",
-    "    uploads.append(\n",
-    "        {\n",
-    "            \"row_data\": f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-    "            \"global_key\": \"TEST-ID-%id\" % uuid.uuid1(),\n",
-    "        }\n",
-    "    )\n",
-    "\n",
-    "data_rows = dataset.create_data_rows(uploads)\n",
-    "data_rows.wait_till_done()\n",
-    "print(\"ERRORS: \", data_rows.errors)\n",
-    "print(\"RESULT URL: \", data_rows.result_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "299af781",
-   "metadata": {},
-   "source": [
-    "## Setup batch project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2adbc23d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project = client.create_project(\n",
-    "    name=\"Demo-Batches-Project\", media_type=lb.MediaType.Image\n",
-    ")\n",
-    "print(\"Project Name: \", project.name, \"Project ID: \", project.uid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff7ae419",
-   "metadata": {},
-   "source": [
-    "## Create batches"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fff65df2",
-   "metadata": {},
-   "source": [
-    "### Select all data rows from the dataset\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "041cb1c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client.enable_experimental = True\n",
-    "\n",
-    "export_task = dataset.export()\n",
-    "export_task.wait_till_done()\n",
-    "\n",
-    "data_rows = []\n",
-    "\n",
-    "\n",
-    "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-    "    data_row = json.loads(output.json_str)\n",
-    "    data_rows.append(data_row)\n",
-    "\n",
-    "\n",
-    "if export_task.has_errors():\n",
-    "    export_task.get_stream(\n",
-    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.ERRORS\n",
-    "    ).start(stream_handler=lambda error: print(error))\n",
-    "\n",
-    "if export_task.has_result():\n",
-    "    export_json = export_task.get_stream(\n",
-    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT\n",
-    "    ).start(stream_handler=json_stream_handler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5286f399",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\n",
-    "print(\"Number of global keys:\", len(global_keys))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5bb64625",
-   "metadata": {},
-   "source": [
-    "### Select a random sample\n",
-    "This method is useful if you have large datasets and only want to work with a handful of data rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc279d64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = random.sample(global_keys, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bf836b5",
-   "metadata": {},
-   "source": [
-    "### Create a batch\n",
-    "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab4cf24b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch = project.create_batch(\n",
-    "    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n",
-    "    global_keys=sample,  # A list of data rows or data row ids\n",
-    "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
-    ")\n",
-    "# number of data rows in the batch\n",
-    "print(\"Number of data rows in batch: \", batch.size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba61918d",
-   "metadata": {},
-   "source": [
-    "### Create multiple batches\n",
-    "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
-    "\n",
-    "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
-    "\n",
-    "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
-    "\n",
-    "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3bb23fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First, we must create a second project so that we can re-use the data rows we already created.\n",
-    "second_project = client.create_project(\n",
-    "    name=\"Second-Demo-Batches-Project\", media_type=lb.MediaType.Image\n",
-    ")\n",
-    "print(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n",
-    "\n",
-    "# Then, use the method that will create multiple batches if necessary.\n",
-    "task = second_project.create_batches(\n",
-    "    name_prefix=\"demo-create-batches-\", global_keys=global_keys, priority=5\n",
-    ")\n",
-    "\n",
-    "print(\"Errors: \", task.errors())\n",
-    "print(\"Result: \", task.result())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54f2fc1f",
-   "metadata": {},
-   "source": [
-    "### Create batches from a dataset\n",
-    "\n",
-    "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
-    "\n",
-    "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "affa9fdf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First, we must create a third project so that we can re-use the data rows we already created.\n",
-    "third_project = client.create_project(\n",
-    "    name=\"Third-Demo-Batches-Project\", media_type=lb.MediaType.Image\n",
-    ")\n",
-    "print(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n",
-    "\n",
-    "# Then, use the method to create batches from a dataset.\n",
-    "task = third_project.create_batches_from_dataset(\n",
-    "    name_prefix=\"demo-batches-from-dataset-\", dataset_id=dataset.uid, priority=5\n",
-    ")\n",
-    "\n",
-    "print(\"Errors: \", task.errors())\n",
-    "print(\"Result: \", task.result())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95df8a59",
-   "metadata": {},
-   "source": [
-    "## Manage Batches\n",
-    "Note: You can view your batch data through the **Data Rows** tab."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00b72b6d",
-   "metadata": {},
-   "source": [
-    "### Export Batches"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e8a455f",
-   "metadata": {},
-   "source": [
-    "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a178c178",
-   "metadata": {},
-   "source": [
-    "#### Create and Attach Ontology to Project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "889d6798",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "classification_features = [\n",
-    "    lb.Classification(\n",
-    "        class_type=lb.Classification.Type.CHECKLIST,\n",
-    "        name=\"Quality Issues\",\n",
-    "        options=[\n",
-    "            lb.Option(value=\"blurry\", label=\"Blurry\"),\n",
-    "            lb.Option(value=\"distorted\", label=\"Distorted\"),\n",
-    "        ],\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "ontology_builder = lb.OntologyBuilder(\n",
-    "    tools=[], classifications=classification_features\n",
-    ")\n",
-    "\n",
-    "ontology = client.create_ontology(\n",
-    "    \"Ontology from new features\",\n",
-    "    ontology_builder.asdict(),\n",
-    "    media_type=lb.MediaType.Image,\n",
-    ")\n",
-    "\n",
-    "project.setup_editor(ontology)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c39e4efa",
-   "metadata": {},
-   "source": [
-    "#### Export from Project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1844ddcc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "client.enable_experimental = True\n",
-    "\n",
-    "export_params = {\n",
-    "    \"attachments\": True,\n",
-    "    \"metadata_fields\": True,\n",
-    "    \"data_row_details\": True,\n",
-    "    \"project_details\": True,\n",
-    "    \"performance_details\": True,\n",
-    "    \"batch_ids\": [\n",
-    "        batch.uid\n",
-    "    ],  # Include batch ids if you only want to export specific batches, otherwise,\n",
-    "    # you can export all the data without using this parameter\n",
-    "}\n",
-    "filters = {}\n",
-    "\n",
-    "# A task is returned, this provides additional information about the status of your task, such as\n",
-    "# any errors encountered\n",
-    "export_task = project.export(params=export_params, filters=filters)\n",
-    "export_task.wait_till_done()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "00431979",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_rows = []\n",
-    "\n",
-    "\n",
-    "def json_stream_handler(output: lb.JsonConverterOutput):\n",
-    "    data_row = json.loads(output.json_str)\n",
-    "    data_rows.append(data_row)\n",
-    "\n",
-    "\n",
-    "if export_task.has_errors():\n",
-    "    export_task.get_stream(\n",
-    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.ERRORS\n",
-    "    ).start(stream_handler=lambda error: print(error))\n",
-    "\n",
-    "if export_task.has_result():\n",
-    "    export_json = export_task.get_stream(\n",
-    "        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT\n",
-    "    ).start(stream_handler=json_stream_handler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Export the data row iDs\n",
-    "data_rows = [dr for dr in data_rows]\n",
-    "print(\"Data rows in batch: \", data_rows)\n",
-    "\n",
-    "## List the batches in your project\n",
-    "for batch in project.batches():\n",
-    "    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Archive a batch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Archiving a batch removes all queued data rows in the batch from the project\n",
-    "batch.remove_queued_data_rows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean up \n",
-    "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Delete Batch\n",
-    "# batch.delete()\n",
-    "\n",
-    "# Delete Project\n",
-    "# project.delete()\n",
-    "\n",
-    "# Delete DataSet\n",
-    "# dataset.delete()"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": [
+                "# Batches\n",
+                "https://docs.labelbox.com/docs/batches"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* A batch is collection of data rows.\n",
+                "* A data row cannot be part of more than one batch in a given project.\n",
+                "* Batches work for all data types, but there can only be one data type per project.\n",
+                "* Batches can not be shared between projects.\n",
+                "* Batches may have data rows from multiple datasets.\n",
+                "* Currently, only benchmarks quality settings is supported in batch projects\n",
+                "* You can set the priority for each batch."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API key and client\n",
+                "Provide a valid API key below in order to properly connect to the Labelbox Client."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create a dataset and data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Setup batch project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Create batches"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Select all data rows from the dataset\n"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Select a random sample\n",
+                "This method is useful if you have large datasets and only want to work with a handful of data rows"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "sample = random.sample(global_keys, 4)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create a batch\n",
+                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create multiple batches\n",
+                "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
+                "\n",
+                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
+                "\n",
+                "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
+                "\n",
+                "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Create batches from a dataset\n",
+                "\n",
+                "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
+                "\n",
+                "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Manage Batches\n",
+                "Note: You can view your batch data through the **Data Rows** tab."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Export Batches"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Create and Attach Ontology to Project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### Export from Project"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Archive a batch"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Clean up \n",
+                "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index 28d67bab9..5e028e049 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,19 +18,18 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# User Management\n",
@@ -38,288 +40,209 @@
                 "    * assign users to projects\n",
                 "        * set / update / revoke project role\n",
                 "    * delete users from org"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f345bd18",
             "metadata": {},
+            "source": "%pip install \"labelbox[data]\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install \"labelbox[data]\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "4fd5adda",
             "metadata": {},
+            "source": "import labelbox as lb\nimport os",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "import os"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "6325834e",
             "metadata": {},
             "source": [
                 "* You have to specifically enable experimental features to use this functionality. Notice the \n",
                 "`enable_experimental = True`\n",
                 "    * enables users to send invites and checking the number of seats available via the sdk"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
-            "id": "72511d8c",
             "metadata": {},
             "source": [
                 "# API Key and Client\n",
                 "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "f9697fac",
             "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Add your api key\n",
-                "API_KEY = None\n",
-                "client = lb.Client(api_key=API_KEY, enable_experimental=True)\n",
-                "organization = client.get_organization()"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7c4d302f",
             "metadata": {},
+            "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Please provide a dummy email here:\n",
-                "# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\n",
-                "DUMMY_EMAIL = \"SET THIS\"\n",
-                "# This should be set to an account that you wan't to change the permissions for.\n",
-                "# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\n",
-                "DUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "47f63fcf",
             "metadata": {},
             "source": [
                 "### Roles\n",
                 "* When inviting a new user to an organization, there are various roles to select from.\n",
                 "* All available roles to your org can be accessed via `client.get_roles()`"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "45b2cb42",
             "metadata": {},
+            "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "roles = client.get_roles()\n",
-                "for name, role in roles.items():\n",
-                "    print(role.name, \":\", role.uid)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "b532d716",
             "metadata": {},
             "source": [
                 "* Above we printed out all of the roles available to the current org.\n",
                 "* Notice the `NONE`. That is for project level roles"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
-            "id": "b664c049",
             "metadata": {},
             "source": [
                 "### Create\n",
                 "* Users are created by sending an invite\n",
                 "* An email will be sent to them and they will be asked to join your organization"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
-            "id": "c235a7ad",
             "metadata": {},
             "source": [
                 "#### Organization Level Permissions\n",
                 "* Invite a new labeler with labeling permissions on all projects"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "dea95f3d",
             "metadata": {},
+            "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# First make sure that you have enough seats:\n",
-                "organization.invite_limit()"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "0ad0c884",
             "metadata": {},
+            "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "3d8e18b3",
             "metadata": {},
+            "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "print(invite.created_at)\n",
-                "print(invite.organization_role_name)\n",
-                "print(invite.email)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "6f5e5a21",
             "metadata": {},
             "source": [
                 "#### Project Level Permissions\n",
                 "* Invite a new labeler with labeling permissions specific to a set of projects\n",
                 "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "26073bce",
             "metadata": {},
+            "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "project = client.create_project(name=\"test_user_management\",\n",
-                "                                media_type=lb.MediaType.Image)\n",
-                "project_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\n",
-                "invite = organization.invite_user(DUMMY_EMAIL,\n",
-                "                                  roles[\"NONE\"],\n",
-                "                                  project_roles=[project_role])"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Read\n",
                 "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
                 "* You are able to query for members once they have joined."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "users = list(organization.users())\nprint(users[0])",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "users = list(organization.users())\n",
-                "print(users[0])"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Update\n",
                 "* There is no update on invites. Instead you must delete and resend them\n",
                 "* You can update User roles"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n",
-                "\n",
-                "# Give the user organization level permissions\n",
-                "user.update_org_role(roles[\"LABELER\"])\n",
-                "print(user.org_role())\n",
-                "# Restore project level permissions\n",
-                "user.update_org_role(roles[\"NONE\"])\n",
-                "print(user.org_role())\n",
-                "# Make the user a labeler for the current project\n",
-                "user.upsert_project_role(project, roles[\"LABELER\"])\n",
-                "print(user.org_role())"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\n",
-                "user.remove_from_project(project)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Delete"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Invites can only be deleted from the ui at this time. \n",
                 "* Deleting invites can be done in the members tab of the web app."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Delete the User\n",
                 "* Make sure you want to remove the user from the org:\n",
                 "* `>>> organization.remove_user(user)`"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Cleanup\n",
                 "* We created an extra project. Let's delete it"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "project.delete()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "project.delete()"
-            ]
+            "execution_count": null
         }
-    ],
-    "metadata": {
-        "language_info": {
-            "name": "python"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index 8132fe656..b2de76f56 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,304 +18,146 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# LLM pairwise comparison with Conversational text using Model\n",
                 "\n",
                 "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install -q \"labelbox[data]\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Set up"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "import labelbox.types as lb_types\n",
-                "import uuid"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Replace with your API Key"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "API_KEY = \"\"\n",
-                "client = lb.Client(api_key=API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Supported annotations for conversational text"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Entity"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "ner_prediction = lb_types.ObjectAnnotation(\n",
-                "    name=\"ner\",\n",
-                "    confidence=0.5,\n",
-                "    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n",
-                ")\n",
-                "\n",
-                "ner_prediction_ndjson = {\n",
-                "    \"name\": \"ner\",\n",
-                "    \"confidence\": 0.5,\n",
-                "    \"location\": {\n",
-                "        \"start\": 0,\n",
-                "        \"end\": 8\n",
-                "    },\n",
-                "    \"messageId\": \"message-1\",\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Radio (single-choice)"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "radio_prediction = lb_types.ClassificationAnnotation(\n",
-                "    name=\"Choose the best response\",\n",
-                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n",
-                "                                                              confidence=0.5)),\n",
-                ")\n",
-                "\n",
-                "radio_prediction_ndjson = {\n",
-                "    \"name\": \"Choose the best response\",\n",
-                "    \"answer\": {\n",
-                "        \"name\": \"Response B\",\n",
-                "        \"confidence\": 0.5\n",
-                "    },\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Free-form text"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "text_prediction = lb_types.ClassificationAnnotation(\n",
-                "    name=\"Provide a reason for your choice\",\n",
-                "    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n",
-                "                        confidence=0.5),\n",
-                ")\n",
-                "\n",
-                "text_prediction_ndjson = {\n",
-                "    \"name\": \"Provide a reason for your choice\",\n",
-                "    \"answer\": \"This is the more concise answer\",\n",
-                "    \"confidence\": 0.5,\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Checklist (multi-choice)"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "checklist_prediction = lb_types.ClassificationAnnotation(\n",
-                "    name=\"checklist_convo\",  # must match your ontology feature\"s name\n",
-                "    value=lb_types.Checklist(answer=[\n",
-                "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n",
-                "                                      confidence=0.5),\n",
-                "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n",
-                "                                      confidence=0.5),\n",
-                "    ]),\n",
-                "    message_id=\"message-1\",  # Message specific annotation\n",
-                ")\n",
-                "\n",
-                "checklist_prediction_ndjson = {\n",
-                "    \"name\": \"checklist_convo\",\n",
-                "    \"answers\": [\n",
-                "        {\n",
-                "            \"name\": \"first_checklist_answer\",\n",
-                "            \"confidence\": 0.5\n",
-                "        },\n",
-                "        {\n",
-                "            \"name\": \"second_checklist_answer\",\n",
-                "            \"confidence\": 0.5\n",
-                "        },\n",
-                "    ],\n",
-                "    \"messageId\": \"message-1\",\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Classification: Nested radio and checklist"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Message based\n",
-                "nested_checklist_prediction = lb_types.ClassificationAnnotation(\n",
-                "    name=\"nested_checklist_question\",\n",
-                "    message_id=\"message-1\",\n",
-                "    value=lb_types.Checklist(answer=[\n",
-                "        lb_types.ClassificationAnswer(\n",
-                "            name=\"first_checklist_answer\",\n",
-                "            confidence=0.5,  # Confidence scores should be added to the answer\n",
-                "            classifications=[\n",
-                "                lb_types.ClassificationAnnotation(\n",
-                "                    name=\"sub_checklist_question\",\n",
-                "                    value=lb_types.Checklist(answer=[\n",
-                "                        lb_types.ClassificationAnswer(\n",
-                "                            name=\"first_sub_checklist_answer\",\n",
-                "                            confidence=\n",
-                "                            0.5,  # Confidence scores should be added to the answer\n",
-                "                        )\n",
-                "                    ]),\n",
-                "                )\n",
-                "            ],\n",
-                "        )\n",
-                "    ]),\n",
-                ")\n",
-                "# Message based\n",
-                "nested_checklist_prediction_ndjson = {\n",
-                "    \"name\":\n",
-                "        \"nested_checklist_question\",\n",
-                "    \"messageId\":\n",
-                "        \"message-1\",\n",
-                "    \"answer\": [{\n",
-                "        \"name\":\n",
-                "            \"first_checklist_answer\",\n",
-                "        \"confidence\":\n",
-                "            0.5,  # Confidence scores should be added to the answer\n",
-                "        \"classifications\": [{\n",
-                "            \"name\": \"sub_checklist_question\",\n",
-                "            \"answer\": {\n",
-                "                \"name\": \"first_sub_checklist_answer\",\n",
-                "                \"confidence\":\n",
-                "                    0.5,  # Confidence scores should be added to the answer\n",
-                "            },\n",
-                "        }],\n",
-                "    }],\n",
-                "}\n",
-                "# Global\n",
-                "nested_radio_prediction = lb_types.ClassificationAnnotation(\n",
-                "    name=\"nested_radio_question\",\n",
-                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "        name=\"first_radio_answer\",\n",
-                "        confidence=0.5,  # Confidence scores should be added to the answer\n",
-                "        classifications=[\n",
-                "            lb_types.ClassificationAnnotation(\n",
-                "                name=\"sub_radio_question\",\n",
-                "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "                    name=\"first_sub_radio_answer\",\n",
-                "                    confidence=\n",
-                "                    0.5,  # Confidence scores should be added to the answer\n",
-                "                )),\n",
-                "            )\n",
-                "        ],\n",
-                "    )),\n",
-                ")\n",
-                "# Global\n",
-                "nested_radio_prediction_ndjson = {\n",
-                "    \"name\": \"nested_radio_question\",\n",
-                "    \"answer\": {\n",
-                "        \"name\":\n",
-                "            \"first_radio_answer\",\n",
-                "        \"confidence\":\n",
-                "            0.5,\n",
-                "        \"classifications\": [{\n",
-                "            \"name\": \"sub_radio_question\",\n",
-                "            \"answer\": {\n",
-                "                \"name\": \"first_sub_radio_answer\",\n",
-                "                \"confidence\": 0.5\n",
-                "            },\n",
-                "        }],\n",
-                "    },\n",
-                "}"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
@@ -326,503 +171,218 @@
                 "  }\n",
                 "]\n",
                 "```\n"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### Example of row_data with model outputs"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "pairwise_shopping_2 = \"\"\"\n",
-                " {\n",
-                "  \"type\": \"application/vnd.labelbox.conversational\",\n",
-                "  \"version\": 1,\n",
-                "  \"messages\": [\n",
-                "    {\n",
-                "      \"messageId\": \"message-0\",\n",
-                "      \"timestampUsec\": 1530718491,\n",
-                "      \"content\": \"Hi! How can I help?\",\n",
-                "      \"user\": {\n",
-                "        \"userId\": \"Bot 002\",\n",
-                "        \"name\": \"Bot\"\n",
-                "      },\n",
-                "      \"align\": \"left\",\n",
-                "      \"canLabel\": false\n",
-                "    },\n",
-                "    {\n",
-                "      \"messageId\": \"message-1\",\n",
-                "      \"timestampUsec\": 1530718503,\n",
-                "      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n",
-                "      \"user\": {\n",
-                "        \"userId\": \"User 00686\",\n",
-                "        \"name\": \"User\"\n",
-                "      },\n",
-                "      \"align\": \"right\",\n",
-                "      \"canLabel\": true\n",
-                "    }\n",
-                "\n",
-                "  ],\n",
-                "  \"modelOutputs\": [\n",
-                "    {\n",
-                "      \"title\": \"Response A\",\n",
-                "      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n",
-                "      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n",
-                "    },\n",
-                "    {\n",
-                "      \"title\": \"Response B\",\n",
-                "      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n",
-                "      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n",
-                "    }\n",
-                "  ]\n",
-                "}\n",
-                "\"\"\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "global_key = \"pairwise_shooping_asset\"\n",
-                "convo_data = {\n",
-                "    \"row_data\":\n",
-                "        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n",
-                "    \"global_key\":\n",
-                "        global_key,\n",
-                "}\n",
-                "# Create a dataset\n",
-                "dataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n",
-                "# Create a datarows\n",
-                "task = dataset.create_data_rows([convo_data])\n",
-                "print(\"Errors:\", task.errors)\n",
-                "print(\"Failed data rows:\", task.failed_data_rows)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 2: Create/select an Ontology for your model predictions"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Create an ontology with relevant classifications\n",
-                "\n",
-                "ontology_builder = lb.OntologyBuilder(\n",
-                "    tools=[\n",
-                "        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n",
-                "    ],\n",
-                "    classifications=[\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.RADIO,\n",
-                "            scope=lb.Classification.Scope.GLOBAL,\n",
-                "            name=\"Choose the best response\",\n",
-                "            options=[\n",
-                "                lb.Option(value=\"Response A\"),\n",
-                "                lb.Option(value=\"Response B\"),\n",
-                "                lb.Option(value=\"Tie\"),\n",
-                "            ],\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.TEXT,\n",
-                "            name=\"Provide a reason for your choice\",\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.CHECKLIST,\n",
-                "            scope=lb.Classification.Scope.INDEX,\n",
-                "            name=\"checklist_convo\",\n",
-                "            options=[\n",
-                "                lb.Option(value=\"first_checklist_answer\"),\n",
-                "                lb.Option(value=\"second_checklist_answer\"),\n",
-                "            ],\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.CHECKLIST,\n",
-                "            name=\"nested_checklist_question\",\n",
-                "            scope=lb.Classification.Scope.INDEX,\n",
-                "            options=[\n",
-                "                lb.Option(\n",
-                "                    \"first_checklist_answer\",\n",
-                "                    options=[\n",
-                "                        lb.Classification(\n",
-                "                            class_type=lb.Classification.Type.CHECKLIST,\n",
-                "                            name=\"sub_checklist_question\",\n",
-                "                            options=[lb.Option(\"first_sub_checklist_answer\")],\n",
-                "                        )\n",
-                "                    ],\n",
-                "                )\n",
-                "            ],\n",
-                "        ),\n",
-                "        lb.Classification(\n",
-                "            class_type=lb.Classification.Type.RADIO,\n",
-                "            name=\"nested_radio_question\",\n",
-                "            scope=lb.Classification.Scope.GLOBAL,\n",
-                "            options=[\n",
-                "                lb.Option(\n",
-                "                    \"first_radio_answer\",\n",
-                "                    options=[\n",
-                "                        lb.Classification(\n",
-                "                            class_type=lb.Classification.Type.RADIO,\n",
-                "                            name=\"sub_radio_question\",\n",
-                "                            options=[lb.Option(\"first_sub_radio_answer\")],\n",
-                "                        )\n",
-                "                    ],\n",
-                "                )\n",
-                "            ],\n",
-                "        ),\n",
-                "    ],\n",
-                ")\n",
-                "\n",
-                "ontology = client.create_ontology(\n",
-                "    \"Pairwise comparison ontology\",\n",
-                "    ontology_builder.asdict(),\n",
-                "    media_type=lb.MediaType.Conversational,\n",
-                ")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 3: Create a Model and Model Run"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# create model\n",
-                "model = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n",
-                "                            ontology_id=ontology.uid)\n",
-                "# create model run\n",
-                "model_run = model.create_model_run(\"iteration 1\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 4: Send data rows to the Model run"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "model_run.upsert_data_rows(global_keys=[global_key])"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Step 5: Create the predictions payload"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "label_prediction = []\n",
-                "label_prediction.append(\n",
-                "    lb_types.Label(\n",
-                "        data={\"global_key\": global_key},\n",
-                "        annotations=[\n",
-                "            ner_prediction,\n",
-                "            text_prediction,\n",
-                "            checklist_prediction,\n",
-                "            radio_prediction,\n",
-                "            nested_radio_prediction,\n",
-                "            nested_checklist_prediction,\n",
-                "        ],\n",
-                "    ))"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "Setup the payload with the annotations that were created in Step 1."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "label_ndjson = []\n",
-                "for annotations in [\n",
-                "        ner_prediction_ndjson,\n",
-                "        text_prediction_ndjson,\n",
-                "        checklist_prediction_ndjson,\n",
-                "        radio_prediction_ndjson,\n",
-                "        nested_checklist_prediction_ndjson,\n",
-                "        nested_radio_prediction_ndjson,\n",
-                "]:\n",
-                "    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n",
-                "    label_ndjson.append(annotations)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 6: Upload the predictions payload to the Model Run"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Upload the prediction label to the Model Run\n",
-                "upload_job_prediction = model_run.add_predictions(\n",
-                "    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n",
-                "    predictions=label_prediction,\n",
-                ")\n",
-                "\n",
-                "# Errors will appear for annotation uploads that failed.\n",
-                "print(\"Errors:\", upload_job_prediction.errors)\n",
-                "print(\"Status of uploads: \", upload_job_prediction.statuses)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Step 7: Send annotations to the Model Run "
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.1 Create a labelbox project"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "project = client.create_project(\n",
-                "    name=\"Conversational Human Evaluation Demo\",\n",
-                "    media_type=lb.MediaType.Conversational,\n",
-                ")\n",
-                "project.setup_editor(ontology)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.2 Create a batch to send to the project"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "project.create_batch(\n",
-                "    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n",
-                "    global_keys=[\n",
-                "        global_key\n",
-                "    ],  # Paginated collection of data row objects, list of data row ids or global keys\n",
-                "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
-                ")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.3 Create the annotations payload"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "ner_annotation = lb_types.ObjectAnnotation(\n",
-                "    name=\"ner\",\n",
-                "    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n",
-                ")\n",
-                "\n",
-                "radio_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"Choose the best response\",\n",
-                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "        name=\"Response B\")),\n",
-                ")\n",
-                "\n",
-                "text_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"Provide a reason for your choice\",\n",
-                "    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n",
-                ")\n",
-                "\n",
-                "checklist_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"checklist_convo\",  # must match your ontology feature\"s name\n",
-                "    value=lb_types.Checklist(answer=[\n",
-                "        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n",
-                "        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n",
-                "    ]),\n",
-                "    message_id=\"message-1\",  # Message specific annotation\n",
-                ")\n",
-                "\n",
-                "nested_checklist_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"nested_checklist_question\",\n",
-                "    message_id=\"message-1\",\n",
-                "    value=lb_types.Checklist(answer=[\n",
-                "        lb_types.ClassificationAnswer(\n",
-                "            name=\"first_checklist_answer\",\n",
-                "            classifications=[\n",
-                "                lb_types.ClassificationAnnotation(\n",
-                "                    name=\"sub_checklist_question\",\n",
-                "                    value=lb_types.Checklist(answer=[\n",
-                "                        lb_types.ClassificationAnswer(\n",
-                "                            name=\"first_sub_checklist_answer\")\n",
-                "                    ]),\n",
-                "                )\n",
-                "            ],\n",
-                "        )\n",
-                "    ]),\n",
-                ")\n",
-                "\n",
-                "nested_radio_annotation = lb_types.ClassificationAnnotation(\n",
-                "    name=\"nested_radio_question\",\n",
-                "    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "        name=\"first_radio_answer\",\n",
-                "        classifications=[\n",
-                "            lb_types.ClassificationAnnotation(\n",
-                "                name=\"sub_radio_question\",\n",
-                "                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n",
-                "                    name=\"first_sub_radio_answer\")),\n",
-                "            )\n",
-                "        ],\n",
-                "    )),\n",
-                ")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.4 Create the label object"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "label_annotation = []\n",
-                "label_annotation.append(\n",
-                "    lb_types.Label(\n",
-                "        data=lb_types.ConversationData(global_key=global_key),\n",
-                "        annotations=[\n",
-                "            ner_annotation,\n",
-                "            text_annotation,\n",
-                "            checklist_annotation,\n",
-                "            radio_annotation,\n",
-                "            nested_radio_annotation,\n",
-                "            nested_checklist_annotation,\n",
-                "        ],\n",
-                "    ))"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.5 Upload annotations to the project using Label Import"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "upload_job_annotation = lb.LabelImport.create_from_objects(\n",
-                "    client=client,\n",
-                "    project_id=project.uid,\n",
-                "    name=\"label_import_job\" + str(uuid.uuid4()),\n",
-                "    labels=label_annotation,\n",
-                ")\n",
-                "\n",
-                "upload_job_annotation.wait_until_done()\n",
-                "# Errors will appear for annotation uploads that failed.\n",
-                "print(\"Errors:\", upload_job_annotation.errors)\n",
-                "print(\"Status of uploads: \", upload_job_annotation.statuses)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "7.6 Send the annotations to the Model Run"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# get the labels id from the project\n",
-                "model_run.upsert_labels(project_id=project.uid)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Option deletions for cleanup"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# project.delete()\n# dataset.delete()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# project.delete()\n",
-                "# dataset.delete()"
-            ]
+            "execution_count": null
         }
-    ],
-    "metadata": {
-        "language_info": {
-            "name": "python"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 0
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index 94d58218a..d35aa8067 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,242 +18,161 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Project Setup\n",
                 "* This notebok describes how to create and configure a project\n",
                 "* This is the same as creating a new project in the editor and going through all of the steps."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
                 "    * An ontology must be set\n",
                 "    * A Batch must be created"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install \"labelbox[data]\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install \"labelbox[data]\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "from labelbox.schema.quality_mode import QualityMode"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# API Key and Client\n",
                 "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Add your api key\n",
-                "API_KEY = \"\"\n",
-                "client = lb.Client(api_key=API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Create Dataset\n",
                 "* Create dataset and attach data\n",
                 "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "12e08acb",
             "metadata": {},
+            "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "dataset = client.create_dataset(name=\"project_setup_demo\")\n",
-                "global_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n",
-                "## Example image\n",
-                "uploads = []\n",
-                "# Generate data rows\n",
-                "for i in range(1, 5):\n",
-                "    uploads.append({\n",
-                "        \"row_data\":\n",
-                "            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n",
-                "        \"global_key\":\n",
-                "            global_keys[i - 1],\n",
-                "    })\n",
-                "task = dataset.create_data_rows(uploads)\n",
-                "task.wait_till_done()\n",
-                "print(\"ERRORS: \", task.errors)\n",
-                "print(\"RESULT URL: \", task.result_url)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "90252611",
             "metadata": {},
             "source": [
                 "### Create Or Select an Ontology\n",
                 "* Optionally create an ontology or select from an existing one.\n",
                 "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
                 "   "
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "e083711e",
             "metadata": {},
+            "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Create\n",
-                "ontology_builder = lb.OntologyBuilder(\n",
-                "    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\n",
-                "ontology = client.create_ontology(\"project-setup-demo-ontology\",\n",
-                "                                  ontology_builder.asdict())\n",
-                "# Select existing ontology\n",
-                "# ontology = client.get_ontology(\"<ontology_id>\")\n",
-                "# ontology = existing_project.ontology()"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "5d57b9c2",
             "metadata": {},
             "source": [
                 "### Create Project and Setup the Editor\n",
                 "* Setting up a project will add an ontology and will enable labeling to begin\n",
                 "* Creating batches will add all data_rows belonging to the dataset to the queue."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "820689b9",
             "metadata": {},
+            "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "batch_project = client.create_project(\n",
-                "    name=\"Project Setup Demo\",\n",
-                "    quality_mode=QualityMode.\n",
-                "    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n",
-                "    media_type=lb.MediaType.Image,\n",
-                ")\n",
-                "\n",
-                "batch_project.setup_editor(ontology)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
-            "id": "1dc9fe33",
             "metadata": {},
             "source": [
                 "# Add data to your projects "
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "40d3a423",
             "metadata": {},
+            "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "## When creating a batch you can also setup the data rows priority\n",
-                "batch = batch_project.create_batch(\n",
-                "    \"batch-demo-4\",  # Each batch in a project must have a unique name\n",
-                "    global_keys=global_keys,  # A list of data rows or data row ids\n",
-                "    priority=5,  # priority between 1(Highest) - 5(lowest)\n",
-                "    consensus_settings={\n",
-                "        \"number_of_labels\": 2,\n",
-                "        \"coverage_percentage\": 1\n",
-                "    },\n",
-                ")\n",
-                "print(\"Batch: \", batch)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Review"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Note setup_complete will be None if it fails.\n",
-                "print(batch_project.setup_complete)\n",
-                "print(batch_project.ontology())\n",
-                "print([ds.name for ds in batch_project.batches()])"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Cleanup"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# batch_project.delete()\n",
-                "# dataset_project.delete()\n",
-                "# dataset.delete()"
-            ]
-        }
-    ],
-    "metadata": {
-        "language_info": {
-            "name": "python"
+            "execution_count": null
         }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index f77778ff1..1ed5ec118 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 5,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,124 +18,77 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Webhook Configuration"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "Webhooks are supported for the following events:\n",
                 "* label_created\n",
                 "* label_updated\n",
                 "* label_deleted"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q requests\n%pip install -q hmac\n%pip install -q hashlib\n%pip install -q flask\n%pip install -q Werkzeug",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install -q \"labelbox[data]\"\n",
-                "%pip install -q requests\n",
-                "%pip install -q hmac\n",
-                "%pip install -q hashlib\n",
-                "%pip install -q flask\n",
-                "%pip install -q Werkzeug"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "from flask import Flask, request\n",
-                "import hmac\n",
-                "import hashlib\n",
-                "import threading\n",
-                "from werkzeug.serving import run_simple\n",
-                "import json\n",
-                "import requests\n",
-                "import os\n",
-                "from getpass import getpass\n",
-                "import socket"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# If you don\"t want to give google access to drive you can skip this cell\n",
-                "# and manually set `API_KEY` below.\n",
-                "\n",
-                "COLAB = \"google.colab\" in str(get_ipython())\n",
-                "if COLAB:\n",
-                "    %pip install colab-env -qU\n",
-                "    from colab_env import envvar_handler\n",
-                "\n",
-                "    envvar_handler.envload()\n",
-                "\n",
-                "API_KEY = os.environ.get(\"LABELBOX_API_KEY\")\n",
-                "if not os.environ.get(\"LABELBOX_API_KEY\"):\n",
-                "    API_KEY = getpass(\"Please enter your labelbox api key\")\n",
-                "    if COLAB:\n",
-                "        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Set this to a project that you want to use for the webhook\n",
-                "PROJECT_ID = \"\"\n",
-                "# Only update this if you have an on-prem deployment\n",
-                "ENDPOINT = \"https://api.labelbox.com/graphql\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# We are using port 3001 for this example.\n",
-                "# Feel free to set to whatever port you want\n",
-                "WH_PORT = 3001"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Configure NGROK (Optional)\n",
@@ -143,211 +99,114 @@
                 "2. Download ngrok and extract the zip file\n",
                 "3. Add ngrok to your path\n",
                 "4. Add the authtoken `ngrok authtoken <token>`"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "if not COLAB:\n",
-                "    os.system(f\"ngrok http {WH_PORT} &\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Configure server to receive requests"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# This can be any secret that matches your webhook config (we will set later)\n",
-                "secret = b\"example_secret\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "app = Flask(__name__)\n",
-                "\n",
-                "\n",
-                "@app.route(\"/\")\n",
-                "def hello_world():\n",
-                "    return \"Hello, World!\"\n",
-                "\n",
-                "\n",
-                "@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\n",
-                "def print_webhook_info():\n",
-                "    payload = request.data\n",
-                "    computed_signature = hmac.new(secret, msg=payload,\n",
-                "                                  digestmod=hashlib.sha1).hexdigest()\n",
-                "    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n",
-                "        print(\n",
-                "            \"Error: computed_signature does not match signature provided in the headers\"\n",
-                "        )\n",
-                "        return \"Error\", 500, 200\n",
-                "\n",
-                "    print(\"=========== New Webhook Delivery ============\")\n",
-                "    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n",
-                "    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n",
-                "    print(\"Payload: %s\" %\n",
-                "          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n",
-                "    return \"Success\"\n",
-                "\n",
-                "\n",
-                "thread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\n",
-                "thread.start()"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### Test server"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "print(requests.get(\"http://localhost:3001\").text)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "print(requests.get(\"http://localhost:3001\").text)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Create Webhook"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "- Set ip address if your ip is publicly accessible.\n",
                 "- Otherwise use the following to get ngrok public_url"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "if not COLAB:\n",
-                "    res = requests.get(\"http://localhost:4040/api/tunnels\")\n",
-                "    assert (res.status_code == 200\n",
-                "           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n",
-                "    tunnels = res.json()[\"tunnels\"]\n",
-                "    tunnel = [\n",
-                "        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n",
-                "    ]\n",
-                "    tunnel = tunnel[0]  # Should only be one..\n",
-                "    public_url = tunnel[\"public_url\"]\n",
-                "else:\n",
-                "    public_url = (\n",
-                "        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\n",
-                "print(public_url)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Set project to limit the scope to a single project\n",
-                "project = client.get_project(PROJECT_ID)\n",
-                "topics = {topic.value for topic in lb.Webhook.Topic}\n",
-                "# For Global Webhooks (Global = per workspace) project = None\n",
-                "webhook = lb.Webhook.create(\n",
-                "    client,\n",
-                "    topics=topics,\n",
-                "    url=public_url,\n",
-                "    secret=secret.decode(),\n",
-                "    project=project,\n",
-                ")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Ok so we should be configured assuming everything is setup correctly.\n",
-                "# Go to the following url and make a new label to see if it works\n",
-                "print(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Update Webhook"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# url, topics, and status can all be updated\n",
-                "updated_url = f\"{public_url}/webhook-endpoint\"\n",
-                "print(updated_url)\n",
-                "webhook.update(url=updated_url)\n",
-                "# Go to the following url and try one last time.\n",
-                "# Any supported action should work (create, delete, or update a label)\n",
-                "print(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### List and delete all webhooks"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# DELETE:\n",
-                "webhook.update(status=lb.Webhook.Status.INACTIVE.value)\n",
-                "\n",
-                "# FETCH ALL WEBHOOKS:\n",
-                "org = client.get_organization()\n",
-                "webhooks = org.webhooks()\n",
-                "\n",
-                "# Run this to clear all.\n",
-                "# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n",
-                "# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n",
-                "# for webhook in webhooks:\n",
-                "#    print(webhook)\n",
-                "#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)"
-            ]
+            "execution_count": null
         }
-    ],
-    "metadata": {
-        "language_info": {
-            "name": "python"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 5988f9a6a..5c286dde2 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -10,7 +10,15 @@
 
 IGNORE = ["template.ipynb"]
 
-ORDER = ["basics", "exports", "project_configuration", "annotation_import", "integrations", "model_experiments", "prediction_upload"]
+ORDER = [
+    "basics",
+    "exports",
+    "project_configuration",
+    "annotation_import",
+    "integrations",
+    "model_experiments",
+    "prediction_upload",
+]
 
 SDK_EXAMPLE_HEADER = """
 # Labelbox SDK Examples\n
@@ -140,9 +148,7 @@ def make_table(base: str) -> str:
             )
             pandas_dict["Google Colab"].append(
                 make_link(
-                    COLAB_TEMPLATE.format(
-                        filename="/".join(link.split("/"))
-                    ),
+                    COLAB_TEMPLATE.format(filename="/".join(link.split("/"))),
                     "https://colab.research.google.com/assets/colab-badge.svg",
                     "Colab",
                 )
diff --git a/examples/template.ipynb b/examples/template.ipynb
index 59f344b48..87683b942 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -1,149 +1,135 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Labelbox Example SDK Template\n",
-    "This notebook serves as a template to guide develop of Labelbox example notebooks. Review notebook [CONTRIBUTING.md](https://github.com/Labelbox/labelbox-python/blob/develop/examples/CONTRIBUTING.md) for more detailed information."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q \"labelbox[data]\"\n",
-    "# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n",
-    "# Include other installs in this block with similar format: \"%pip install -q <package name>\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import labelbox as lb\n",
-    "# Include other imports in this block try grouping similar installs together"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API key and client\n",
-    "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add your API key\n",
-    "API_KEY = None\n",
-    "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-    "client = lb.Client(api_key=API_KEY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Main section header\n",
-    "\n",
-    "* Include information on what section discusses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sub section header\n",
-    "\n",
-    "* Include information on what sub section discusses\n",
-    "* If parameters are needed specify they need included"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DATASET_ID = None\n",
-    "# Inline Parameters should be all caps with a default value typically None"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Notebook section with steps\n",
-    "\n",
-    "* For long winded processes step headers should be included"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step \"step number\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Include information on step"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### \"step number.sub step number\"\n",
-    "\n",
-    "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean up\n",
-    "Uncomment and run the cell below to optionally delete Labelbox objects created"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# batch.delete()\n",
-    "# project.delete()\n",
-    "# dataset.delete()\n",
-    "\n",
-    "## Include other objects that were created"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "db768cda",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "</td>"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cb5611d0",
+            "metadata": {},
+            "source": [
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
+                "\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "</td>"
+            ]
+        },
+        {
+            "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar installs together",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## API key and client\n",
+                "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Main section header\n",
+                "\n",
+                "* Include information on what section discusses"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Sub section header\n",
+                "\n",
+                "* Include information on what sub section discusses\n",
+                "* If parameters are needed specify they need included"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Notebook section with steps\n",
+                "\n",
+                "* For long winded processes step headers should be included"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "### Step \"step number\""
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "* Include information on step"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "#### \"step number.sub step number\"\n",
+                "\n",
+                "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": [
+                "## Clean up\n",
+                "Uncomment and run the cell below to optionally delete Labelbox objects created"
+            ],
+            "cell_type": "markdown"
+        },
+        {
+            "metadata": {},
+            "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
+            "cell_type": "code",
+            "outputs": [],
+            "execution_count": null
+        }
+    ]
+}
\ No newline at end of file

From d9f9ae75173d8b96eec0f4ead755ba7b14b63b48 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 14:44:09 +0000
Subject: [PATCH 45/80] :memo: README updated

---
 examples/README.md | 152 ++++++++++++++++++++++-----------------------
 1 file changed, 76 insertions(+), 76 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index 8322e2845..c342e10c3 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -17,29 +17,34 @@
   </thead>
   <tbody>
     <tr>
-      <td>Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Ontologies</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Export V1 to V2 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Data Rows</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Batches</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
     <tr>
       <td>Projects</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>User Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Custom Embeddings</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Data Row Metadata</td>
@@ -47,19 +52,14 @@
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Export V1 to V2 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Ontologies</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Basics</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Data Rows</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>User Management</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
@@ -129,14 +129,9 @@
   </thead>
   <tbody>
     <tr>
-      <td>Import Labeled Dataset Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Audio</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational LLM Data Generation</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Video</td>
@@ -149,24 +144,29 @@
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Tiled</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Audio</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Conversational</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
+    <tr>
+      <td>Import Labeled Dataset Image</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>PDF</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational LLM Data Generation</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Image</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>DICOM</td>
@@ -174,9 +174,9 @@
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational LLM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>HTML</td>
@@ -184,9 +184,9 @@
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Conversational LLM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Tiled</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
@@ -203,14 +203,9 @@
   </thead>
   <tbody>
     <tr>
-      <td>Meta SAM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Meta SAM Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Huggingface Custom Embeddings</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Langchain</td>
@@ -218,9 +213,14 @@
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>Huggingface Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Meta SAM Video</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>Meta SAM</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
 </table>
@@ -236,6 +236,11 @@
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <td>Custom Metrics Demo</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>Model Slices</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
@@ -246,11 +251,6 @@
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>Custom Metrics Demo</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
     <tr>
       <td>Model Predictions to Project</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
@@ -270,46 +270,46 @@
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <td>PDF Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
+    <tr>
+      <td>HTML Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>Conversational Predictions</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
+    <tr>
+      <td>Image Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>Text Predictions</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>HTML Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-    <tr>
-      <td>Conversational LLM Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
     <tr>
       <td>Geospatial Predictions</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
-      <td>PDF Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+      <td>Conversational LLM Predictions</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Video Predictions</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>Image Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
   </tbody>
 </table>
 

From 1c3d4288e053fbead4e455a5d065f8b00b235df6 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Mon, 20 May 2024 09:48:38 -0500
Subject: [PATCH 46/80] modifed readme language

---
 examples/CONTRIBUTING.md | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index 6114782ac..9ea2d1d39 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -1,12 +1,11 @@
 # Contribution Guide
 
-Thank you for expressing your interest in contributing to our notebook examples! To ensure that your contribution aligns with our guidelines, please carefully review the following guide.
+Thank you for contributing to our notebook examples! To ensure that your contribution aligns with our guidelines, please carefully review the following guide.
 
 ## Table of Contents
 
 - [General Guidelines](#general-guidelines)
 - [Branches and Tags](#branches-and-tags)
-- [Release Steps](#release-steps)
 - [Github Workflows](#github-workflows)
 - [General Prerequisites](#general-prerequisites)
 - [Styling Tools](#styling-tools)
@@ -23,10 +22,6 @@ Thank you for expressing your interest in contributing to our notebook examples!
 * Approved PRs are merged to the `develop` branch.
 * All releases align to a git tag.
 
-## Release Steps
-
-Your contributions will be released as soon as it is approved and merged into the `develop` branch. Please consult the [Labelbox](https://docs.labelbox.com/docs/contacting-customer-support) team for if you need a more specific time frame of when your contributions may be released.
-
 ## Github Workflows
 
 * Github Branch Workflow
@@ -35,7 +30,7 @@ Your contributions will be released as soon as it is approved and merged into th
 
 ## General Prerequisites
 
-[Rye](https://rye-up.com/) may be installed before contributing to the repository as it is the tool used to style our example notebooks. This could be used to avoid the github styling workflow. This is also the packaging tool used for the main SDK. The pyproject used for the example notebooks is a virtual package and does not get published. Rye is used for the sack of consistency.
+[Rye](https://rye-up.com/) may be installed before contributing to the repository as it is the tool used to style our example notebooks. This could be used to avoid the github styling workflow. This is also the packaging tool used for the main SDK. The pyproject used for the example notebooks is a virtual package and does not get published. 
 
 ## Styling Tools
 

From cf8391b1ddcfc7f32dcd86b43b57159f96d65293 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Mon, 20 May 2024 09:57:32 -0500
Subject: [PATCH 47/80] modifed contrubuting guide

---
 examples/CONTRIBUTING.md | 29 ++++++++++++-----------------
 1 file changed, 12 insertions(+), 17 deletions(-)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index 9ea2d1d39..0887e4095 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -4,17 +4,24 @@ Thank you for contributing to our notebook examples! To ensure that your contrib
 
 ## Table of Contents
 
-- [General Guidelines](#general-guidelines)
+- [General Notebook Requirements](#general-notebook-requirements)
 - [Branches and Tags](#branches-and-tags)
 - [Github Workflows](#github-workflows)
 - [General Prerequisites](#general-prerequisites)
 - [Styling Tools](#styling-tools)
-- [General Notebook Requirements](#general-notebook-requirements)
 
-## General Guidelines
+# General Notebook Requirements
+
+Review our [template notebook](template.ipynbs) for general overview on how notebooks should be structure. This notebook and section just serves as a guide and exception can be made. Here are our general requirements:
 
-* Ensure that any modified notebooks run when edited.
-* Ensure that you update any relevant headers and comments within the code block you may add or change.
+1. Ensure that any modified notebooks run when edited.
+2. Ensure that you update any relevant headers and comments within the code block you may add or change.
+3. Notebooks should start with a top header below the Labelbox and link icons with the title of the notebook as a main header "#" and a overview of what the notebook shows.
+4. Use "labelbox[data]" over labelbox for installs to ensure you have the correct dependencies.
+5. Imports and installs should come after the main header under a Setup section
+6. Labelbox and other platforms with clients and API keys should be specified under a single section.
+7. Subsections need a second level header "##" and an overview of the section.
+8. The last cell should be a clean up section to delete any labelbox objects created.
 
 ## Branches and Tags
 
@@ -39,15 +46,3 @@ Rye is setup in this directory to use a customs script that will run the noteboo
 * `rye sync` in the examples folder to install the correct dev dependencies.
 * `rye run clean` runs a series of formatting tools.
 * `rye run create-readme` creates a readme based off our notebooks.
-
-# General Notebook Requirements
-
-Review our [template notebook](https://github.com/Labelbox/labelbox-python/blob/develop/examples/template.ipynb) for general overview on how notebooks should be structure. This notebook and section just serves as a guide and exception can be made. Here are our general requirements:
- 
-1. Notebooks should start with a top header below the Labelbox and link icons with the title of the notebook as a main header "#" and a overview of what the notebook shows
-2. Use "labelbox[data]" over labelbox for installs to ensure you have the correct dependencies.
-3. Imports and installs should come after the main header under a Setup section
-4. Labelbox and other platform with clients API keys should be specified under a single section.
-5. Subsections need a second level header "##".
-6. Subsections includes an overview of the section does any links to our docs.
-7. The last cell should be a clean up section to delete any labelbox objects created.

From f7a9e50d4bf424b52ac8bf85b1bd7387e4af67b2 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Mon, 20 May 2024 10:31:34 -0500
Subject: [PATCH 48/80] added important section

---
 examples/CONTRIBUTING.md                      |  23 +-
 .../huggingface_custom_embeddings.ipynb       | 211 +++++++++++++-----
 examples/template.ipynb                       | 116 ++++++----
 3 files changed, 237 insertions(+), 113 deletions(-)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index 0887e4095..834200fcb 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -10,7 +10,7 @@ Thank you for contributing to our notebook examples! To ensure that your contrib
 - [General Prerequisites](#general-prerequisites)
 - [Styling Tools](#styling-tools)
 
-# General Notebook Requirements
+## General Notebook Requirements
 
 Review our [template notebook](template.ipynbs) for general overview on how notebooks should be structure. This notebook and section just serves as a guide and exception can be made. Here are our general requirements:
 
@@ -23,17 +23,20 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 7. Subsections need a second level header "##" and an overview of the section.
 8. The last cell should be a clean up section to delete any labelbox objects created.
 
+> [!IMPORTANT]
+> Please make sure to remove any API keys before pushing changes
+
 ## Branches and Tags
 
-* All development happens in feature branches ideally prefixed by contributor's initials. For example `fs/feature_name`.
-* Approved PRs are merged to the `develop` branch.
-* All releases align to a git tag.
+- All development happens in feature branches ideally prefixed by contributor's initials. For example `fs/feature_name`.
+- Approved PRs are merged to the `develop` branch.
+- All releases align to a git tag.
 
 ## Github Workflows
 
-* Github Branch Workflow
-  * When you push to a branch that contains files inside the examples directory, it will automatically reformat your notebook to match our given style and provide appropriate headers. Once this workflow is completed it will commit back to your branch which then you can then pull.
-  * If your push contains new notebooks or modifies the names of notebooks the readme will be updated to reflect the change with updated links
+- Github Branch Workflow
+  - When you push to a branch that contains files inside the examples directory, it will automatically reformat your notebook to match our given style and provide appropriate headers. Once this workflow is completed it will commit back to your branch which then you can then pull.
+  - If your push contains new notebooks or modifies the names of notebooks the readme will be updated to reflect the change with updated links
 
 ## General Prerequisites
 
@@ -43,6 +46,6 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 
 Rye is setup in this directory to use a customs script that will run the notebooks through our formatting tools and create readmes.
 
-* `rye sync` in the examples folder to install the correct dev dependencies.
-* `rye run clean` runs a series of formatting tools.
-* `rye run create-readme` creates a readme based off our notebooks.
+- `rye sync` in the examples folder to install the correct dev dependencies.
+- `rye run clean` runs a series of formatting tools.
+- `rye run create-readme` creates a readme based off our notebooks.
diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index 29d6acdf2..70866ca70 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,135 +15,233 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Install required libraries"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox\"\n%pip install -q transformers",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install -q \"labelbox[data]\"\n",
+                "%pip install -q transformers"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Imports"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "import transformers\n",
+                "\n",
+                "transformers.logging.set_verbosity(50)\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from PIL import Image\n",
+                "import requests\n",
+                "from tqdm import tqdm\n",
+                "import numpy as np"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Labelbox Credentials"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Add your API key\n",
+                "API_KEY = \"\"\n",
+                "client = lb.Client(API_KEY)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Select data rows in Labelbox for custom embeddings"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Get images from a Labelbox dataset,\n",
+                "# Ensure the images are available by obtaining a token from your cloud provider if necessary\n",
+                "DATASET_ID = \"\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "dataset = client.get_dataset(DATASET_ID)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "dataset = client.get_dataset(DATASET_ID)"
+            ]
         },
         {
-            "metadata": {},
-            "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "export_task = dataset.export_v2()\n",
+                "\n",
+                "export_task.wait_till_done()\n",
+                "if export_task.errors:\n",
+                "    print(export_task.errors)\n",
+                "export_json = export_task.result\n",
+                "\n",
+                "data_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Get a HuggingFace Model to generate custom embeddings"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Get ResNet-50 from HuggingFace\n",
+                "image_processor = transformers.AutoImageProcessor.from_pretrained(\n",
+                "    \"microsoft/resnet-50\")\n",
+                "model = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\n",
+                "new_custom_embedding_id = client.create_embedding(\n",
+                "    name=\"My new awesome embedding\", dims=2048).id\n",
+                "\n",
+                "# Or use an existing embedding from your workspace\n",
+                "# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Generate and upload custom embeddings"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "img_emb = []\n",
+                "\n",
+                "for url in tqdm(data_row_urls):\n",
+                "    try:\n",
+                "        response = requests.get(url, stream=True)\n",
+                "        if response.status_code == 200:\n",
+                "            # Open the image, convert to RGB, and resize to 224x224\n",
+                "            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n",
+                "\n",
+                "            # Preprocess the image for model input\n",
+                "            img_hf = image_processor(image, return_tensors=\"pt\")\n",
+                "\n",
+                "            # Pass the image through the model to get embeddings\n",
+                "            with torch.no_grad():\n",
+                "                last_layer = model(**img_hf,\n",
+                "                                   output_hidden_states=True).last_hidden_state\n",
+                "                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n",
+                "                resnet_embeddings = torch.flatten(resnet_embeddings,\n",
+                "                                                  start_dim=1,\n",
+                "                                                  end_dim=3)\n",
+                "                img_emb.append(resnet_embeddings.cpu().numpy())\n",
+                "        else:\n",
+                "            continue\n",
+                "    except Exception as e:\n",
+                "        print(f\"Error processing URL: {url}. Exception: {e}\")\n",
+                "        continue\n",
+                "\n",
+                "data_rows = []\n",
+                "\n",
+                "# Create data rows payload to send to a dataset\n",
+                "for url, embedding in tqdm(zip(data_row_urls, img_emb)):\n",
+                "    data_rows.append({\n",
+                "        \"row_data\":\n",
+                "            url,\n",
+                "        \"embeddings\": [{\n",
+                "            \"embedding_id\": new_custom_embedding_id,\n",
+                "            \"vector\": embedding[0].tolist(),\n",
+                "        }],\n",
+                "    })"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Upload to a new dataset\n",
+                "dataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n",
+                "                                iam_integration=None)\n",
+                "task = dataset.create_data_rows(data_rows)\n",
+                "print(task.errors)"
+            ]
         }
-    ]
-}
\ No newline at end of file
+    ],
+    "metadata": {
+        "language_info": {
+            "name": "python"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 0
+}
diff --git a/examples/template.ipynb b/examples/template.ipynb
index 87683b942..ae523c619 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -1,15 +1,12 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+                "<td>\n",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
                 "</td>"
             ]
         },
@@ -18,118 +15,147 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "<td>\n",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+                "</td>\n",
                 "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "<td>\n",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
         {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "%pip install -q \"labelbox[data]\"\n",
+                "# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n",
+                "# Include other installs in this block with similar format: \"%pip install -q <package name>\""
+            ]
         },
         {
-            "metadata": {},
-            "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar installs together",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "import labelbox as lb\n",
+                "\n",
+                "# Include other imports in this block try grouping similar imports together"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## API key and client\n",
                 "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# Add your API key\n",
+                "API_KEY = None\n",
+                "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
+                "client = lb.Client(api_key=API_KEY)"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Main section header\n",
                 "\n",
                 "* Include information on what section discusses"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Sub section header\n",
                 "\n",
                 "* Include information on what sub section discusses\n",
                 "* If parameters are needed specify they need included"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "DATASET_ID = None\n",
+                "# Inline Parameters should be all caps with a default value typically None"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Notebook section with steps\n",
                 "\n",
                 "* For long winded processes step headers should be included"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Step \"step number\""
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Include information on step"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### \"step number.sub step number\"\n",
                 "\n",
                 "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
+            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Clean up\n",
                 "Uncomment and run the cell below to optionally delete Labelbox objects created"
-            ],
-            "cell_type": "markdown"
+            ]
         },
         {
-            "metadata": {},
-            "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
             "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
             "outputs": [],
-            "execution_count": null
+            "source": [
+                "# batch.delete()\n",
+                "# project.delete()\n",
+                "# dataset.delete()\n",
+                "\n",
+                "## Include other objects that were created"
+            ]
         }
-    ]
-}
\ No newline at end of file
+    ],
+    "metadata": {
+        "language_info": {
+            "name": "python"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}

From fb8f7d2db3d8b22e5fcd75fd78808cb7b43353e2 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 May 2024 15:32:45 +0000
Subject: [PATCH 49/80] :art: Cleaned

---
 .../huggingface_custom_embeddings.ipynb       | 211 +++++-------------
 examples/template.ipynb                       | 116 ++++------
 2 files changed, 103 insertions(+), 224 deletions(-)

diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index 70866ca70..667e1d4a1 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 0,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,233 +18,135 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Install required libraries"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q transformers",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install -q \"labelbox[data]\"\n",
-                "%pip install -q transformers"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Imports"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "import transformers\n",
-                "\n",
-                "transformers.logging.set_verbosity(50)\n",
-                "import torch\n",
-                "import torch.nn.functional as F\n",
-                "from PIL import Image\n",
-                "import requests\n",
-                "from tqdm import tqdm\n",
-                "import numpy as np"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Labelbox Credentials"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Add your API key\n",
-                "API_KEY = \"\"\n",
-                "client = lb.Client(API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Select data rows in Labelbox for custom embeddings"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Get images from a Labelbox dataset,\n",
-                "# Ensure the images are available by obtaining a token from your cloud provider if necessary\n",
-                "DATASET_ID = \"\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "dataset = client.get_dataset(DATASET_ID)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "dataset = client.get_dataset(DATASET_ID)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "export_task = dataset.export_v2()\n",
-                "\n",
-                "export_task.wait_till_done()\n",
-                "if export_task.errors:\n",
-                "    print(export_task.errors)\n",
-                "export_json = export_task.result\n",
-                "\n",
-                "data_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Get a HuggingFace Model to generate custom embeddings"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Get ResNet-50 from HuggingFace\n",
-                "image_processor = transformers.AutoImageProcessor.from_pretrained(\n",
-                "    \"microsoft/resnet-50\")\n",
-                "model = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\n",
-                "new_custom_embedding_id = client.create_embedding(\n",
-                "    name=\"My new awesome embedding\", dims=2048).id\n",
-                "\n",
-                "# Or use an existing embedding from your workspace\n",
-                "# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "# Generate and upload custom embeddings"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "img_emb = []\n",
-                "\n",
-                "for url in tqdm(data_row_urls):\n",
-                "    try:\n",
-                "        response = requests.get(url, stream=True)\n",
-                "        if response.status_code == 200:\n",
-                "            # Open the image, convert to RGB, and resize to 224x224\n",
-                "            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n",
-                "\n",
-                "            # Preprocess the image for model input\n",
-                "            img_hf = image_processor(image, return_tensors=\"pt\")\n",
-                "\n",
-                "            # Pass the image through the model to get embeddings\n",
-                "            with torch.no_grad():\n",
-                "                last_layer = model(**img_hf,\n",
-                "                                   output_hidden_states=True).last_hidden_state\n",
-                "                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n",
-                "                resnet_embeddings = torch.flatten(resnet_embeddings,\n",
-                "                                                  start_dim=1,\n",
-                "                                                  end_dim=3)\n",
-                "                img_emb.append(resnet_embeddings.cpu().numpy())\n",
-                "        else:\n",
-                "            continue\n",
-                "    except Exception as e:\n",
-                "        print(f\"Error processing URL: {url}. Exception: {e}\")\n",
-                "        continue\n",
-                "\n",
-                "data_rows = []\n",
-                "\n",
-                "# Create data rows payload to send to a dataset\n",
-                "for url, embedding in tqdm(zip(data_row_urls, img_emb)):\n",
-                "    data_rows.append({\n",
-                "        \"row_data\":\n",
-                "            url,\n",
-                "        \"embeddings\": [{\n",
-                "            \"embedding_id\": new_custom_embedding_id,\n",
-                "            \"vector\": embedding[0].tolist(),\n",
-                "        }],\n",
-                "    })"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Upload to a new dataset\n",
-                "dataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n",
-                "                                iam_integration=None)\n",
-                "task = dataset.create_data_rows(data_rows)\n",
-                "print(task.errors)"
-            ]
+            "execution_count": null
         }
-    ],
-    "metadata": {
-        "language_info": {
-            "name": "python"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 0
-}
+    ]
+}
\ No newline at end of file
diff --git a/examples/template.ipynb b/examples/template.ipynb
index ae523c619..416bb963f 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -1,12 +1,15 @@
 {
+    "nbformat": 4,
+    "nbformat_minor": 2,
+    "metadata": {},
     "cells": [
         {
             "cell_type": "markdown",
             "id": "db768cda",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>\n",
+                "<td>",
+                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
                 "</td>"
             ]
         },
@@ -15,147 +18,118 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-                "</td>\n",
+                "<td>",
+                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
+                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
+                "</td>",
                 "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+                "<td>",
+                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
                 "</td>"
             ]
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "%pip install -q \"labelbox[data]\"\n",
-                "# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n",
-                "# Include other installs in this block with similar format: \"%pip install -q <package name>\""
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar imports together",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "import labelbox as lb\n",
-                "\n",
-                "# Include other imports in this block try grouping similar imports together"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## API key and client\n",
                 "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# Add your API key\n",
-                "API_KEY = None\n",
-                "# To get your API key go to: Workspace settings -> API -> Create API Key\n",
-                "client = lb.Client(api_key=API_KEY)"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Main section header\n",
                 "\n",
                 "* Include information on what section discusses"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Sub section header\n",
                 "\n",
                 "* Include information on what sub section discusses\n",
                 "* If parameters are needed specify they need included"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "DATASET_ID = None\n",
-                "# Inline Parameters should be all caps with a default value typically None"
-            ]
+            "execution_count": null
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Notebook section with steps\n",
                 "\n",
                 "* For long winded processes step headers should be included"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "### Step \"step number\""
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "* Include information on step"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "#### \"step number.sub step number\"\n",
                 "\n",
                 "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "markdown",
             "metadata": {},
             "source": [
                 "## Clean up\n",
                 "Uncomment and run the cell below to optionally delete Labelbox objects created"
-            ]
+            ],
+            "cell_type": "markdown"
         },
         {
-            "cell_type": "code",
-            "execution_count": null,
             "metadata": {},
+            "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
+            "cell_type": "code",
             "outputs": [],
-            "source": [
-                "# batch.delete()\n",
-                "# project.delete()\n",
-                "# dataset.delete()\n",
-                "\n",
-                "## Include other objects that were created"
-            ]
+            "execution_count": null
         }
-    ],
-    "metadata": {
-        "language_info": {
-            "name": "python"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
+    ]
+}
\ No newline at end of file

From 30077c76deea64a41ef794be26cc58c8e053ea2c Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Mon, 20 May 2024 10:34:19 -0500
Subject: [PATCH 50/80] added emphasis on readme

---
 examples/CONTRIBUTING.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index 834200fcb..b35577ac6 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -16,11 +16,11 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 
 1. Ensure that any modified notebooks run when edited.
 2. Ensure that you update any relevant headers and comments within the code block you may add or change.
-3. Notebooks should start with a top header below the Labelbox and link icons with the title of the notebook as a main header "#" and a overview of what the notebook shows.
+3. Notebooks should start with a top header below the Labelbox and link icons with the title of the notebook as a main header **#** and a overview of what the notebook shows.
 4. Use "labelbox[data]" over labelbox for installs to ensure you have the correct dependencies.
-5. Imports and installs should come after the main header under a Setup section
+5. Imports and installs should come after the main header under a **Setup** section.
 6. Labelbox and other platforms with clients and API keys should be specified under a single section.
-7. Subsections need a second level header "##" and an overview of the section.
+7. Subsections need a second level header **##** and an overview of the section.
 8. The last cell should be a clean up section to delete any labelbox objects created.
 
 > [!IMPORTANT]
@@ -36,11 +36,11 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 
 - Github Branch Workflow
   - When you push to a branch that contains files inside the examples directory, it will automatically reformat your notebook to match our given style and provide appropriate headers. Once this workflow is completed it will commit back to your branch which then you can then pull.
-  - If your push contains new notebooks or modifies the names of notebooks the readme will be updated to reflect the change with updated links
+  - If your push contains new notebooks or modifies the names of notebooks the readme will be updated to reflect the change with updated links.
 
 ## General Prerequisites
 
-[Rye](https://rye-up.com/) may be installed before contributing to the repository as it is the tool used to style our example notebooks. This could be used to avoid the github styling workflow. This is also the packaging tool used for the main SDK. The pyproject used for the example notebooks is a virtual package and does not get published. 
+[Rye](https://rye-up.com/) may be installed before contributing to the repository as it is the tool used to style our example notebooks. This could be used to avoid the github styling workflow. This is also the packaging tool used for the main SDK. The pyproject used for the example notebooks is a virtual package and does not get published.
 
 ## Styling Tools
 

From ec6d088c819b1c4a516db07a97d66f03642e801e Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Mon, 20 May 2024 10:37:45 -0500
Subject: [PATCH 51/80] fixed typos

---
 examples/CONTRIBUTING.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index b35577ac6..d63c3fced 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -12,7 +12,7 @@ Thank you for contributing to our notebook examples! To ensure that your contrib
 
 ## General Notebook Requirements
 
-Review our [template notebook](template.ipynbs) for general overview on how notebooks should be structure. This notebook and section just serves as a guide and exception can be made. Here are our general requirements:
+Review our [template notebook](template.ipynbs) for general overview on how notebooks should be structure. This notebook and section just serves as a guide and exceptions can be made. Here are our general requirements:
 
 1. Ensure that any modified notebooks run when edited.
 2. Ensure that you update any relevant headers and comments within the code block you may add or change.
@@ -35,7 +35,7 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 ## Github Workflows
 
 - Github Branch Workflow
-  - When you push to a branch that contains files inside the examples directory, it will automatically reformat your notebook to match our given style and provide appropriate headers. Once this workflow is completed it will commit back to your branch which then you can then pull.
+  - When you push to a branch that contains files inside the examples directory, it will automatically reformat your notebook to match our given style and provide appropriate headers. Once this workflow is completed it will commit back to your branch which then you can pull.
   - If your push contains new notebooks or modifies the names of notebooks the readme will be updated to reflect the change with updated links.
 
 ## General Prerequisites

From 529fb1145dfc2d04e0dc5f191c10072c136ed6de Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Tue, 21 May 2024 06:36:27 -0500
Subject: [PATCH 52/80] added readme workflow

---
 .github/workflows/rdme-custompages.yml | 25 +++++++++++++++++++++++++
 examples/scripts/generate_readme.py    | 17 +++++------------
 2 files changed, 30 insertions(+), 12 deletions(-)
 create mode 100644 .github/workflows/rdme-custompages.yml

diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
new file mode 100644
index 000000000..0a7ed4dc7
--- /dev/null
+++ b/.github/workflows/rdme-custompages.yml
@@ -0,0 +1,25 @@
+name: ReadMe GitHub Action 🦉
+
+on:
+  push:
+    branches: [develop]
+    paths:
+    - examples/README.md
+  pull_request:
+    branches: [develop]
+    paths:
+      - examples/README.md
+
+jobs:
+  rdme-custompages:
+    runs-on: ubuntu-latest
+    steps:
+        uses: actions/checkout@v4
+        uses: ./.github/actions/notebooks
+      - name: Create readme
+        working-directory: examples
+        run: rye run create-doc-readme
+      - name: Run `custompages` command
+        uses: readmeio/rdme@v8
+        with:
+          rdme: custompages examples/tutorials.html --key=${{ secrets.README_API_KEY }}
diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 5c286dde2..f480fab71 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -10,15 +10,7 @@
 
 IGNORE = ["template.ipynb"]
 
-ORDER = [
-    "basics",
-    "exports",
-    "project_configuration",
-    "annotation_import",
-    "integrations",
-    "model_experiments",
-    "prediction_upload",
-]
+ORDER = ["basics", "exports", "project_configuration", "annotation_import", "integrations", "model_experiments", "prediction_upload"]
 
 SDK_EXAMPLE_HEADER = """
 # Labelbox SDK Examples\n
@@ -117,7 +109,6 @@ def make_links_dict(links: str):
             split_link = link.split("/")[0]
             link_dict[split_link].append(link)
     link_dict["Extras"] = extra_links
-    pprint(link_dict)
     return link_dict
 
 
@@ -140,7 +131,7 @@ def make_table(base: str) -> str:
             pandas_dict["Github"].append(
                 make_link(
                     GITHUB_TEMPLATE.format(
-                        filename="/".join(link.split("/")[1:])
+                        filename="/".join(link.split("/"))
                     ),
                     "https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white",
                     "Github",
@@ -148,7 +139,9 @@ def make_table(base: str) -> str:
             )
             pandas_dict["Google Colab"].append(
                 make_link(
-                    COLAB_TEMPLATE.format(filename="/".join(link.split("/"))),
+                    COLAB_TEMPLATE.format(
+                        filename="/".join(link.split("/"))
+                    ),
                     "https://colab.research.google.com/assets/colab-badge.svg",
                     "Colab",
                 )

From af92ebf5eb5da00c7d1770961c549c71bf171aea Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Tue, 21 May 2024 11:37:31 +0000
Subject: [PATCH 53/80] :art: Cleaned

---
 examples/scripts/generate_readme.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index f480fab71..1c3ff283b 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -10,7 +10,15 @@
 
 IGNORE = ["template.ipynb"]
 
-ORDER = ["basics", "exports", "project_configuration", "annotation_import", "integrations", "model_experiments", "prediction_upload"]
+ORDER = [
+    "basics",
+    "exports",
+    "project_configuration",
+    "annotation_import",
+    "integrations",
+    "model_experiments",
+    "prediction_upload",
+]
 
 SDK_EXAMPLE_HEADER = """
 # Labelbox SDK Examples\n
@@ -130,18 +138,14 @@ def make_table(base: str) -> str:
             pandas_dict["Notebook"].append(create_title(link))
             pandas_dict["Github"].append(
                 make_link(
-                    GITHUB_TEMPLATE.format(
-                        filename="/".join(link.split("/"))
-                    ),
+                    GITHUB_TEMPLATE.format(filename="/".join(link.split("/"))),
                     "https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white",
                     "Github",
                 )
             )
             pandas_dict["Google Colab"].append(
                 make_link(
-                    COLAB_TEMPLATE.format(
-                        filename="/".join(link.split("/"))
-                    ),
+                    COLAB_TEMPLATE.format(filename="/".join(link.split("/"))),
                     "https://colab.research.google.com/assets/colab-badge.svg",
                     "Colab",
                 )

From b296197aadf2a7096c2d5951190417e90078aa01 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Tue, 21 May 2024 06:38:39 -0500
Subject: [PATCH 54/80] fixed workflow

---
 .github/workflows/rdme-custompages.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index 0a7ed4dc7..7fd800dcf 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -11,7 +11,7 @@ on:
       - examples/README.md
 
 jobs:
-  rdme-custompages:
+  custompages:
     runs-on: ubuntu-latest
     steps:
         uses: actions/checkout@v4

From 568f741a654c40d0b2efff7e629d941dc705458a Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Tue, 21 May 2024 11:40:24 +0000
Subject: [PATCH 55/80] :memo: README updated

---
 examples/README.md | 88 +++++++++++++++++++++++-----------------------
 1 file changed, 44 insertions(+), 44 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index c342e10c3..6e4f8e749 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -18,47 +18,47 @@
   <tbody>
     <tr>
       <td>Ontologies</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Export V1 to V2 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Data Rows</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Batches</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Projects</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Data Row Metadata</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/data_row_metadata.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>User Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -77,12 +77,12 @@
   <tbody>
     <tr>
       <td>Composite Mask Export</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Export Data</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/export_data.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -101,17 +101,17 @@
   <tbody>
     <tr>
       <td>Project Setup</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_setup.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Webhooks</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/webhooks.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Queue Management</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/queue_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -130,62 +130,62 @@
   <tbody>
     <tr>
       <td>Conversational LLM Data Generation</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Text</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Audio</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Conversational</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Import Labeled Dataset Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>PDF</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Image</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>DICOM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/dicom.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Conversational LLM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>HTML</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Tiled</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -204,22 +204,22 @@
   <tbody>
     <tr>
       <td>Huggingface Custom Embeddings</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Langchain</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/langchain/langchain.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Meta SAM Video</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Meta SAM</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -238,22 +238,22 @@
   <tbody>
     <tr>
       <td>Custom Metrics Demo</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Model Slices</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Custom Metrics Basics</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Model Predictions to Project</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -272,42 +272,42 @@
   <tbody>
     <tr>
       <td>PDF Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>HTML Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Conversational Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Image Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Text Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Geospatial Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Conversational LLM Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
     <tr>
       <td>Video Predictions</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -326,7 +326,7 @@
   <tbody>
     <tr>
       <td>Object Detection</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/object_detection.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>
@@ -345,7 +345,7 @@
   <tbody>
     <tr>
       <td>Classification Confusion Matrix</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
   </tbody>

From ea2db31ccd242d1fd815f42a633134c21c4ae9f7 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Tue, 21 May 2024 06:42:57 -0500
Subject: [PATCH 56/80] fix generator

---
 .github/workflows/rdme-custompages.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index 7fd800dcf..4338dc071 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -4,7 +4,7 @@ on:
   push:
     branches: [develop]
     paths:
-    - examples/README.md
+      - examples/README.md
   pull_request:
     branches: [develop]
     paths:
@@ -14,8 +14,8 @@ jobs:
   custompages:
     runs-on: ubuntu-latest
     steps:
-        uses: actions/checkout@v4
-        uses: ./.github/actions/notebooks
+      - uses: actions/checkout@v4
+      - uses: ./.github/actions/notebooks
       - name: Create readme
         working-directory: examples
         run: rye run create-doc-readme

From 9acf6cb142e5e7a86700af50fbea922c6ef541eb Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Tue, 21 May 2024 06:49:14 -0500
Subject: [PATCH 57/80] set workflow to merge on pr close

---
 .github/workflows/rdme-custompages.yml | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index 4338dc071..73a2bed4f 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -1,17 +1,16 @@
 name: ReadMe GitHub Action 🦉
 
 on:
-  push:
-    branches: [develop]
-    paths:
-      - examples/README.md
   pull_request:
     branches: [develop]
+    types:
+      - closed
     paths:
       - examples/README.md
 
 jobs:
   custompages:
+    if: github.event.pull_request.merged == true
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From d9678df3b9cd0d19f43bed5de26f3f58c19635f4 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 10:02:36 -0500
Subject: [PATCH 58/80] modified format notebook

---
 .github/workflows/notebooks.yml                           | 1 +
 examples/annotation_import/audio.ipynb                    | 8 ++++----
 examples/annotation_import/conversational.ipynb           | 8 ++++----
 examples/annotation_import/conversational_LLM.ipynb       | 8 ++++----
 .../conversational_LLM_data_generation.ipynb              | 8 ++++----
 examples/annotation_import/dicom.ipynb                    | 8 ++++----
 examples/annotation_import/html.ipynb                     | 8 ++++----
 examples/annotation_import/image.ipynb                    | 8 ++++----
 .../annotation_import/import_labeled_dataset_image.ipynb  | 8 ++++----
 examples/annotation_import/pdf.ipynb                      | 8 ++++----
 examples/annotation_import/text.ipynb                     | 8 ++++----
 examples/annotation_import/tiled.ipynb                    | 8 ++++----
 examples/annotation_import/video.ipynb                    | 8 ++++----
 examples/basics/basics.ipynb                              | 8 ++++----
 examples/basics/batches.ipynb                             | 8 ++++----
 examples/basics/custom_embeddings.ipynb                   | 8 ++++----
 examples/basics/data_row_metadata.ipynb                   | 8 ++++----
 examples/basics/data_rows.ipynb                           | 8 ++++----
 examples/basics/export_v1_to_v2_migration_support.ipynb   | 8 ++++----
 examples/basics/ontologies.ipynb                          | 8 ++++----
 examples/basics/projects.ipynb                            | 8 ++++----
 examples/basics/user_management.ipynb                     | 8 ++++----
 examples/exports/composite_mask_export.ipynb              | 8 ++++----
 examples/exports/export_data.ipynb                        | 8 ++++----
 examples/extras/classification_confusion_matrix.ipynb     | 8 ++++----
 examples/foundry/object_detection.ipynb                   | 8 ++++----
 .../huggingface/huggingface_custom_embeddings.ipynb       | 8 ++++----
 examples/integrations/langchain/langchain.ipynb           | 8 ++++----
 examples/integrations/sam/meta_sam.ipynb                  | 8 ++++----
 examples/integrations/sam/meta_sam_video.ipynb            | 8 ++++----
 examples/model_experiments/custom_metrics_basics.ipynb    | 8 ++++----
 examples/model_experiments/custom_metrics_demo.ipynb      | 8 ++++----
 .../model_experiments/model_predictions_to_project.ipynb  | 8 ++++----
 examples/model_experiments/model_slices.ipynb             | 8 ++++----
 .../conversational_LLM_predictions.ipynb                  | 8 ++++----
 .../prediction_upload/conversational_predictions.ipynb    | 8 ++++----
 examples/prediction_upload/geospatial_predictions.ipynb   | 8 ++++----
 examples/prediction_upload/html_predictions.ipynb         | 8 ++++----
 examples/prediction_upload/image_predictions.ipynb        | 8 ++++----
 examples/prediction_upload/pdf_predictions.ipynb          | 8 ++++----
 examples/prediction_upload/text_predictions.ipynb         | 8 ++++----
 examples/prediction_upload/video_predictions.ipynb        | 8 ++++----
 examples/project_configuration/project_setup.ipynb        | 8 ++++----
 examples/project_configuration/queue_management.ipynb     | 8 ++++----
 examples/project_configuration/webhooks.ipynb             | 8 ++++----
 examples/scripts/format_notebooks.py                      | 8 ++++----
 examples/template.ipynb                                   | 8 ++++----
 47 files changed, 185 insertions(+), 184 deletions(-)

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index 950f4bea0..53339848b 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -54,6 +54,7 @@ jobs:
             - added|deleted: 'examples/**/*.ipynb'
   create:
     needs: changes
+    if: github.event.pull_request.merged == false
     if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
     runs-on: ubuntu-latest
     steps:
diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb
index babc5b9ce..1f29e2934 100644
--- a/examples/annotation_import/audio.ipynb
+++ b/examples/annotation_import/audio.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/conversational.ipynb b/examples/annotation_import/conversational.ipynb
index 139b6e494..65adcc857 100644
--- a/examples/annotation_import/conversational.ipynb
+++ b/examples/annotation_import/conversational.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index 9f0339fad..90b773956 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/conversational_LLM_data_generation.ipynb b/examples/annotation_import/conversational_LLM_data_generation.ipynb
index f620a3a8d..c89ca35b4 100644
--- a/examples/annotation_import/conversational_LLM_data_generation.ipynb
+++ b/examples/annotation_import/conversational_LLM_data_generation.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/dicom.ipynb b/examples/annotation_import/dicom.ipynb
index 2b9640955..19bd7d2ba 100644
--- a/examples/annotation_import/dicom.ipynb
+++ b/examples/annotation_import/dicom.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/html.ipynb b/examples/annotation_import/html.ipynb
index d4bcc1b9b..7faac2f30 100644
--- a/examples/annotation_import/html.ipynb
+++ b/examples/annotation_import/html.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/image.ipynb b/examples/annotation_import/image.ipynb
index aaccd029d..abbf869fe 100644
--- a/examples/annotation_import/image.ipynb
+++ b/examples/annotation_import/image.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/import_labeled_dataset_image.ipynb b/examples/annotation_import/import_labeled_dataset_image.ipynb
index e049f2da4..f8077588d 100644
--- a/examples/annotation_import/import_labeled_dataset_image.ipynb
+++ b/examples/annotation_import/import_labeled_dataset_image.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/pdf.ipynb b/examples/annotation_import/pdf.ipynb
index efd00aff2..4113a62b4 100644
--- a/examples/annotation_import/pdf.ipynb
+++ b/examples/annotation_import/pdf.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/text.ipynb b/examples/annotation_import/text.ipynb
index 74691ad78..31d643579 100644
--- a/examples/annotation_import/text.ipynb
+++ b/examples/annotation_import/text.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 5aa68ef0c..bef8c9f2b 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/annotation_import/video.ipynb b/examples/annotation_import/video.ipynb
index 266197bdf..e40ac5efd 100644
--- a/examples/annotation_import/video.ipynb
+++ b/examples/annotation_import/video.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index cb2dd6230..3588e8bcf 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index dc62d4a7a..852f0c5e7 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/custom_embeddings.ipynb b/examples/basics/custom_embeddings.ipynb
index 65f4952d6..15b63cc35 100644
--- a/examples/basics/custom_embeddings.ipynb
+++ b/examples/basics/custom_embeddings.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/data_row_metadata.ipynb b/examples/basics/data_row_metadata.ipynb
index 9c9f64987..5e15c0a93 100644
--- a/examples/basics/data_row_metadata.ipynb
+++ b/examples/basics/data_row_metadata.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/data_rows.ipynb b/examples/basics/data_rows.ipynb
index c240b0d30..def791228 100644
--- a/examples/basics/data_rows.ipynb
+++ b/examples/basics/data_rows.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
index c56513435..11073ccf4 100644
--- a/examples/basics/export_v1_to_v2_migration_support.ipynb
+++ b/examples/basics/export_v1_to_v2_migration_support.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/ontologies.ipynb b/examples/basics/ontologies.ipynb
index 6937bbe9c..ca1447252 100644
--- a/examples/basics/ontologies.ipynb
+++ b/examples/basics/ontologies.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index 53dbb3bc0..79fe66336 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index 5e028e049..ad1db4bf2 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/exports/composite_mask_export.ipynb b/examples/exports/composite_mask_export.ipynb
index 9b37c4305..5c848da2b 100644
--- a/examples/exports/composite_mask_export.ipynb
+++ b/examples/exports/composite_mask_export.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/exports/export_data.ipynb b/examples/exports/export_data.ipynb
index 0d897709c..6b5820e7e 100644
--- a/examples/exports/export_data.ipynb
+++ b/examples/exports/export_data.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
index f21c8436f..b366c3931 100644
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ b/examples/extras/classification_confusion_matrix.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/foundry/object_detection.ipynb b/examples/foundry/object_detection.ipynb
index a477d2541..d1a6a6144 100644
--- a/examples/foundry/object_detection.ipynb
+++ b/examples/foundry/object_detection.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index 667e1d4a1..c2647cd87 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/integrations/langchain/langchain.ipynb b/examples/integrations/langchain/langchain.ipynb
index 60c9fb099..d07681a63 100644
--- a/examples/integrations/langchain/langchain.ipynb
+++ b/examples/integrations/langchain/langchain.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/integrations/sam/meta_sam.ipynb b/examples/integrations/sam/meta_sam.ipynb
index 96c1ef9a0..1f2f9b788 100644
--- a/examples/integrations/sam/meta_sam.ipynb
+++ b/examples/integrations/sam/meta_sam.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/integrations/sam/meta_sam_video.ipynb b/examples/integrations/sam/meta_sam_video.ipynb
index 45e425c36..b928cbb9b 100644
--- a/examples/integrations/sam/meta_sam_video.ipynb
+++ b/examples/integrations/sam/meta_sam_video.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/model_experiments/custom_metrics_basics.ipynb b/examples/model_experiments/custom_metrics_basics.ipynb
index 26a4b8d0f..9b1c78f58 100644
--- a/examples/model_experiments/custom_metrics_basics.ipynb
+++ b/examples/model_experiments/custom_metrics_basics.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/model_experiments/custom_metrics_demo.ipynb b/examples/model_experiments/custom_metrics_demo.ipynb
index e6de146b9..54be24129 100644
--- a/examples/model_experiments/custom_metrics_demo.ipynb
+++ b/examples/model_experiments/custom_metrics_demo.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/model_experiments/model_predictions_to_project.ipynb b/examples/model_experiments/model_predictions_to_project.ipynb
index 9cb07e3b4..542bdef53 100644
--- a/examples/model_experiments/model_predictions_to_project.ipynb
+++ b/examples/model_experiments/model_predictions_to_project.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/model_experiments/model_slices.ipynb b/examples/model_experiments/model_slices.ipynb
index 87c760328..b4e96c4b9 100644
--- a/examples/model_experiments/model_slices.ipynb
+++ b/examples/model_experiments/model_slices.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index b2de76f56..006035826 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/conversational_predictions.ipynb b/examples/prediction_upload/conversational_predictions.ipynb
index c2063cc8d..3f82a49a9 100644
--- a/examples/prediction_upload/conversational_predictions.ipynb
+++ b/examples/prediction_upload/conversational_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/geospatial_predictions.ipynb b/examples/prediction_upload/geospatial_predictions.ipynb
index f0125193f..5cc985ee4 100644
--- a/examples/prediction_upload/geospatial_predictions.ipynb
+++ b/examples/prediction_upload/geospatial_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/html_predictions.ipynb b/examples/prediction_upload/html_predictions.ipynb
index f5b4bb94f..f9627dcff 100644
--- a/examples/prediction_upload/html_predictions.ipynb
+++ b/examples/prediction_upload/html_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/image_predictions.ipynb b/examples/prediction_upload/image_predictions.ipynb
index b0ce9a97f..d3158884a 100644
--- a/examples/prediction_upload/image_predictions.ipynb
+++ b/examples/prediction_upload/image_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/pdf_predictions.ipynb b/examples/prediction_upload/pdf_predictions.ipynb
index a07642a63..a10d5ad94 100644
--- a/examples/prediction_upload/pdf_predictions.ipynb
+++ b/examples/prediction_upload/pdf_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/text_predictions.ipynb b/examples/prediction_upload/text_predictions.ipynb
index 4d80894f9..1616d7341 100644
--- a/examples/prediction_upload/text_predictions.ipynb
+++ b/examples/prediction_upload/text_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/prediction_upload/video_predictions.ipynb b/examples/prediction_upload/video_predictions.ipynb
index 0899ee332..2315b01cc 100644
--- a/examples/prediction_upload/video_predictions.ipynb
+++ b/examples/prediction_upload/video_predictions.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index d35aa8067..7b119d8a0 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/project_configuration/queue_management.ipynb b/examples/project_configuration/queue_management.ipynb
index fd5fad0dc..7d0c7938f 100644
--- a/examples/project_configuration/queue_management.ipynb
+++ b/examples/project_configuration/queue_management.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index 1ed5ec118..4cb3da344 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index aafa0d6de..5833cd200 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -24,14 +24,14 @@
     "id": "cb5611d0",
     "metadata": {},
     "source": [
-        "<td>",
+        "<td>\n",
         '<a href="{colab}" target="_blank"><img',
         'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>',
-        "</td>",
+        "</td>\n",
         "\n",
-        "<td>",
+        "<td>\n",
         '<a href="{github}" target="_blank"><img',
-        'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>',
+        'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>\n',
         "</td>",
     ],
 }
diff --git a/examples/template.ipynb b/examples/template.ipynb
index 416bb963f..290c262a4 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -18,14 +18,14 @@
             "id": "cb5611d0",
             "metadata": {},
             "source": [
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
                 "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
+                "</td>\n",
                 "\n",
-                "<td>",
+                "<td>\n",
                 "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
+                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
                 "</td>"
             ]
         },

From 06f2aaf38bd42fb5a09c508e21e10e7cd80be604 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 10:08:27 -0500
Subject: [PATCH 59/80] modified files again

---
 examples/annotation_import/audio.ipynb        |  596 +++++----
 .../annotation_import/conversational.ipynb    |  628 +++++----
 .../conversational_LLM.ipynb                  |  614 +++++----
 .../conversational_LLM_data_generation.ipynb  |  536 ++++----
 examples/annotation_import/dicom.ipynb        |  530 ++++----
 examples/annotation_import/html.ipynb         |  610 +++++----
 examples/annotation_import/image.ipynb        |  868 ++++++------
 .../import_labeled_dataset_image.ipynb        |  368 +++--
 examples/annotation_import/pdf.ipynb          |  768 ++++++-----
 examples/annotation_import/text.ipynb         |  630 +++++----
 examples/annotation_import/tiled.ipynb        |  688 +++++-----
 examples/annotation_import/video.ipynb        |  812 ++++++------
 examples/basics/basics.ipynb                  |  368 +++--
 examples/basics/batches.ipynb                 |  596 +++++----
 examples/basics/custom_embeddings.ipynb       |  570 ++++----
 examples/basics/data_row_metadata.ipynb       |  598 +++++----
 examples/basics/data_rows.ipynb               |  628 +++++----
 .../export_v1_to_v2_migration_support.ipynb   | 1180 ++++++++---------
 examples/basics/ontologies.ipynb              |  602 +++++----
 examples/basics/projects.ipynb                |  680 +++++-----
 examples/basics/user_management.ipynb         |  490 ++++---
 examples/exports/composite_mask_export.ipynb  |  508 ++++---
 examples/exports/export_data.ipynb            |  952 +++++++------
 .../classification_confusion_matrix.ipynb     |  212 ++-
 examples/foundry/object_detection.ipynb       |  514 ++++---
 .../huggingface_custom_embeddings.ipynb       |  298 +++--
 .../integrations/langchain/langchain.ipynb    |  350 +++--
 examples/integrations/sam/meta_sam.ipynb      |  496 ++++---
 .../integrations/sam/meta_sam_video.ipynb     |  464 ++++---
 .../custom_metrics_basics.ipynb               |  508 ++++---
 .../custom_metrics_demo.ipynb                 |  846 ++++++------
 .../model_predictions_to_project.ipynb        |  538 ++++----
 examples/model_experiments/model_slices.ipynb |  488 ++++---
 .../conversational_LLM_predictions.ipynb      |  770 ++++++-----
 .../conversational_predictions.ipynb          |  712 +++++-----
 .../geospatial_predictions.ipynb              |  756 ++++++-----
 .../prediction_upload/html_predictions.ipynb  |  672 +++++-----
 .../prediction_upload/image_predictions.ipynb |  940 +++++++------
 .../prediction_upload/pdf_predictions.ipynb   |  838 ++++++------
 .../prediction_upload/text_predictions.ipynb  |  690 +++++-----
 .../prediction_upload/video_predictions.ipynb |  750 ++++++-----
 .../project_configuration/project_setup.ipynb |  350 +++--
 .../queue_management.ipynb                    |  410 +++---
 examples/project_configuration/webhooks.ipynb |  418 +++---
 examples/pyproject.toml                       |    2 +-
 examples/scripts/format_notebooks.py          |    7 +-
 examples/template.ipynb                       |  264 ++--
 47 files changed, 13511 insertions(+), 13602 deletions(-)

diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb
index 1f29e2934..356554ba1 100644
--- a/examples/annotation_import/audio.ipynb
+++ b/examples/annotation_import/audio.ipynb
@@ -1,301 +1,299 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Audio Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n",
-                "\n",
-                "Suported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Bouding box\n",
-                "* NER\n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask\n",
-                "\n",
-                "MAL and Label Import:\n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for Audio"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_audio\",\n    value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_audio\",\n    \"answer\": \"free text audio annotation\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_audio\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_audio\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_audio\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_audio\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT,\n                      name=\"text_audio\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_audio\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_audio\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology Audio Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Audio,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n                                media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-audio-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[text_annotation, checklist_annotation, radio_annotation],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations \n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Audio Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Suported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Bouding box\n",
+        "* NER\n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask\n",
+        "\n",
+        "MAL and Label Import:\n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for Audio"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_audio\",\n    value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_audio\",\n    \"answer\": \"free text audio annotation\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_audio\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_audio\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_audio\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_audio\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT,\n                      name=\"text_audio\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_audio\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_audio\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology Audio Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Audio,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n                                media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-audio-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[text_annotation, checklist_annotation, radio_annotation],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations \n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational.ipynb b/examples/annotation_import/conversational.ipynb
index 65adcc857..54bfbc820 100644
--- a/examples/annotation_import/conversational.ipynb
+++ b/examples/annotation_import/conversational.ipynb
@@ -1,317 +1,315 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Conversational Text Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for conversational text assets, and also  cover MAL and Label Import methods:\n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "* NER\n",
-                "\n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Relationships\n",
-                "* Bouding box \n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask \n",
-                "\n",
-                "MAL and Label Import:\n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# message based classifications\nner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n    message_id=\"0\",\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions right here\",\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"2\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the text annotation, we provided the `name` as `text_convo`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_convo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. However,for conversational texts NDJSON is the only supported format. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations \n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Conversational Text Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for conversational text assets, and also  cover MAL and Label Import methods:\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "* NER\n",
+        "\n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Relationships\n",
+        "* Bouding box \n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask \n",
+        "\n",
+        "MAL and Label Import:\n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for conversational text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# message based classifications\nner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n    message_id=\"0\",\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions right here\",\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"2\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the text annotation, we provided the `name` as `text_convo`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_convo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. However,for conversational texts NDJSON is the only supported format. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations \n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index 90b773956..01efccd21 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -1,310 +1,308 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
-                "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Set up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Entity "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
-                "\n",
-                "```\n",
-                "\"modelOutputs\" : [\n",
-                "  {\n",
-                "      \"title\": \"Name of the response option\",\n",
-                "      \"content\": \"Content of the response\",\n",
-                "      \"modelConfigName\": \"Name of model configuration\"\n",
-                "  }\n",
-                "]\n",
-                "```\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Example of row_data with model outputs"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Python annotation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "NDJSON annotation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload annotations to a project as pre-labels or complete labels "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Model Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
+        "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Set up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Supported annotations for conversational text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Entity "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
+        "\n",
+        "```\n",
+        "\"modelOutputs\" : [\n",
+        "  {\n",
+        "      \"title\": \"Name of the response option\",\n",
+        "      \"content\": \"Content of the response\",\n",
+        "      \"modelConfigName\": \"Name of model configuration\"\n",
+        "  }\n",
+        "]\n",
+        "```\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Example of row_data with model outputs"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Python annotation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "NDJSON annotation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload annotations to a project as pre-labels or complete labels "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Model Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational_LLM_data_generation.ipynb b/examples/annotation_import/conversational_LLM_data_generation.ipynb
index c89ca35b4..d5d03d749 100644
--- a/examples/annotation_import/conversational_LLM_data_generation.ipynb
+++ b/examples/annotation_import/conversational_LLM_data_generation.ipynb
@@ -1,271 +1,269 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LLM Data Generation with MAL and Ground Truth\n",
-                "This demo is meant to showcase how to generate prompts and responses to fine-tune large language models (LLMs) using MAL and Ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Set up "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for LLM data generation\n",
-                "Currently, we only support NDJson format for prompt and responses"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Prompt:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "prompt_annotation_ndjson = {\n    \"name\": \"Follow the prompt and select answers\",\n    \"answer\": \"This is an example of a prompt\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Responses:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "response_radio_annotation_ndjson = {\n    \"name\": \"response_radio\",\n    \"answer\": {\n        \"name\": \"response_a\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Only NDJson is currently supported\nresponse_text_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is an example of a response text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "response_checklist_annotation_ndjson = {\n    \"name\": \"response_checklist\",\n    \"answer\": [{\n        \"name\": \"response_a\"\n    }, {\n        \"name\": \"response_c\"\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Create a project and data rows in Labelbox UI\n",
-                "\n",
-                "Currently we do not support this workflow through the SDK.\n",
-                "#### Workflow:\n",
-                "\n",
-                "1. Navigate to annotate and select ***New project***\n",
-                "\n",
-                "2. Select ***LLM data generation*** and then select ***Humans generate prompts and responses***\n",
-                "\n",
-                "3. Name your project, select ***create a new dataset*** and name your dataset. (data rows will be generated automatically in \n",
-                "this step)\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Enter the project id\nproject_id = \"\"\n\n# Select one of the global keys from the data rows generated\nglobal_key = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2 : Create/select an Ontology in Labelbox UI"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Currently we do not support this workflow through the SDK\n",
-                "#### Workflow: \n",
-                "1. In your project, navigate to ***Settings*** and ***Label editor***\n",
-                "\n",
-                "2. Click on ***Edit***\n",
-                "\n",
-                "3. Create a new ontology and add the features used in this demo\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### For this demo the following ontology was generated in the UI: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_json = \"\"\"\n{\n \"tools\": [],\n \"relationships\": [],\n \"classifications\": [\n  {\n   \"schemaNodeId\": \"clpvq9d0002yt07zy0khq42rp\",\n   \"featureSchemaId\": \"clpvq9d0002ys07zyf2eo9p14\",\n   \"type\": \"prompt\",\n   \"name\": \"Follow the prompt and select answers\",\n   \"archived\": false,\n   \"required\": true,\n   \"options\": [],\n   \"instructions\": \"Follow the prompt and select answers\",\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002yz07zy0fjg28z7\",\n   \"featureSchemaId\": \"clpvq9d0002yu07zy28ik5w3i\",\n   \"type\": \"response-radio\",\n   \"name\": \"response_radio\",\n   \"instructions\": \"response_radio\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0002yw07zyci2q5adq\",\n     \"featureSchemaId\": \"clpvq9d0002yv07zyevmz1yoj\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0002yy07zy8pe48zdj\",\n     \"featureSchemaId\": \"clpvq9d0002yx07zy0jvmdxk8\",\n     \"value\": \"response_b\",\n     \"label\": \"response_b\",\n     \"position\": 1,\n     \"options\": []\n    }\n   ]\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002z107zygf8l62ys\",\n   \"featureSchemaId\": \"clpvq9d0002z007zyg26115f9\",\n   \"type\": \"response-text\",\n   \"name\": \"provide_a_reason_for_your_choice\",\n   \"instructions\": \"Provide a reason for your choice\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [],\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0102z907zy8b10hjcj\",\n   \"featureSchemaId\": \"clpvq9d0002z207zy6xla7f82\",\n   \"type\": \"response-checklist\",\n   \"name\": \"response_checklist\",\n   \"instructions\": \"response_checklist\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0102z407zy0adq0rfr\",\n     \"featureSchemaId\": \"clpvq9d0002z307zy6dqb8xsw\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z607zych8b2z5d\",\n     \"featureSchemaId\": \"clpvq9d0102z507zyfwfgacrn\",\n     \"value\": \"response_c\",\n     \"label\": \"response_c\",\n     \"position\": 1,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z807zy03y7gysp\",\n     \"featureSchemaId\": \"clpvq9d0102z707zyh61y5o3u\",\n     \"value\": \"response_d\",\n     \"label\": \"response_d\",\n     \"position\": 2,\n     \"options\": []\n    }\n   ]\n  }\n ],\n \"realTime\": false\n}\n\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        prompt_annotation_ndjson,\n        response_radio_annotation_ndjson,\n        response_text_annotation_ndjson,\n        response_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.get_project(project_id=project_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LLM Data Generation with MAL and Ground Truth\n",
+        "This demo is meant to showcase how to generate prompts and responses to fine-tune large language models (LLMs) using MAL and Ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Set up "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for LLM data generation\n",
+        "Currently, we only support NDJson format for prompt and responses"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Prompt:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "prompt_annotation_ndjson = {\n    \"name\": \"Follow the prompt and select answers\",\n    \"answer\": \"This is an example of a prompt\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Responses:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "response_radio_annotation_ndjson = {\n    \"name\": \"response_radio\",\n    \"answer\": {\n        \"name\": \"response_a\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Only NDJson is currently supported\nresponse_text_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is an example of a response text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "response_checklist_annotation_ndjson = {\n    \"name\": \"response_checklist\",\n    \"answer\": [{\n        \"name\": \"response_a\"\n    }, {\n        \"name\": \"response_c\"\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Create a project and data rows in Labelbox UI\n",
+        "\n",
+        "Currently we do not support this workflow through the SDK.\n",
+        "#### Workflow:\n",
+        "\n",
+        "1. Navigate to annotate and select ***New project***\n",
+        "\n",
+        "2. Select ***LLM data generation*** and then select ***Humans generate prompts and responses***\n",
+        "\n",
+        "3. Name your project, select ***create a new dataset*** and name your dataset. (data rows will be generated automatically in \n",
+        "this step)\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Enter the project id\nproject_id = \"\"\n\n# Select one of the global keys from the data rows generated\nglobal_key = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2 : Create/select an Ontology in Labelbox UI"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Currently we do not support this workflow through the SDK\n",
+        "#### Workflow: \n",
+        "1. In your project, navigate to ***Settings*** and ***Label editor***\n",
+        "\n",
+        "2. Click on ***Edit***\n",
+        "\n",
+        "3. Create a new ontology and add the features used in this demo\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### For this demo the following ontology was generated in the UI: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_json = \"\"\"\n{\n \"tools\": [],\n \"relationships\": [],\n \"classifications\": [\n  {\n   \"schemaNodeId\": \"clpvq9d0002yt07zy0khq42rp\",\n   \"featureSchemaId\": \"clpvq9d0002ys07zyf2eo9p14\",\n   \"type\": \"prompt\",\n   \"name\": \"Follow the prompt and select answers\",\n   \"archived\": false,\n   \"required\": true,\n   \"options\": [],\n   \"instructions\": \"Follow the prompt and select answers\",\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002yz07zy0fjg28z7\",\n   \"featureSchemaId\": \"clpvq9d0002yu07zy28ik5w3i\",\n   \"type\": \"response-radio\",\n   \"name\": \"response_radio\",\n   \"instructions\": \"response_radio\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0002yw07zyci2q5adq\",\n     \"featureSchemaId\": \"clpvq9d0002yv07zyevmz1yoj\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0002yy07zy8pe48zdj\",\n     \"featureSchemaId\": \"clpvq9d0002yx07zy0jvmdxk8\",\n     \"value\": \"response_b\",\n     \"label\": \"response_b\",\n     \"position\": 1,\n     \"options\": []\n    }\n   ]\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002z107zygf8l62ys\",\n   \"featureSchemaId\": \"clpvq9d0002z007zyg26115f9\",\n   \"type\": \"response-text\",\n   \"name\": \"provide_a_reason_for_your_choice\",\n   \"instructions\": \"Provide a reason for your choice\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [],\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0102z907zy8b10hjcj\",\n   \"featureSchemaId\": \"clpvq9d0002z207zy6xla7f82\",\n   \"type\": \"response-checklist\",\n   \"name\": \"response_checklist\",\n   \"instructions\": \"response_checklist\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0102z407zy0adq0rfr\",\n     \"featureSchemaId\": \"clpvq9d0002z307zy6dqb8xsw\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z607zych8b2z5d\",\n     \"featureSchemaId\": \"clpvq9d0102z507zyfwfgacrn\",\n     \"value\": \"response_c\",\n     \"label\": \"response_c\",\n     \"position\": 1,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z807zy03y7gysp\",\n     \"featureSchemaId\": \"clpvq9d0102z707zyh61y5o3u\",\n     \"value\": \"response_d\",\n     \"label\": \"response_d\",\n     \"position\": 2,\n     \"options\": []\n    }\n   ]\n  }\n ],\n \"realTime\": false\n}\n\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        prompt_annotation_ndjson,\n        response_radio_annotation_ndjson,\n        response_text_annotation_ndjson,\n        response_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.get_project(project_id=project_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/dicom.ipynb b/examples/annotation_import/dicom.ipynb
index 19bd7d2ba..301f68233 100644
--- a/examples/annotation_import/dicom.ipynb
+++ b/examples/annotation_import/dicom.ipynb
@@ -1,268 +1,266 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# DICOM Annotation Import\n",
-                "\n",
-                "* Annotations must be created and uploaded using NDJSON\n",
-                "* Supported annotations that can be uploaded through the SDK:\n",
-                "    * Polyline\n",
-                "    * Segmentation masks     \n",
-                "* **NOT** supported:\n",
-                "    * Bounding box\n",
-                "    * Point\n",
-                "    * Polygons\n",
-                "    * Free form text classifications\n",
-                "    * Radio classifications \n",
-                "    * Checklist classifications"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for DICOM\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######## Polyline ########\npolyline_annotation = [\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=1,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=20),\n            lb_types.Point(x=250, y=250),\n        ]),\n        segment_index=0,\n        keyframe=True,\n    ),\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=20,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=10),\n            lb_types.Point(x=300, y=300),\n        ]),\n        segment_index=1,\n        keyframe=True,\n    ),\n]\n\npolyline_annotation_ndjson = {\n    \"name\":\n        \"line_dicom\",\n    \"groupKey\":\n        \"axial\",  # should be 'axial', 'sagittal', or 'coronal'\n    \"segments\": [\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    1,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 20\n                    },\n                    {\n                        \"x\": 250,\n                        \"y\": 250\n                    },\n                ],\n            }]\n        },\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    20,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 300,\n                        \"y\": 300\n                    },\n                ],\n            }]\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Segmentation Masks ########\n\nmask_annotation = [\n    lb_types.DICOMMaskAnnotation(\n        group_key=\"axial\",\n        frames=[\n            lb_types.MaskFrame(\n                index=1,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n            lb_types.MaskFrame(\n                index=5,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n        ],\n        instances=[\n            lb_types.MaskInstance(color_rgb=(255, 255, 255),\n                                  name=\"segmentation_mask_dicom\")\n        ],\n    )\n]\n\nmask_annotation_ndjson = {\n    \"groupKey\": \"axial\",\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\":\n                    1,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n            {\n                \"index\":\n                    5,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": (255, 255, 255),\n            \"name\": \"segmentation_mask_dicom\"\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"sample-dicom-1.dcm\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-dicom-1.dcm\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"dicom_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors :\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the line annotation above, we provided the `name` as `line_dicom`. Now, when we setup our ontology, we must ensure that the name of my line tool is also `line_dicom`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-                "\n",
-                "\n",
-                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(\n        tool=lb.Tool.Type.RASTER_SEGMENTATION,\n        name=\"segmentation_mask_dicom\",\n    ),\n    lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_dicom\"),\n])\n\nontology = client.create_ontology(\n    \"Ontology DICOM Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Dicom,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project \n",
-                "Connect the ontology to the labeling project."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"dicom_project_demo\",\n                                media_type=lb.MediaType.Dicom)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create batches\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-dicom-demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # a list of data row objects, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload \n",
-                "Create the annotations payload using the snippets of code above.\n",
-                "\n",
-                "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python Annotation Types"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "annotations_list = polyline_annotation + mask_annotation\nlabels = [\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=annotations_list)\n]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations\n",
-                "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\n\nfor annotation in [polyline_annotation_ndjson, mask_annotation_ndjson]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
-                "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job-\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)\nprint(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# DICOM Annotation Import\n",
+        "\n",
+        "* Annotations must be created and uploaded using NDJSON\n",
+        "* Supported annotations that can be uploaded through the SDK:\n",
+        "    * Polyline\n",
+        "    * Segmentation masks     \n",
+        "* **NOT** supported:\n",
+        "    * Bounding box\n",
+        "    * Point\n",
+        "    * Polygons\n",
+        "    * Free form text classifications\n",
+        "    * Radio classifications \n",
+        "    * Checklist classifications"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for DICOM\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######## Polyline ########\npolyline_annotation = [\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=1,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=20),\n            lb_types.Point(x=250, y=250),\n        ]),\n        segment_index=0,\n        keyframe=True,\n    ),\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=20,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=10),\n            lb_types.Point(x=300, y=300),\n        ]),\n        segment_index=1,\n        keyframe=True,\n    ),\n]\n\npolyline_annotation_ndjson = {\n    \"name\":\n        \"line_dicom\",\n    \"groupKey\":\n        \"axial\",  # should be 'axial', 'sagittal', or 'coronal'\n    \"segments\": [\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    1,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 20\n                    },\n                    {\n                        \"x\": 250,\n                        \"y\": 250\n                    },\n                ],\n            }]\n        },\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    20,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 300,\n                        \"y\": 300\n                    },\n                ],\n            }]\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Segmentation Masks ########\n\nmask_annotation = [\n    lb_types.DICOMMaskAnnotation(\n        group_key=\"axial\",\n        frames=[\n            lb_types.MaskFrame(\n                index=1,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n            lb_types.MaskFrame(\n                index=5,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n        ],\n        instances=[\n            lb_types.MaskInstance(color_rgb=(255, 255, 255),\n                                  name=\"segmentation_mask_dicom\")\n        ],\n    )\n]\n\nmask_annotation_ndjson = {\n    \"groupKey\": \"axial\",\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\":\n                    1,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n            {\n                \"index\":\n                    5,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": (255, 255, 255),\n            \"name\": \"segmentation_mask_dicom\"\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"sample-dicom-1.dcm\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-dicom-1.dcm\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"dicom_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors :\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the line annotation above, we provided the `name` as `line_dicom`. Now, when we setup our ontology, we must ensure that the name of my line tool is also `line_dicom`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+        "\n",
+        "\n",
+        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(\n        tool=lb.Tool.Type.RASTER_SEGMENTATION,\n        name=\"segmentation_mask_dicom\",\n    ),\n    lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_dicom\"),\n])\n\nontology = client.create_ontology(\n    \"Ontology DICOM Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Dicom,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project \n",
+        "Connect the ontology to the labeling project."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"dicom_project_demo\",\n                                media_type=lb.MediaType.Dicom)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create batches\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-dicom-demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # a list of data row objects, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above.\n",
+        "\n",
+        "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python Annotation Types"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "annotations_list = polyline_annotation + mask_annotation\nlabels = [\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=annotations_list)\n]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations\n",
+        "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\n\nfor annotation in [polyline_annotation_ndjson, mask_annotation_ndjson]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
+        "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job-\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)\nprint(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/html.ipynb b/examples/annotation_import/html.ipynb
index 7faac2f30..20d38368c 100644
--- a/examples/annotation_import/html.ipynb
+++ b/examples/annotation_import/html.ipynb
@@ -1,308 +1,306 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# HTML Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for HTML assets, and also  cover MAL and Label Import methods:\n",
-                "\n",
-                "Suported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Bouding box\n",
-                "* NER\n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask\n",
-                "\n",
-                "MAL and Label Import:\n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for HTML"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_html\",\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_html\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_html\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_html\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_html\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_html\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample_html_1.html\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_1.html\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"html_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the text annotation, we provided the `name` as `text_html`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_html`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_html\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_html\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_html\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"nested_checklist_question\",\n        options=[\n            lb.Option(\n                \"first_checklist_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.CHECKLIST,\n                        name=\"sub_checklist_question\",\n                        options=[lb.Option(\"first_sub_checklist_answer\")],\n                    )\n                ],\n            )\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"nested_radio_question\",\n        options=[\n            lb.Option(\n                value=\"first_radio_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.RADIO,\n                        name=\"sub_radio_question\",\n                        options=[lb.Option(value=\"first_sub_radio_answer\")],\n                    ),\n                ],\n            )\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology HTML Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(name=\"HTML Import Annotation Demo\",\n                                media_type=lb.MediaType.Html)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-html-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations \n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# HTML Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for HTML assets, and also  cover MAL and Label Import methods:\n",
+        "\n",
+        "Suported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Bouding box\n",
+        "* NER\n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask\n",
+        "\n",
+        "MAL and Label Import:\n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for HTML"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_html\",\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_html\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_html\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_html\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_html\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_html\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample_html_1.html\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_1.html\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"html_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the text annotation, we provided the `name` as `text_html`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_html`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_html\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_html\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_html\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"nested_checklist_question\",\n        options=[\n            lb.Option(\n                \"first_checklist_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.CHECKLIST,\n                        name=\"sub_checklist_question\",\n                        options=[lb.Option(\"first_sub_checklist_answer\")],\n                    )\n                ],\n            )\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"nested_radio_question\",\n        options=[\n            lb.Option(\n                value=\"first_radio_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.RADIO,\n                        name=\"sub_radio_question\",\n                        options=[lb.Option(value=\"first_sub_radio_answer\")],\n                    ),\n                ],\n            )\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology HTML Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(name=\"HTML Import Annotation Demo\",\n                                media_type=lb.MediaType.Html)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-html-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations \n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/image.ipynb b/examples/annotation_import/image.ipynb
index abbf869fe..6d99c0e51 100644
--- a/examples/annotation_import/image.ipynb
+++ b/examples/annotation_import/image.ipynb
@@ -1,437 +1,435 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Image annotation import\n",
-                "This notebook will provide examples of each supported annotation type for image assets.\n",
-                "\n",
-                "### [Model-assisted labeling (MAL)](https://docs.labelbox.com/docs/model-assisted-labeling)\n",
-                "\n",
-                "* This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.\n",
-                "\n",
-                "The imported annotations will be pre-populated in the labeling editor. However, in order to convert the pre-labels to real annotations, a human labeler will still need to open the Data Row in the Editor and submit it. This functionality is designed to speed up human labeling.\n",
-                "\n",
-                "### [Import ground truth](https://docs.labelbox.com/docs/import-ground-truth)\n",
-                "\n",
-                "* This  workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.\n",
-                "\n",
-                "### Python annotation types vs NDJSON\n",
-                "**Python annotation type (recommended)**\n",
-                "- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.\n",
-                "\n",
-                "- Allows you to build annotations locally with local file paths, numpy arrays, or URLs\n",
-                "\n",
-                "- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox\n",
-                "\n",
-                "- It supports one-level nested classification (free text / radio / checklist) under the object or classification annotation.\n",
-                "\n",
-                "**NDJSON**\n",
-                "- Skip formatting annotation payload in the Python Annotation Types format just to convert back to NDJSON\n",
-                "\n",
-                "- Ability to create the payload in the NDJSON import format directly\n",
-                "\n",
-                "- It supports any levels of nested classification (free text / radio / checklist) under the object or classification annotation."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key\n",
-                "\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for image\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification : Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Relationship with bounding box\n",
-                "> **NOTE:**  \n",
-                "> Only supported for MAL imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2096, y=1264),\n        end=lb_types.Point(x=2240, y=1689),\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2272, y=1346),\n        end=lb_types.Point(x=2416, y=1704),\n    ),\n)\n\nrelationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"uuid\": uuid_source,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1264.0,\n        \"left\": 2096.0,\n        \"height\": 425.0,\n        \"width\": 144.0\n    },\n}\n\nbbox_target_ndjson = {\n    \"uuid\": uuid_target,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1346.0,\n        \"left\": 2272.0,\n        \"height\": 358.0,\n        \"width\": 144.0\n    },\n}\n\nrelationship_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with nested classification"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\")),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\"\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polygon"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",  # must match your ontology feature\"s name\n    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n        points=[\n            lb_types.Point(x=1489.581, y=183.934),\n            lb_types.Point(x=2278.306, y=256.885),\n            lb_types.Point(x=2428.197, y=200.437),\n            lb_types.Point(x=2560.0, y=335.419),\n            lb_types.Point(x=2557.386, y=503.165),\n            lb_types.Point(x=2320.596, y=503.103),\n            lb_types.Point(x=2156.083, y=628.943),\n            lb_types.Point(x=2161.111, y=785.519),\n            lb_types.Point(x=2002.115, y=894.647),\n            lb_types.Point(x=1838.456, y=877.874),\n            lb_types.Point(x=1436.53, y=874.636),\n            lb_types.Point(x=1411.403, y=758.579),\n            lb_types.Point(x=1353.853, y=751.74),\n            lb_types.Point(x=1345.264, y=453.461),\n            lb_types.Point(x=1426.011, y=421.129),\n        ]),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Composite mask upload using different mask tools from the project's ontology\n",
-                "This example shows how to assigned different annotations (mask instances) from a composite mask using different mask tools"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First we need to extract all the unique colors from the composite mask\ndef extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "cp_mask_url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/composite_mask.png\"\ncolors = extract_rgb_colors_from_url(cp_mask_url)\nresponse = requests.get(cp_mask_url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nrgb_colors_for_mask_with_text_subclass_tool = [\n    (73, 39, 85),\n    (111, 87, 176),\n    (23, 169, 254),\n]\n\ncp_mask = []\nfor color in colors:\n    # We are assigning the color related to the mask_with_text_subclass tool by identifying the unique RGB colors\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask.append(\n            lb_types.ObjectAnnotation(\n                name=\n                \"mask_with_text_subclass\",  # must match your ontology feature\"s name\n                value=lb_types.Mask(mask=mask_data, color=color),\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_free_text\",\n                        value=lb_types.Text(answer=\"free text answer sample\"),\n                    )\n                ],\n            ))\n    else:\n        # Create ObjectAnnotation for other masks\n        cp_mask.append(\n            lb_types.ObjectAnnotation(name=\"mask\",\n                                      value=lb_types.Mask(mask=mask_data,\n                                                          color=color)))\n\n# NDJSON using bytes array\ncp_mask_ndjson = []\n\n# Using bytes array.\nresponse = requests.get(cp_mask_url)\nim_bytes = base64.b64encode(response.content).decode(\"utf-8\")\nfor color in colors:\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask_ndjson.append({\n            \"name\":\n                \"mask_with_text_subclass\",\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n            \"classifications\": [{\n                \"name\": \"sub_free_text\",\n                \"answer\": \"free text answer\"\n            }],\n        })\n    else:\n        cp_mask_ndjson.append({\n            \"name\": \"mask\",\n            \"classifications\": [],\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n        })",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",  # must match your ontology feature\"s name\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point\",\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",  # must match your ontology feature\"s name\n    value=lb_types.Line(  # Coordinates for the keypoints in your polyline\n        points=[\n            lb_types.Point(x=2534.353, y=249.471),\n            lb_types.Point(x=2429.492, y=182.092),\n            lb_types.Point(x=2294.322, y=221.962),\n            lb_types.Point(x=2224.491, y=180.463),\n            lb_types.Point(x=2136.123, y=204.716),\n            lb_types.Point(x=1712.247, y=173.949),\n            lb_types.Point(x=1703.838, y=84.438),\n            lb_types.Point(x=1579.772, y=82.61),\n            lb_types.Point(x=1583.442, y=167.552),\n            lb_types.Point(x=1478.869, y=164.903),\n            lb_types.Point(x=1418.941, y=318.149),\n            lb_types.Point(x=1243.128, y=400.815),\n            lb_types.Point(x=1022.067, y=319.007),\n            lb_types.Point(x=892.367, y=379.216),\n            lb_types.Point(x=670.273, y=364.408),\n            lb_types.Point(x=613.114, y=288.16),\n            lb_types.Point(x=377.559, y=238.251),\n            lb_types.Point(x=368.087, y=185.064),\n            lb_types.Point(x=246.557, y=167.286),\n            lb_types.Point(x=236.648, y=285.61),\n            lb_types.Point(x=90.929, y=326.412),\n        ]),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# End-to-end example: Import pre-labels or ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into catalog\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"image-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(dataset)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we created the bounding box annotation above, we provided the `name` as `bounding_box`. Now, when we setup our ontology, we must ensure that the name of the bounding box tool is also `bounding_box`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"Image Annotation Import Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    \"image-demo-batch\",  # each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Python annotations\n",
-                "\n",
-                "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n    bbox_source,\n    bbox_target,\n    relationship,\n] + cp_mask\n\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations\n",
-                "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nannotations = [\n    radio_annotation_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    checklist_annotation_ndjson,\n    text_annotation_ndjson,\n    bbox_annotation_ndjson,\n    bbox_with_radio_subclass_ndjson,\n    polygon_annotation_ndjson,\n    point_annotation_ndjson,\n    polyline_annotation_ndjson,\n    bbox_source_ndjson,\n    bbox_target_ndjson,\n    relationship_ndjson,  ## Only supported for MAL imports\n] + cp_mask_ndjson\n\nfor annotation in annotations:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload annotations to a project as pre-labels or ground truth\n",
-                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option A: Upload to a labeling project as pre-labels (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# upload MAL labels for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=label,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option B: Upload to a labeling project using ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Relationships are not supported with LabelImport\n# For this demo either run MAL or Ground Truth, not both\n\n# Upload label for this data row in project\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=label)\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Image annotation import\n",
+        "This notebook will provide examples of each supported annotation type for image assets.\n",
+        "\n",
+        "### [Model-assisted labeling (MAL)](https://docs.labelbox.com/docs/model-assisted-labeling)\n",
+        "\n",
+        "* This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.\n",
+        "\n",
+        "The imported annotations will be pre-populated in the labeling editor. However, in order to convert the pre-labels to real annotations, a human labeler will still need to open the Data Row in the Editor and submit it. This functionality is designed to speed up human labeling.\n",
+        "\n",
+        "### [Import ground truth](https://docs.labelbox.com/docs/import-ground-truth)\n",
+        "\n",
+        "* This  workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.\n",
+        "\n",
+        "### Python annotation types vs NDJSON\n",
+        "**Python annotation type (recommended)**\n",
+        "- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.\n",
+        "\n",
+        "- Allows you to build annotations locally with local file paths, numpy arrays, or URLs\n",
+        "\n",
+        "- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox\n",
+        "\n",
+        "- It supports one-level nested classification (free text / radio / checklist) under the object or classification annotation.\n",
+        "\n",
+        "**NDJSON**\n",
+        "- Skip formatting annotation payload in the Python Annotation Types format just to convert back to NDJSON\n",
+        "\n",
+        "- Ability to create the payload in the NDJSON import format directly\n",
+        "\n",
+        "- It supports any levels of nested classification (free text / radio / checklist) under the object or classification annotation."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key\n",
+        "\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for image\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification : Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Relationship with bounding box\n",
+        "> **NOTE:**  \n",
+        "> Only supported for MAL imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2096, y=1264),\n        end=lb_types.Point(x=2240, y=1689),\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2272, y=1346),\n        end=lb_types.Point(x=2416, y=1704),\n    ),\n)\n\nrelationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"uuid\": uuid_source,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1264.0,\n        \"left\": 2096.0,\n        \"height\": 425.0,\n        \"width\": 144.0\n    },\n}\n\nbbox_target_ndjson = {\n    \"uuid\": uuid_target,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1346.0,\n        \"left\": 2272.0,\n        \"height\": 358.0,\n        \"width\": 144.0\n    },\n}\n\nrelationship_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with nested classification"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\")),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\"\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polygon"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",  # must match your ontology feature\"s name\n    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n        points=[\n            lb_types.Point(x=1489.581, y=183.934),\n            lb_types.Point(x=2278.306, y=256.885),\n            lb_types.Point(x=2428.197, y=200.437),\n            lb_types.Point(x=2560.0, y=335.419),\n            lb_types.Point(x=2557.386, y=503.165),\n            lb_types.Point(x=2320.596, y=503.103),\n            lb_types.Point(x=2156.083, y=628.943),\n            lb_types.Point(x=2161.111, y=785.519),\n            lb_types.Point(x=2002.115, y=894.647),\n            lb_types.Point(x=1838.456, y=877.874),\n            lb_types.Point(x=1436.53, y=874.636),\n            lb_types.Point(x=1411.403, y=758.579),\n            lb_types.Point(x=1353.853, y=751.74),\n            lb_types.Point(x=1345.264, y=453.461),\n            lb_types.Point(x=1426.011, y=421.129),\n        ]),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Composite mask upload using different mask tools from the project's ontology\n",
+        "This example shows how to assigned different annotations (mask instances) from a composite mask using different mask tools"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First we need to extract all the unique colors from the composite mask\ndef extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "cp_mask_url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/composite_mask.png\"\ncolors = extract_rgb_colors_from_url(cp_mask_url)\nresponse = requests.get(cp_mask_url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nrgb_colors_for_mask_with_text_subclass_tool = [\n    (73, 39, 85),\n    (111, 87, 176),\n    (23, 169, 254),\n]\n\ncp_mask = []\nfor color in colors:\n    # We are assigning the color related to the mask_with_text_subclass tool by identifying the unique RGB colors\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask.append(\n            lb_types.ObjectAnnotation(\n                name=\n                \"mask_with_text_subclass\",  # must match your ontology feature\"s name\n                value=lb_types.Mask(mask=mask_data, color=color),\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_free_text\",\n                        value=lb_types.Text(answer=\"free text answer sample\"),\n                    )\n                ],\n            ))\n    else:\n        # Create ObjectAnnotation for other masks\n        cp_mask.append(\n            lb_types.ObjectAnnotation(name=\"mask\",\n                                      value=lb_types.Mask(mask=mask_data,\n                                                          color=color)))\n\n# NDJSON using bytes array\ncp_mask_ndjson = []\n\n# Using bytes array.\nresponse = requests.get(cp_mask_url)\nim_bytes = base64.b64encode(response.content).decode(\"utf-8\")\nfor color in colors:\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask_ndjson.append({\n            \"name\":\n                \"mask_with_text_subclass\",\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n            \"classifications\": [{\n                \"name\": \"sub_free_text\",\n                \"answer\": \"free text answer\"\n            }],\n        })\n    else:\n        cp_mask_ndjson.append({\n            \"name\": \"mask\",\n            \"classifications\": [],\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n        })",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",  # must match your ontology feature\"s name\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point\",\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",  # must match your ontology feature\"s name\n    value=lb_types.Line(  # Coordinates for the keypoints in your polyline\n        points=[\n            lb_types.Point(x=2534.353, y=249.471),\n            lb_types.Point(x=2429.492, y=182.092),\n            lb_types.Point(x=2294.322, y=221.962),\n            lb_types.Point(x=2224.491, y=180.463),\n            lb_types.Point(x=2136.123, y=204.716),\n            lb_types.Point(x=1712.247, y=173.949),\n            lb_types.Point(x=1703.838, y=84.438),\n            lb_types.Point(x=1579.772, y=82.61),\n            lb_types.Point(x=1583.442, y=167.552),\n            lb_types.Point(x=1478.869, y=164.903),\n            lb_types.Point(x=1418.941, y=318.149),\n            lb_types.Point(x=1243.128, y=400.815),\n            lb_types.Point(x=1022.067, y=319.007),\n            lb_types.Point(x=892.367, y=379.216),\n            lb_types.Point(x=670.273, y=364.408),\n            lb_types.Point(x=613.114, y=288.16),\n            lb_types.Point(x=377.559, y=238.251),\n            lb_types.Point(x=368.087, y=185.064),\n            lb_types.Point(x=246.557, y=167.286),\n            lb_types.Point(x=236.648, y=285.61),\n            lb_types.Point(x=90.929, y=326.412),\n        ]),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# End-to-end example: Import pre-labels or ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into catalog\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"image-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(dataset)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we created the bounding box annotation above, we provided the `name` as `bounding_box`. Now, when we setup our ontology, we must ensure that the name of the bounding box tool is also `bounding_box`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"Image Annotation Import Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch = project.create_batch(\n    \"image-demo-batch\",  # each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Python annotations\n",
+        "\n",
+        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n    bbox_source,\n    bbox_target,\n    relationship,\n] + cp_mask\n\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations\n",
+        "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nannotations = [\n    radio_annotation_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    checklist_annotation_ndjson,\n    text_annotation_ndjson,\n    bbox_annotation_ndjson,\n    bbox_with_radio_subclass_ndjson,\n    polygon_annotation_ndjson,\n    point_annotation_ndjson,\n    polyline_annotation_ndjson,\n    bbox_source_ndjson,\n    bbox_target_ndjson,\n    relationship_ndjson,  ## Only supported for MAL imports\n] + cp_mask_ndjson\n\nfor annotation in annotations:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload annotations to a project as pre-labels or ground truth\n",
+        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option A: Upload to a labeling project as pre-labels (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# upload MAL labels for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=label,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option B: Upload to a labeling project using ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Relationships are not supported with LabelImport\n# For this demo either run MAL or Ground Truth, not both\n\n# Upload label for this data row in project\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=label)\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/import_labeled_dataset_image.ipynb b/examples/annotation_import/import_labeled_dataset_image.ipynb
index f8077588d..7b7fee951 100644
--- a/examples/annotation_import/import_labeled_dataset_image.ipynb
+++ b/examples/annotation_import/import_labeled_dataset_image.ipynb
@@ -1,187 +1,185 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.data_row_metadata import (\n    DataRowMetadataField,\n    DataRowMetadataKind,\n)\nimport datetime\nimport random\nimport os\nimport json\nfrom PIL import Image\nfrom labelbox.schema.ontology import OntologyBuilder, Tool\nimport requests\nfrom tqdm.notebook import tqdm\nimport uuid\nfrom labelbox.data.annotation_types import (\n    Label,\n    ImageData,\n    ObjectAnnotation,\n    Rectangle,\n    Point,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup Labelbox client"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Initialize the Labelbox client\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Download a public dataset\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Function to download files\ndef download_files(filemap):\n    path, uri = filemap\n    if not os.path.exists(path):\n        response = requests.get(uri, stream=True)\n        with open(path, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n    return path",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Download data rows and annotations\nDATA_ROWS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json\"\nANNOTATIONS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json\"\ndownload_files((\"data_rows.json\", DATA_ROWS_URL))\ndownload_files((\"annotations.json\", ANNOTATIONS_URL))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Load data rows and annotations\nwith open(\"data_rows.json\") as fp:\n    data_rows = json.load(fp)\nwith open(\"annotations.json\") as fp:\n    annotations = json.load(fp)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Create a dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new dataset\ndataset = client.create_dataset(name=\"Geospatial vessel detection\")\nprint(f\"Created dataset with ID: {dataset.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Import Data Rows with Metadata"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Here is an example of adding two metadata fields to your Data Rows: a \"captureDateTime\" field with datetime value, and a \"tag\" field with string value\nmetadata_ontology = client.get_data_row_metadata_ontology()\ndatetime_schema_id = metadata_ontology.reserved_by_name[\"captureDateTime\"].uid\ntag_schema_id = metadata_ontology.reserved_by_name[\"tag\"].uid\ntag_items = [\"WorldView-1\", \"WorldView-2\", \"WorldView-3\", \"WorldView-4\"]\n\nfor datarow in tqdm(data_rows):\n    dt = datetime.datetime.utcnow() + datetime.timedelta(\n        days=random.random() * 30)  # this is random datetime value\n    tag_item = random.choice(tag_items)  # this is a random tag value\n\n    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.\n    metadata_fields = [\n        DataRowMetadataField(schema_id=datetime_schema_id, value=dt),\n        DataRowMetadataField(schema_id=tag_schema_id, value=tag_item),\n    ]\n\n    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.\n    # metadata_fields = [\n    #                    {\"schema_id\": datetime_schema_id, \"value\": dt},\n    #                    {\"schema_id\": tag_schema_id, \"value\": tag_item}\n    #                    ]\n\n    datarow[\"metadata_fields\"] = metadata_fields",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "task = dataset.create_data_rows(data_rows)\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Examine a Data Row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "datarow = next(dataset.data_rows())\nprint(datarow)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup a labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Initialize the OntologyBuilder\nontology_builder = OntologyBuilder()\n\n# Assuming 'annotations' is defined and contains the necessary data\nfor category in annotations[\"categories\"]:\n    print(category[\"name\"])\n    # Add tools to the ontology builder\n    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category[\"name\"]))\n\n# Create the ontology in Labelbox\nontology = client.create_ontology(\n    \"Vessel Detection Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\nprint(f\"Created ontology with ID: {ontology.uid}\")\n\n# Create a project and set up the ontology\nproject = client.create_project(name=\"Vessel Detection\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology=ontology)\nprint(f\"Created project with ID: {project.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\n# Minimal ExportV2 parameters focused solely on data row IDs\nexport_params = {\"data_row_details\": True}  # Only export data row details\n\n# Initiate the streamable export task from catalog\ndataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID\nexport_task = dataset.export(params=export_params)\nexport_task.wait_till_done()\nprint(export_task)\n\ndata_rows = []\n\n\n# Callback used for JSON Converter to correctly collect data row IDs\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    # Parse the JSON string to access the data\n    data = json.loads(output.json_str)\n\n    # Correctly extract and append DataRow ID\n    if \"data_row\" in data and \"id\" in data[\"data_row\"]:\n        data_rows.append(data[\"data_row\"][\"id\"])\n\n\n# Process the stream if there are results\nif export_task.has_result():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.RESULT).start(\n                               stream_handler=json_stream_handler)\n\n# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)\nsampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))\n\n# Create a new batch in the project and add the sampled data rows\nbatch = project.create_batch(\n    \"Initial batch\",  # name of the batch\n    sampled_data_rows,  # list of Data Rows\n    1,  # priority between 1-5\n)\nprint(f\"Created batch with ID: {batch.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Create annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set export parameters focused on data row details\nexport_params = {\n    \"data_row_details\": True,  # Only export data row details\n    \"batch_ids\": [batch.uid\n                 ],  # Optional: Include batch ids to filter by specific batches\n}\n\n# Initialize the streamable export task from project\nexport_task = project.export(params=export_params)\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nlabels = []\nfor datarow in data_rows:\n    annotations_list = []\n    # Access the 'data_row' dictionary first\n    data_row_dict = datarow[\"data_row\"]\n    folder = data_row_dict[\"external_id\"].split(\"/\")[0]\n    id = data_row_dict[\"external_id\"].split(\"/\")[1]\n\n    if folder == \"positive_image_set\":\n        for image in annotations[\"images\"]:\n            if image[\"file_name\"] == id:\n                for annotation in annotations[\"annotations\"]:\n                    if annotation[\"image_id\"] == image[\"id\"]:\n                        bbox = annotation[\"bbox\"]\n                        category_id = annotation[\"category_id\"] - 1\n                        class_name = None\n                        ontology = (ontology_builder.asdict()\n                                   )  # Get the ontology dictionary\n                        for category in ontology[\"tools\"]:\n                            if (category[\"name\"] == annotations[\"categories\"]\n                                [category_id][\"name\"]):\n                                class_name = category[\"name\"]\n                                break\n                        if class_name:\n                            annotations_list.append(\n                                ObjectAnnotation(\n                                    name=class_name,\n                                    value=Rectangle(\n                                        start=Point(x=bbox[0], y=bbox[1]),\n                                        end=Point(\n                                            x=bbox[2] + bbox[0],\n                                            y=bbox[3] + bbox[1],\n                                        ),\n                                    ),\n                                ))\n    image_data = {\"uid\": data_row_dict[\"id\"]}\n    labels.append(Label(data=image_data, annotations=annotations_list))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"label_import_job_{str(uuid.uuid4())}\",\n    labels=labels,\n)\n\n# Wait for the upload to finish and print the results\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.data_row_metadata import (\n    DataRowMetadataField,\n    DataRowMetadataKind,\n)\nimport datetime\nimport random\nimport os\nimport json\nfrom PIL import Image\nfrom labelbox.schema.ontology import OntologyBuilder, Tool\nimport requests\nfrom tqdm.notebook import tqdm\nimport uuid\nfrom labelbox.data.annotation_types import (\n    Label,\n    ImageData,\n    ObjectAnnotation,\n    Rectangle,\n    Point,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup Labelbox client"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Initialize the Labelbox client\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Download a public dataset\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Function to download files\ndef download_files(filemap):\n    path, uri = filemap\n    if not os.path.exists(path):\n        response = requests.get(uri, stream=True)\n        with open(path, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n    return path",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Download data rows and annotations\nDATA_ROWS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json\"\nANNOTATIONS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json\"\ndownload_files((\"data_rows.json\", DATA_ROWS_URL))\ndownload_files((\"annotations.json\", ANNOTATIONS_URL))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Load data rows and annotations\nwith open(\"data_rows.json\") as fp:\n    data_rows = json.load(fp)\nwith open(\"annotations.json\") as fp:\n    annotations = json.load(fp)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create a dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new dataset\ndataset = client.create_dataset(name=\"Geospatial vessel detection\")\nprint(f\"Created dataset with ID: {dataset.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Import Data Rows with Metadata"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Here is an example of adding two metadata fields to your Data Rows: a \"captureDateTime\" field with datetime value, and a \"tag\" field with string value\nmetadata_ontology = client.get_data_row_metadata_ontology()\ndatetime_schema_id = metadata_ontology.reserved_by_name[\"captureDateTime\"].uid\ntag_schema_id = metadata_ontology.reserved_by_name[\"tag\"].uid\ntag_items = [\"WorldView-1\", \"WorldView-2\", \"WorldView-3\", \"WorldView-4\"]\n\nfor datarow in tqdm(data_rows):\n    dt = datetime.datetime.utcnow() + datetime.timedelta(\n        days=random.random() * 30)  # this is random datetime value\n    tag_item = random.choice(tag_items)  # this is a random tag value\n\n    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.\n    metadata_fields = [\n        DataRowMetadataField(schema_id=datetime_schema_id, value=dt),\n        DataRowMetadataField(schema_id=tag_schema_id, value=tag_item),\n    ]\n\n    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.\n    # metadata_fields = [\n    #                    {\"schema_id\": datetime_schema_id, \"value\": dt},\n    #                    {\"schema_id\": tag_schema_id, \"value\": tag_item}\n    #                    ]\n\n    datarow[\"metadata_fields\"] = metadata_fields",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "task = dataset.create_data_rows(data_rows)\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Examine a Data Row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "datarow = next(dataset.data_rows())\nprint(datarow)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup a labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Initialize the OntologyBuilder\nontology_builder = OntologyBuilder()\n\n# Assuming 'annotations' is defined and contains the necessary data\nfor category in annotations[\"categories\"]:\n    print(category[\"name\"])\n    # Add tools to the ontology builder\n    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category[\"name\"]))\n\n# Create the ontology in Labelbox\nontology = client.create_ontology(\n    \"Vessel Detection Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\nprint(f\"Created ontology with ID: {ontology.uid}\")\n\n# Create a project and set up the ontology\nproject = client.create_project(name=\"Vessel Detection\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology=ontology)\nprint(f\"Created project with ID: {project.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\n# Minimal ExportV2 parameters focused solely on data row IDs\nexport_params = {\"data_row_details\": True}  # Only export data row details\n\n# Initiate the streamable export task from catalog\ndataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID\nexport_task = dataset.export(params=export_params)\nexport_task.wait_till_done()\nprint(export_task)\n\ndata_rows = []\n\n\n# Callback used for JSON Converter to correctly collect data row IDs\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    # Parse the JSON string to access the data\n    data = json.loads(output.json_str)\n\n    # Correctly extract and append DataRow ID\n    if \"data_row\" in data and \"id\" in data[\"data_row\"]:\n        data_rows.append(data[\"data_row\"][\"id\"])\n\n\n# Process the stream if there are results\nif export_task.has_result():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.RESULT).start(\n                               stream_handler=json_stream_handler)\n\n# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)\nsampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))\n\n# Create a new batch in the project and add the sampled data rows\nbatch = project.create_batch(\n    \"Initial batch\",  # name of the batch\n    sampled_data_rows,  # list of Data Rows\n    1,  # priority between 1-5\n)\nprint(f\"Created batch with ID: {batch.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set export parameters focused on data row details\nexport_params = {\n    \"data_row_details\": True,  # Only export data row details\n    \"batch_ids\": [batch.uid\n                 ],  # Optional: Include batch ids to filter by specific batches\n}\n\n# Initialize the streamable export task from project\nexport_task = project.export(params=export_params)\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nlabels = []\nfor datarow in data_rows:\n    annotations_list = []\n    # Access the 'data_row' dictionary first\n    data_row_dict = datarow[\"data_row\"]\n    folder = data_row_dict[\"external_id\"].split(\"/\")[0]\n    id = data_row_dict[\"external_id\"].split(\"/\")[1]\n\n    if folder == \"positive_image_set\":\n        for image in annotations[\"images\"]:\n            if image[\"file_name\"] == id:\n                for annotation in annotations[\"annotations\"]:\n                    if annotation[\"image_id\"] == image[\"id\"]:\n                        bbox = annotation[\"bbox\"]\n                        category_id = annotation[\"category_id\"] - 1\n                        class_name = None\n                        ontology = (ontology_builder.asdict()\n                                   )  # Get the ontology dictionary\n                        for category in ontology[\"tools\"]:\n                            if (category[\"name\"] == annotations[\"categories\"]\n                                [category_id][\"name\"]):\n                                class_name = category[\"name\"]\n                                break\n                        if class_name:\n                            annotations_list.append(\n                                ObjectAnnotation(\n                                    name=class_name,\n                                    value=Rectangle(\n                                        start=Point(x=bbox[0], y=bbox[1]),\n                                        end=Point(\n                                            x=bbox[2] + bbox[0],\n                                            y=bbox[3] + bbox[1],\n                                        ),\n                                    ),\n                                ))\n    image_data = {\"uid\": data_row_dict[\"id\"]}\n    labels.append(Label(data=image_data, annotations=annotations_list))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"label_import_job_{str(uuid.uuid4())}\",\n    labels=labels,\n)\n\n# Wait for the upload to finish and print the results\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/pdf.ipynb b/examples/annotation_import/pdf.ipynb
index 4113a62b4..cfc2b82bd 100644
--- a/examples/annotation_import/pdf.ipynb
+++ b/examples/annotation_import/pdf.ipynb
@@ -1,387 +1,385 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 1,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# PDF Annotation Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "Supported annotations for PDF assets \n",
-                "\n",
-                "*Annotation types*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box\n",
-                "- Entities\n",
-                "- Relationships (only supported for MAL imports)\n",
-                "\n",
-                "\n",
-                "*NDJson*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box \n",
-                "- Entities \n",
-                "- Relationships (only supported for MAL imports)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key\n",
-                "Guides on https://docs.labelbox.com/docs/create-an-api-key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Supported Annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entity ##########\n\n# Annotation Types\nentities_annotations = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_annotations_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification #########\n\n# Annotation types\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Bounding Box ###########\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=102.771, y=135.3),  # x = left, y = top\n        end=lb_types.Point(x=518.571,\n                           y=245.143),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 135.3,\n        \"left\": 102.771,\n        \"height\": 109.843,\n        \"width\": 415.8\n    },\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############## Classification Free-form text ##############\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\"name\": \"free_text\", \"answer\": \"sample text\"}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### BBOX with nested classifications #########\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=317.271, y=226.757),  # x = left, y = top\n        end=lb_types.Point(x=566.657,\n                           y=420.986),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_annotation_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\"\n                },\n            }],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 226.757,\n        \"left\": 317.271,\n        \"height\": 194.229,\n        \"width\": 249.386,\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\"\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### Relationships ##########\nentity_source = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_target = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nentity_source_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_source,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\n\nentity_target_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_target,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\nner_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### BBOX with relationships #############\n# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=188.257, y=68.875),  # x = left, y = top\n        end=lb_types.Point(x=270.907,\n                           y=149.556),  # x = left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=96.424, y=66.251),\n        end=lb_types.Point(x=179.074, y=146.932),\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source_2 = str(uuid.uuid4())\nuuid_target_2 = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_source_2,\n    \"bbox\": {\n        \"top\": 68.875,\n        \"left\": 188.257,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_target_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_target_2,\n    \"bbox\": {\n        \"top\": 66.251,\n        \"left\": 96.424,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source_2,\n        \"target\": uuid_target_2,\n        \"type\": \"unidirectional\",\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
-                "\n",
-                "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
-                "- The document must have no more than 15 pages.\n",
-                "- The file size should not exceed 20 MB.\n",
-                "\n",
-                "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
-                "\n",
-                "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
-                "\n",
-                "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"0801.3483_doc.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an Ontology for your project\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Creating a labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"PDF_annotation_demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"PDF_annotation_batch\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5. Create the annotation payload\n",
-                "Create the annotations payload using the snippets of code in Supported predictions section.\n",
-                "\n",
-                "Labelbox support NDJSON only for this data type.\n",
-                "\n",
-                "The resulting label should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Step 5.1: First, we need to populate the text selections for Entity annotations\n",
-                "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "To extract the generated text layer url we first need to export the data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n    \"Organic charge transfer salts based on the donor\",\n    \"the experimental investigations on this issue have not\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\ntext_selections_source = []\ntext_selections_target = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_annotations_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_annotation_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[2]:\n            relationship_source = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_source = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=relationship_source, page=1)\n            text_selections_source.append(text_selection_entity_source)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entity_source_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_source,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[3]:\n            relationship_target = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_target = lb_types.DocumentTextSelection(\n                group_id=group[\"id\"], tokenIds=relationship_target, page=1)\n            text_selections_target.append(text_selection_entity_target)\n            # build text selections forthe NDJson annotations\n            update_text_selections(\n                annotation=entity_target_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_target,  # ids representing individual words from the group\n                page=1,\n            )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Re-write the python annotations to include text selections (only required for python annotation types)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# re-write the entity annotation with text selections\nentities_annotation_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", textSelections=text_selections)\nentities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_annotation_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\", textSelections=text_selections_ner)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# re-write the entity source and target annotations withe text selectios\nentity_source_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_source)\nentity_source = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_source_doc)\n\nentity_target_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_target)\nentity_target = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_target_doc)\n\n# re-write the entity relationship with the re-created entities\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\nprint(f\"entities_annotation={entities_annotation}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_annotation_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")\nprint(f\"entity_source_ndjson={entity_source_ndjson}\")\nprint(f\"entity_target_ndjson={entity_target_ndjson}\")\nprint(f\"entity_source={entity_source}\")\nprint(f\"entity_target={entity_target}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n            entity_source,\n            entity_target,\n            entity_relationship,  # Only supported for MAL imports\n            bbox_source,\n            bbox_target,\n            bbox_relationship,  # Only supported for MAL imports\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJson annotations\n",
-                "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annot in [\n        entities_annotations_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        bbox_annotation_ndjson,\n        bbox_with_radio_subclass_annotation_ndjson,\n        ner_with_checklist_subclass_annotation_ndjson,\n        entity_source_ndjson,\n        entity_target_ndjson,\n        ner_relationship_annotation_ndjson,  # Only supported for MAL imports\n        bbox_source_ndjson,\n        bbox_target_ndjson,\n        bbox_relationship_annotation_ndjson,  # Only supported for MAL imports\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Import the annotation payload\n",
-                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option A: Upload to a labeling project as pre-labels (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option B: Upload to a labeling project using ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Uncomment this code when excluding relationships from label import\n## Relationships are not currently supported for label import\n\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=labels) ## Remove unsupported relationships from the labels list\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 1,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# PDF Annotation Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "Supported annotations for PDF assets \n",
+        "\n",
+        "*Annotation types*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box\n",
+        "- Entities\n",
+        "- Relationships (only supported for MAL imports)\n",
+        "\n",
+        "\n",
+        "*NDJson*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box \n",
+        "- Entities \n",
+        "- Relationships (only supported for MAL imports)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key\n",
+        "Guides on https://docs.labelbox.com/docs/create-an-api-key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Supported Annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entity ##########\n\n# Annotation Types\nentities_annotations = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_annotations_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification #########\n\n# Annotation types\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Bounding Box ###########\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=102.771, y=135.3),  # x = left, y = top\n        end=lb_types.Point(x=518.571,\n                           y=245.143),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 135.3,\n        \"left\": 102.771,\n        \"height\": 109.843,\n        \"width\": 415.8\n    },\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############## Classification Free-form text ##############\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\"name\": \"free_text\", \"answer\": \"sample text\"}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### BBOX with nested classifications #########\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=317.271, y=226.757),  # x = left, y = top\n        end=lb_types.Point(x=566.657,\n                           y=420.986),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_annotation_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\"\n                },\n            }],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 226.757,\n        \"left\": 317.271,\n        \"height\": 194.229,\n        \"width\": 249.386,\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\"\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### Relationships ##########\nentity_source = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_target = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nentity_source_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_source,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\n\nentity_target_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_target,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\nner_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### BBOX with relationships #############\n# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=188.257, y=68.875),  # x = left, y = top\n        end=lb_types.Point(x=270.907,\n                           y=149.556),  # x = left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=96.424, y=66.251),\n        end=lb_types.Point(x=179.074, y=146.932),\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source_2 = str(uuid.uuid4())\nuuid_target_2 = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_source_2,\n    \"bbox\": {\n        \"top\": 68.875,\n        \"left\": 188.257,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_target_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_target_2,\n    \"bbox\": {\n        \"top\": 66.251,\n        \"left\": 96.424,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source_2,\n        \"target\": uuid_target_2,\n        \"type\": \"unidirectional\",\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
+        "\n",
+        "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
+        "- The document must have no more than 15 pages.\n",
+        "- The file size should not exceed 20 MB.\n",
+        "\n",
+        "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
+        "\n",
+        "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
+        "\n",
+        "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"0801.3483_doc.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an Ontology for your project\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Creating a labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"PDF_annotation_demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"PDF_annotation_batch\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5. Create the annotation payload\n",
+        "Create the annotations payload using the snippets of code in Supported predictions section.\n",
+        "\n",
+        "Labelbox support NDJSON only for this data type.\n",
+        "\n",
+        "The resulting label should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Step 5.1: First, we need to populate the text selections for Entity annotations\n",
+        "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "To extract the generated text layer url we first need to export the data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n    \"Organic charge transfer salts based on the donor\",\n    \"the experimental investigations on this issue have not\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\ntext_selections_source = []\ntext_selections_target = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_annotations_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_annotation_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[2]:\n            relationship_source = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_source = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=relationship_source, page=1)\n            text_selections_source.append(text_selection_entity_source)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entity_source_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_source,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[3]:\n            relationship_target = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_target = lb_types.DocumentTextSelection(\n                group_id=group[\"id\"], tokenIds=relationship_target, page=1)\n            text_selections_target.append(text_selection_entity_target)\n            # build text selections forthe NDJson annotations\n            update_text_selections(\n                annotation=entity_target_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_target,  # ids representing individual words from the group\n                page=1,\n            )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Re-write the python annotations to include text selections (only required for python annotation types)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# re-write the entity annotation with text selections\nentities_annotation_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", textSelections=text_selections)\nentities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_annotation_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\", textSelections=text_selections_ner)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# re-write the entity source and target annotations withe text selectios\nentity_source_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_source)\nentity_source = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_source_doc)\n\nentity_target_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_target)\nentity_target = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_target_doc)\n\n# re-write the entity relationship with the re-created entities\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\nprint(f\"entities_annotation={entities_annotation}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_annotation_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")\nprint(f\"entity_source_ndjson={entity_source_ndjson}\")\nprint(f\"entity_target_ndjson={entity_target_ndjson}\")\nprint(f\"entity_source={entity_source}\")\nprint(f\"entity_target={entity_target}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n            entity_source,\n            entity_target,\n            entity_relationship,  # Only supported for MAL imports\n            bbox_source,\n            bbox_target,\n            bbox_relationship,  # Only supported for MAL imports\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJson annotations\n",
+        "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annot in [\n        entities_annotations_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        bbox_annotation_ndjson,\n        bbox_with_radio_subclass_annotation_ndjson,\n        ner_with_checklist_subclass_annotation_ndjson,\n        entity_source_ndjson,\n        entity_target_ndjson,\n        ner_relationship_annotation_ndjson,  # Only supported for MAL imports\n        bbox_source_ndjson,\n        bbox_target_ndjson,\n        bbox_relationship_annotation_ndjson,  # Only supported for MAL imports\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Import the annotation payload\n",
+        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option A: Upload to a labeling project as pre-labels (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option B: Upload to a labeling project using ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Uncomment this code when excluding relationships from label import\n## Relationships are not currently supported for label import\n\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=labels) ## Remove unsupported relationships from the labels list\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/text.ipynb b/examples/annotation_import/text.ipynb
index 31d643579..104189074 100644
--- a/examples/annotation_import/text.ipynb
+++ b/examples/annotation_import/text.ipynb
@@ -1,318 +1,316 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Text Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for text assets, and also cover MAL and Label Import methods.\n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK: \n",
-                "\n",
-                "* Entity\n",
-                "* Classification radio \n",
-                "* Classification checklist \n",
-                "* Classification free-form text \n",
-                "\n",
-                "\n",
-                "**Not** supported:\n",
-                "* Relationships\n",
-                "* Segmentation mask\n",
-                "* Polygon\n",
-                "* Bounding box \n",
-                "* Polyline\n",
-                "* Point \n",
-                "\n",
-                "MAL and Label Import: \n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n",
-                "For information on what types of annotations are supported per data type, refer to the Import text annotations [documentation](https://docs.labelbox.com/reference/import-text-annotations)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Notes:\n",
-                "  * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
-                "  * You may need to refresh your browser in order to see the results of the import job."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Supported Python annotation types and NDJSON"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nnamed_entitity_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                      name=\"named_entity\")\n\n# NDJSON\nentities_ndjson = {\n    \"name\": \"named_entity\",\n    \"location\": {\n        \"start\": 67,\n        \"end\": 128\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Checklist (Multi-choice) ##########\n\n# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\n#  NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annoations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# You can now include ohter fields like attachments, media type and metadata in the data row creation step: https://docs.labelbox.com/reference/text-file\nglobal_key = \"lorem-ipsum.txt\"\ntext_asset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"TEXT\",\n    \"attachments\": [{\n        \"type\":\n            \"TEXT_URL\",\n        \"value\":\n            \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n    }],\n}\n\ndataset = client.create_dataset(\n    name=\"text_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([text_asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2:  Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool and classification `name` should match the `name` field in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-                "\n",
-                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(  # Text classification given the name \"text\"\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\"),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Text Annotations\",\n                                  ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project \n",
-                "Connect the ontology to the labeling project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Text Annotation Import Demo\",\n                                media_type=lb.MediaType.Text)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-text-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label\nlabels = []\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            named_entitity_annotation,\n            radio_annotation,\n            checklist_annotation,\n            text_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        entities_ndjson,\n        radio_annotation_ndjson,\n        checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or ground truth\n",
-                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python Annotation types). \n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Text Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for text assets, and also cover MAL and Label Import methods.\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "\n",
+        "* Entity\n",
+        "* Classification radio \n",
+        "* Classification checklist \n",
+        "* Classification free-form text \n",
+        "\n",
+        "\n",
+        "**Not** supported:\n",
+        "* Relationships\n",
+        "* Segmentation mask\n",
+        "* Polygon\n",
+        "* Bounding box \n",
+        "* Polyline\n",
+        "* Point \n",
+        "\n",
+        "MAL and Label Import: \n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n",
+        "For information on what types of annotations are supported per data type, refer to the Import text annotations [documentation](https://docs.labelbox.com/reference/import-text-annotations)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Notes:\n",
+        "  * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+        "  * You may need to refresh your browser in order to see the results of the import job."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Supported Python annotation types and NDJSON"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nnamed_entitity_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                      name=\"named_entity\")\n\n# NDJSON\nentities_ndjson = {\n    \"name\": \"named_entity\",\n    \"location\": {\n        \"start\": 67,\n        \"end\": 128\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Checklist (Multi-choice) ##########\n\n# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\n#  NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annoations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# You can now include ohter fields like attachments, media type and metadata in the data row creation step: https://docs.labelbox.com/reference/text-file\nglobal_key = \"lorem-ipsum.txt\"\ntext_asset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"TEXT\",\n    \"attachments\": [{\n        \"type\":\n            \"TEXT_URL\",\n        \"value\":\n            \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n    }],\n}\n\ndataset = client.create_dataset(\n    name=\"text_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([text_asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2:  Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool and classification `name` should match the `name` field in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+        "\n",
+        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(  # Text classification given the name \"text\"\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\"),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Text Annotations\",\n                                  ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project \n",
+        "Connect the ontology to the labeling project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Text Annotation Import Demo\",\n                                media_type=lb.MediaType.Text)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-text-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label\nlabels = []\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            named_entitity_annotation,\n            radio_annotation,\n            checklist_annotation,\n            text_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        entities_ndjson,\n        radio_annotation_ndjson,\n        checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or ground truth\n",
+        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python Annotation types). \n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index bef8c9f2b..83e547a5a 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -1,347 +1,345 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Tiled Imagery Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK: \n",
-                "  * Point \n",
-                "  * Polygon\n",
-                "  * Bounding Box \n",
-                "  * Classification radio \n",
-                "  * Classification checklist \n",
-                "  * Classification free-form text\n",
-                "\n",
-                "**Not** supported:\n",
-                "  * Segmentation mask\n",
-                "\n",
-                "\n",
-                "MAL and Label Import: \n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n",
-                "For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Notes:\n",
-                " * This notebook uses the Slippy Maps format\n",
-                " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
-                " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
-                " * You may need to refresh your browser in order to see the results of the import job."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport numpy as np\nimport cv2\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for tiled imagery"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Supported Python annotation types and NDJSON "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point_geo\",\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"line\": line_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"polygon\": polygon_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\"\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Geospatial Project Demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-geo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload \n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotations\n",
-                "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=20,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_annotation_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            polygon_annotation_two,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations\n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\n\nfor annotations in [\n        point_annotation_ndjson,\n        polyline_annotation_ndjson,\n        polygon_annotation_ndjson,\n        bbox_annotation_ndjson,\n        radio_annotation_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_geo_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Tiled Imagery Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "  * Point \n",
+        "  * Polygon\n",
+        "  * Bounding Box \n",
+        "  * Classification radio \n",
+        "  * Classification checklist \n",
+        "  * Classification free-form text\n",
+        "\n",
+        "**Not** supported:\n",
+        "  * Segmentation mask\n",
+        "\n",
+        "\n",
+        "MAL and Label Import: \n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n",
+        "For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Notes:\n",
+        " * This notebook uses the Slippy Maps format\n",
+        " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
+        " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+        " * You may need to refresh your browser in order to see the results of the import job."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport numpy as np\nimport cv2\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for tiled imagery"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Supported Python annotation types and NDJSON "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point_geo\",\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"line\": line_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"polygon\": polygon_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\"\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Geospatial Project Demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-geo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotations\n",
+        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=20,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_annotation_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            polygon_annotation_two,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations\n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\n\nfor annotations in [\n        point_annotation_ndjson,\n        polyline_annotation_ndjson,\n        polygon_annotation_ndjson,\n        bbox_annotation_ndjson,\n        radio_annotation_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_geo_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/video.ipynb b/examples/annotation_import/video.ipynb
index e40ac5efd..01dc1c48b 100644
--- a/examples/annotation_import/video.ipynb
+++ b/examples/annotation_import/video.ipynb
@@ -1,409 +1,407 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Video Annotation Import\n",
-                "\n",
-                "* Annotations must be created and uploaded using NDJSON\n",
-                "* Supported annotations that can be uploaded through the SDK:\n",
-                "    * Bounding box\n",
-                "    * Point\n",
-                "    * Polyline \n",
-                "    * Radio classifications \n",
-                "    * Checklist classifications \n",
-                "    *  Segmentation masks\n",
-                "* **NOT** supported:\n",
-                "    * Polygons \n",
-                "\n",
-                "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO\nimport pprint\n\npp = pprint.PrettyPrinter(indent=4)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for video\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box: (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_frame_annotation_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio and checklist (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n# Python annotation\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n]\n\n## NDJSON\nframe_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist and radio (global)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "##### Global Classifications #######\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}\n\n# Python annotation\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist (global)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######### Free text classification ###########\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature\"s name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with sub-classifications (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\"\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\"\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Masks (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "def extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "### Raster Segmentation (Byte string array)\n##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation\n\n# Single mask\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\nresponse = requests.get(url)\nimg_bytes = base64.b64encode(response.content).decode(\"utf-8\")\n\n# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain\n# all frames and instances\nframes_mask_single = [\n    lb_types.MaskFrame(\n        index=20,\n        im_bytes=response.\n        content,  # Instead of bytes you could also pass an instance URI : instance_uri=url\n    )\n]\ninstances_mask_single = [\n    lb_types.MaskInstance(color_rgb=(76, 104, 177), name=\"video_mask\")\n]\n\n## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame\nframes_cp_mask_url = [\n    {\n        \"1\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png\"\n    },\n    {\n        \"24\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\n    },\n    {\n        \"26\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png\"\n    },\n]\n\nrgb_mask_tool = [(227, 135, 126), (169, 248, 152), (83, 152, 103)]\ncp_masks = []\nunique_colors = set()\n\nlb_frames = []\nlb_instances = []\ncounter = 0\n\nfor d in frames_cp_mask_url:\n    for frame_no, v in d.items():\n        response = requests.get(v)\n        colors = extract_rgb_colors_from_url(v)\n        for color in colors:\n            if not color in unique_colors:\n                unique_colors.add(color)\n                name = (\"video_mask\" if color in rgb_mask_tool else\n                        \"mask_with_text_subclass\")\n                lb_instances.append(\n                    lb_types.MaskInstance(color_rgb=color, name=name))\n                counter += 1\n        lb_frames.append(\n            lb_types.MaskFrame(index=frame_no, im_bytes=response.content))\ncp_masks.append(\n    lb_types.VideoMaskAnnotation(\n        frames=lb_frames + frames_mask_single,\n        instances=lb_instances + instances_mask_single,\n    ))\n\npp.pprint(lb_frames)\npp.pprint(cp_masks)\n\n# NDJSON - single tool\nvideo_mask_ndjson_bytes_2 = {\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\": 31,\n                \"imBytes\": img_bytes,\n            },\n            {\n                \"index\": 34,\n                \"imBytes\": img_bytes,\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": [76, 104, 177],\n            \"name\": \"video_mask\"\n        }],\n    }\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Multiple instances of bounding box annotations in the same frame"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Fist instance of bounding box ranging from frame 22 to 27\nbbox_annotation_1 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON example:\nbbox_frame_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}\n\n# Second instance of bounding box ranging from frame 22 to 27\nbbox_annotation_2 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON\nbbox_frame_annotation_ndjson2 = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## End-to-end example: Import pre-labels or ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"sample-video-jellyfish.mp4\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"VIDEO\",\n}\n\ndataset = client.create_dataset(\n    name=\"video_demo_dataset\",\n    iam_integration=\n    None,  # If this argument is removed, labelbox will use the default integration for your organization.\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the bounding box annotation above, we provided the `name` as `bbox_video`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_video`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-                "\n",
-                "\n",
-                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.INDEX,\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## Need to defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Video Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project \n",
-                "Connect the ontology to the labeling project."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Video Annotation Import Demo\",\n                                media_type=lb.MediaType.Video)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    \"first-batch-video-demo2\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # A paginated collection of data row objects, a list of data rows or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload \n",
-                "Create the annotations payload using the snippets of code above.\n",
-                "\n",
-                "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python Annotation Types"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass_annotation,\n    bbox_annotation_1,\n    bbox_annotation_2,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n    cp_masks,\n]\n\nfor annotation in annotations_list:\n    label.append(\n        lb_types.Label(data={\"global_key\": global_key}, annotations=annotation))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations\n",
-                "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "First, let\"s update the bbox with nested classifications with the corresponding featureSchemaId"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\n\nannotations_list_ndjson = [\n    point_annotation_ndjson,\n    bbox_annotation_ndjson,\n    polyline_frame_annotation_ndjson,\n    frame_checklist_classification_ndjson,\n    frame_radio_classification_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    frame_bbox_with_checklist_subclass_annotation_ndjson,\n    global_radio_classification_ndjson,\n    global_checklist_classification_ndjson,\n    text_annotation_ndjson,\n    bbox_frame_annotation_ndjson,\n    bbox_frame_annotation_ndjson2,\n    video_mask_ndjson_bytes_2,\n]\n\nfor annotation in annotations_list_ndjson:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
-                "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=label,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# For this demo either run MAL or Ground truth import, not both.\n\n# upload_job_label_import = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name = \"label_import_job-\" + str(uuid.uuid4()),\n#     labels=label\n# )\n\n# upload_job_label_import.wait_until_done()\n# print(\"Errors:\", upload_job_label_import.errors)\n# print(\"Status of uploads: \", upload_job_label_import.statuses)\n# print(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Video Annotation Import\n",
+        "\n",
+        "* Annotations must be created and uploaded using NDJSON\n",
+        "* Supported annotations that can be uploaded through the SDK:\n",
+        "    * Bounding box\n",
+        "    * Point\n",
+        "    * Polyline \n",
+        "    * Radio classifications \n",
+        "    * Checklist classifications \n",
+        "    *  Segmentation masks\n",
+        "* **NOT** supported:\n",
+        "    * Polygons \n",
+        "\n",
+        "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO\nimport pprint\n\npp = pprint.PrettyPrinter(indent=4)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for video\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box: (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_frame_annotation_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio and checklist (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n# Python annotation\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n]\n\n## NDJSON\nframe_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist and radio (global)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "##### Global Classifications #######\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}\n\n# Python annotation\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist (global)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######### Free text classification ###########\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature\"s name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with sub-classifications (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\"\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\"\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Masks (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "def extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "### Raster Segmentation (Byte string array)\n##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation\n\n# Single mask\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\nresponse = requests.get(url)\nimg_bytes = base64.b64encode(response.content).decode(\"utf-8\")\n\n# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain\n# all frames and instances\nframes_mask_single = [\n    lb_types.MaskFrame(\n        index=20,\n        im_bytes=response.\n        content,  # Instead of bytes you could also pass an instance URI : instance_uri=url\n    )\n]\ninstances_mask_single = [\n    lb_types.MaskInstance(color_rgb=(76, 104, 177), name=\"video_mask\")\n]\n\n## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame\nframes_cp_mask_url = [\n    {\n        \"1\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png\"\n    },\n    {\n        \"24\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\n    },\n    {\n        \"26\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png\"\n    },\n]\n\nrgb_mask_tool = [(227, 135, 126), (169, 248, 152), (83, 152, 103)]\ncp_masks = []\nunique_colors = set()\n\nlb_frames = []\nlb_instances = []\ncounter = 0\n\nfor d in frames_cp_mask_url:\n    for frame_no, v in d.items():\n        response = requests.get(v)\n        colors = extract_rgb_colors_from_url(v)\n        for color in colors:\n            if not color in unique_colors:\n                unique_colors.add(color)\n                name = (\"video_mask\" if color in rgb_mask_tool else\n                        \"mask_with_text_subclass\")\n                lb_instances.append(\n                    lb_types.MaskInstance(color_rgb=color, name=name))\n                counter += 1\n        lb_frames.append(\n            lb_types.MaskFrame(index=frame_no, im_bytes=response.content))\ncp_masks.append(\n    lb_types.VideoMaskAnnotation(\n        frames=lb_frames + frames_mask_single,\n        instances=lb_instances + instances_mask_single,\n    ))\n\npp.pprint(lb_frames)\npp.pprint(cp_masks)\n\n# NDJSON - single tool\nvideo_mask_ndjson_bytes_2 = {\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\": 31,\n                \"imBytes\": img_bytes,\n            },\n            {\n                \"index\": 34,\n                \"imBytes\": img_bytes,\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": [76, 104, 177],\n            \"name\": \"video_mask\"\n        }],\n    }\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Multiple instances of bounding box annotations in the same frame"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Fist instance of bounding box ranging from frame 22 to 27\nbbox_annotation_1 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON example:\nbbox_frame_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}\n\n# Second instance of bounding box ranging from frame 22 to 27\nbbox_annotation_2 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON\nbbox_frame_annotation_ndjson2 = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## End-to-end example: Import pre-labels or ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"sample-video-jellyfish.mp4\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"VIDEO\",\n}\n\ndataset = client.create_dataset(\n    name=\"video_demo_dataset\",\n    iam_integration=\n    None,  # If this argument is removed, labelbox will use the default integration for your organization.\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the bounding box annotation above, we provided the `name` as `bbox_video`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_video`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+        "\n",
+        "\n",
+        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.INDEX,\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## Need to defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Video Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project \n",
+        "Connect the ontology to the labeling project."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Video Annotation Import Demo\",\n                                media_type=lb.MediaType.Video)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch = project.create_batch(\n    \"first-batch-video-demo2\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # A paginated collection of data row objects, a list of data rows or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above.\n",
+        "\n",
+        "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python Annotation Types"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass_annotation,\n    bbox_annotation_1,\n    bbox_annotation_2,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n    cp_masks,\n]\n\nfor annotation in annotations_list:\n    label.append(\n        lb_types.Label(data={\"global_key\": global_key}, annotations=annotation))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations\n",
+        "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "First, let\"s update the bbox with nested classifications with the corresponding featureSchemaId"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\n\nannotations_list_ndjson = [\n    point_annotation_ndjson,\n    bbox_annotation_ndjson,\n    polyline_frame_annotation_ndjson,\n    frame_checklist_classification_ndjson,\n    frame_radio_classification_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    frame_bbox_with_checklist_subclass_annotation_ndjson,\n    global_radio_classification_ndjson,\n    global_checklist_classification_ndjson,\n    text_annotation_ndjson,\n    bbox_frame_annotation_ndjson,\n    bbox_frame_annotation_ndjson2,\n    video_mask_ndjson_bytes_2,\n]\n\nfor annotation in annotations_list_ndjson:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
+        "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=label,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# For this demo either run MAL or Ground truth import, not both.\n\n# upload_job_label_import = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name = \"label_import_job-\" + str(uuid.uuid4()),\n#     labels=label\n# )\n\n# upload_job_label_import.wait_until_done()\n# print(\"Errors:\", upload_job_label_import.errors)\n# print(\"Status of uploads: \", upload_job_label_import.statuses)\n# print(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index 3588e8bcf..7e9c78a7a 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -1,187 +1,185 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Basic project/datasets overview\n",
-                "\n",
-                "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fields\n",
-                "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
-                "* These fields are attributes of the object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(project.name)\nprint(dataset.name)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.update(description=\"new description field\")\nprint(project.description)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Pagination\n",
-                "* Queries that return a list of database objects are return as a PaginatedCollection\n",
-                "* Limits the data that is being returned for better performance"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Query parameters\n",
-                "* Query with the following conventions:\n",
-                "    * `DbObject.Field`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Querying Limitations\n",
-                "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
-                "* The below query is not valid since get_project returns a project not a dataset\n",
-                ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Relationships between projects and batches/datasets\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Basic project/datasets overview\n",
+        "\n",
+        "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API key and client\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Fields\n",
+        "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
+        "* These fields are attributes of the object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(project.name)\nprint(dataset.name)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.update(description=\"new description field\")\nprint(project.description)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Pagination\n",
+        "* Queries that return a list of database objects are return as a PaginatedCollection\n",
+        "* Limits the data that is being returned for better performance"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Query parameters\n",
+        "* Query with the following conventions:\n",
+        "    * `DbObject.Field`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Querying Limitations\n",
+        "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
+        "* The below query is not valid since get_project returns a project not a dataset\n",
+        ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Relationships between projects and batches/datasets\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index 852f0c5e7..15d898289 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -1,301 +1,299 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Batches\n",
-                "https://docs.labelbox.com/docs/batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* A batch is collection of data rows.\n",
-                "* A data row cannot be part of more than one batch in a given project.\n",
-                "* Batches work for all data types, but there can only be one data type per project.\n",
-                "* Batches can not be shared between projects.\n",
-                "* Batches may have data rows from multiple datasets.\n",
-                "* Currently, only benchmarks quality settings is supported in batch projects\n",
-                "* You can set the priority for each batch."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create a dataset and data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup batch project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Select all data rows from the dataset\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Select a random sample\n",
-                "This method is useful if you have large datasets and only want to work with a handful of data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "sample = random.sample(global_keys, 4)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a batch\n",
-                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create multiple batches\n",
-                "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
-                "\n",
-                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
-                "\n",
-                "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
-                "\n",
-                "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create batches from a dataset\n",
-                "\n",
-                "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
-                "\n",
-                "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Manage Batches\n",
-                "Note: You can view your batch data through the **Data Rows** tab."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Export Batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create and Attach Ontology to Project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export from Project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Archive a batch"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up \n",
-                "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Batches\n",
+        "https://docs.labelbox.com/docs/batches"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* A batch is collection of data rows.\n",
+        "* A data row cannot be part of more than one batch in a given project.\n",
+        "* Batches work for all data types, but there can only be one data type per project.\n",
+        "* Batches can not be shared between projects.\n",
+        "* Batches may have data rows from multiple datasets.\n",
+        "* Currently, only benchmarks quality settings is supported in batch projects\n",
+        "* You can set the priority for each batch."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API key and client\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create a dataset and data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup batch project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create batches"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Select all data rows from the dataset\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Select a random sample\n",
+        "This method is useful if you have large datasets and only want to work with a handful of data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "sample = random.sample(global_keys, 4)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a batch\n",
+        "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create multiple batches\n",
+        "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
+        "\n",
+        "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
+        "\n",
+        "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
+        "\n",
+        "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create batches from a dataset\n",
+        "\n",
+        "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
+        "\n",
+        "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Manage Batches\n",
+        "Note: You can view your batch data through the **Data Rows** tab."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Export Batches"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create and Attach Ontology to Project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export from Project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Archive a batch"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Clean up \n",
+        "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/custom_embeddings.ipynb b/examples/basics/custom_embeddings.ipynb
index 15b63cc35..6448cf41a 100644
--- a/examples/basics/custom_embeddings.ipynb
+++ b/examples/basics/custom_embeddings.ipynb
@@ -1,288 +1,286 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Custom Embeddings\n",
-                "\n",
-                "You can improve your data exploration and similarity search experience by adding your own custom embeddings. Labelbox allows you to upload up to 10 different custom embeddings per workspace on any kind of data. You can experiment with different embeddings to power your data selection."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Set up "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip3 install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport numpy as np\nimport json\nimport uuid\nimport random",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Select data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "- Get images from a Labelbox dataset\n",
-                "- To improve similarity search, you need to upload custom embeddings to at least 1,000 data rows.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATASET_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.get_dataset(dataset_id=DATASET_ID)\nexport_task = dataset.export()\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_row_dict = [{\"data_row_id\": dr[\"data_row\"][\"id\"]} for dr in data_rows]\ndata_row_dict = data_row_dict[:\n                              1000]  # keep the first 1000 examples for the sake of this demo",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Create custom embedding payload "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Generate random vectors for embeddings (max : 2048 dimensions)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nb_data_rows = len(data_row_dict)\nprint(\"Number of data rows: \", nb_data_rows)\n# Labelbox supports custom embedding vectors of dimension up to 2048\ncustom_embeddings = [list(np.random.random(2048)) for _ in range(nb_data_rows)]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "List all custom embeddings available in your Labelbox workspace"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "embeddings = client.get_embeddings()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Choose an existing embedding type or create a new one"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Name of the custom embedding must be unique\nembedding = client.create_embedding(\"my_custom_embedding_2048_dimensions\", 2048)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Create payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "The payload should encompass the `key` (data row id  or global key) and the new embedding vector data. Note that the `dataset.upsert_data_rows()` operation will only update the values you pass in the payload; all other existing row data will not be modified."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "payload = []\nfor data_row_dict, custom_embedding in zip(data_row_dict, custom_embeddings):\n    payload.append({\n        \"key\":\n            lb.UniqueId(data_row_dict[\"data_row_id\"]),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": custom_embedding\n        }],\n    })\n\nprint(\"payload\", len(payload), payload[:1])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Upload payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Upsert data rows with custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "task = dataset.upsert_data_rows(payload)\ntask.wait_till_done()\nprint(task.errors)\nprint(task.status)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Get the count of imported vectors for a custom embedding"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Count how many data rows have a specific custom embedding (this can take a couple of minutes)\ncount = embedding.get_imported_vector_count()\nprint(count)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Delete custom embedding type"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# embedding.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Upload custom embeddings during data row creation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Create a dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset_new = client.create_dataset(name=\"data_rows_with_embeddings\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch an embedding (2048 dimension)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "embedding = client.get_embedding_by_name(\"my_custom_embedding_2048_dimensions\")\nvector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Upload data rows with embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": vector\n        }],\n    })\n\ntask1 = dataset_new.create_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Custom Embeddings\n",
+        "\n",
+        "You can improve your data exploration and similarity search experience by adding your own custom embeddings. Labelbox allows you to upload up to 10 different custom embeddings per workspace on any kind of data. You can experiment with different embeddings to power your data selection."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Set up "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip3 install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport numpy as np\nimport json\nimport uuid\nimport random",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Select data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "- Get images from a Labelbox dataset\n",
+        "- To improve similarity search, you need to upload custom embeddings to at least 1,000 data rows.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATASET_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.get_dataset(dataset_id=DATASET_ID)\nexport_task = dataset.export()\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_row_dict = [{\"data_row_id\": dr[\"data_row\"][\"id\"]} for dr in data_rows]\ndata_row_dict = data_row_dict[:\n                              1000]  # keep the first 1000 examples for the sake of this demo",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create custom embedding payload "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Generate random vectors for embeddings (max : 2048 dimensions)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nb_data_rows = len(data_row_dict)\nprint(\"Number of data rows: \", nb_data_rows)\n# Labelbox supports custom embedding vectors of dimension up to 2048\ncustom_embeddings = [list(np.random.random(2048)) for _ in range(nb_data_rows)]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "List all custom embeddings available in your Labelbox workspace"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "embeddings = client.get_embeddings()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Choose an existing embedding type or create a new one"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Name of the custom embedding must be unique\nembedding = client.create_embedding(\"my_custom_embedding_2048_dimensions\", 2048)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "The payload should encompass the `key` (data row id  or global key) and the new embedding vector data. Note that the `dataset.upsert_data_rows()` operation will only update the values you pass in the payload; all other existing row data will not be modified."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "payload = []\nfor data_row_dict, custom_embedding in zip(data_row_dict, custom_embeddings):\n    payload.append({\n        \"key\":\n            lb.UniqueId(data_row_dict[\"data_row_id\"]),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": custom_embedding\n        }],\n    })\n\nprint(\"payload\", len(payload), payload[:1])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Upload payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Upsert data rows with custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "task = dataset.upsert_data_rows(payload)\ntask.wait_till_done()\nprint(task.errors)\nprint(task.status)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Get the count of imported vectors for a custom embedding"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Count how many data rows have a specific custom embedding (this can take a couple of minutes)\ncount = embedding.get_imported_vector_count()\nprint(count)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Delete custom embedding type"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# embedding.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Upload custom embeddings during data row creation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create a dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a dataset\ndataset_new = client.create_dataset(name=\"data_rows_with_embeddings\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch an embedding (2048 dimension)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "embedding = client.get_embedding_by_name(\"my_custom_embedding_2048_dimensions\")\nvector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Upload data rows with embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": vector\n        }],\n    })\n\ntask1 = dataset_new.create_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/data_row_metadata.ipynb b/examples/basics/data_row_metadata.ipynb
index 5e15c0a93..3d2c63424 100644
--- a/examples/basics/data_row_metadata.ipynb
+++ b/examples/basics/data_row_metadata.ipynb
@@ -1,302 +1,300 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Data Row Metadata\n",
-                "\n",
-                "Metadata is useful to better understand data on the platform to help with labeling review, model diagnostics, and data selection. This **should not be confused with attachments**. Attachments provide additional context for labelers but is not searchable within Catalog."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Metadata ontology\n",
-                "\n",
-                "We use a similar system for managing metadata as we do feature schemas. Metadata schemas are strongly typed to ensure we can provide the best experience in the App. Each metadata field can be uniquely accessed by id. Names are unique within the kind of metadata, reserved or custom. A DataRow can have a maximum of 5 metadata fields at a time.\n",
-                "\n",
-                "### Metadata kinds\n",
-                "\n",
-                "* **Enum**: A classification with options, only one option can be selected at a time\n",
-                "* **DateTime**: A utc ISO datetime \n",
-                "* **String**: A string of less than 500 characters\n",
-                "\n",
-                "### Reserved fields\n",
-                "\n",
-                "* **tag**: a free text field\n",
-                "* **split**: enum of train-valid-test\n",
-                "* **captureDateTime**: ISO 8601 datetime field. All times must be in UTC\n",
-                "\n",
-                "### Custom fields\n",
-                "\n",
-                "* **Embedding**: 128 float 32 vector used for similarity. To upload custom embeddings use the following [tutorial](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/basics/custom_embeddings.ipynb)\n",
-                "* Any metadata kind can be customized"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom datetime import datetime\nfrom pprint import pprint\nfrom labelbox.schema.data_row_metadata import DataRowMetadataKind\nfrom uuid import uuid4",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get the current metadata ontology "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "mdo = client.get_data_row_metadata_ontology()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# list all your metadata ontology as a dictionary accessable by id\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Access metadata by name"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "split_field = mdo.reserved_by_name[\"split\"]\nsplit_field",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "tag_field = mdo.reserved_by_name[\"tag\"]\ntag_field",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "train_field = mdo.reserved_by_name[\"split\"][\"train\"]\ntrain_field",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Construct metadata fields for existing metadata schemas\n",
-                "\n",
-                "To construct a metadata field you must provide the name for the metadata field and the value that will be uploaded. You can either construct a DataRowMetadataField object or specify the name and value in a dictionary format.\n",
-                "\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option 1: Specify metadata with a list of `DataRowMetadataField` objects. This is the recommended option since it comes with validation for metadata fields."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Construct a metadata field of string kind\ntag_metadata_field = lb.DataRowMetadataField(\n    name=\"tag\",\n    value=\"tag_string\",\n)\n\n# Construct an metadata field of datetime kind\ncapture_datetime_field = lb.DataRowMetadataField(\n    name=\"captureDateTime\",\n    value=datetime.utcnow(),\n)\n\n# Construct a metadata field of Enums options\nsplit_metadata_field = lb.DataRowMetadataField(\n    name=\"split\",\n    value=\"train\",\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option 2: You can also specify the metadata fields with dictionary format without declaring the `DataRowMetadataField` objects.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Construct a dictionary of string metadata\ntag_metadata_field_dict = {\n    \"name\": \"tag\",\n    \"value\": \"tag_string\",\n}\n\n# Construct a dictionary of datetime metadata\ncapture_datetime_field_dict = {\n    \"name\": \"captureDateTime\",\n    \"value\": datetime.utcnow(),\n}\n\n# Construct a dictionary of Enums options metadata\nsplit_metadata_field_dict = {\n    \"name\": \"split\",\n    \"value\": \"train\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create a custom metadata schema with their corresponding fields\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Final\ncustom_metadata_fields = []\n\n# Create the schema for the metadata\nnumber_schema = mdo.create_schema(name=\"numberMetadataCustom\",\n                                  kind=DataRowMetadataKind.number)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_number = lb.DataRowMetadataField(\n    name=number_schema.name, value=5.0)\n\ncustom_metadata_fields.append(data_row_metadata_fields_number)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create the schema for an enum metadata\ncustom_metadata_fields = []\n\nenum_schema = mdo.create_schema(\n    name=\"enumMetadata\",\n    kind=DataRowMetadataKind.enum,\n    options=[\"option1\", \"option2\"],\n)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_enum_1 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option1\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_1)\n\ndata_row_metadata_fields_enum_2 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option2\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Inspect the newly created metadata schemas\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create data rows with metadata\n",
-                "\n",
-                "See our [documentation](https://docs.labelbox.com/docs/limits) for information on limits for uploading data rows in a single API operation."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# A simple example of uploading data rows with metadata\ndataset = client.create_dataset(\n    name=\"Simple Data Rows import with metadata example\")\nglobal_key = \"s_basic.jpg\"\ndata_row = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/basic.jpg\",\n    \"global_key\":\n        global_key,\n}\n# This line works with dictionaries as well as schemas and fields created with DataRowMetadataField\ndata_row[\"metadata_fields\"] = custom_metadata_fields + [\n    split_metadata_field,\n    capture_datetime_field_dict,\n    tag_metadata_field,\n]\n\ntask = dataset.create_data_rows([data_row])\ntask.wait_till_done()\nresult_task = task.result\nprint(result_task)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Update data row metadata"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get the data row that was uploaded in the previous cell\nnum_schema = mdo.get_by_name(\"numberMetadataCustom\")\n\n# Update the metadata\nupdated_metadata = lb.DataRowMetadataField(schema_id=num_schema.uid, value=10.2)\n\n# Create data row payload\ndata_row_payload = lb.DataRowMetadata(global_key=global_key,\n                                      fields=[updated_metadata])\n\n# Upsert the fields with the update metadata for number-metadata\nmdo.bulk_upsert([data_row_payload])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Update metadata schema"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# update a name\nnumber_schema = mdo.update_schema(name=\"numberMetadataCustom\",\n                                  new_name=\"numberMetadataCustomNew\")\n\n# update an Enum metadata schema option's name, this only applies to Enum metadata schema.\nenum_schema = mdo.update_enum_option(name=\"enumMetadata\",\n                                     option=\"option1\",\n                                     new_option=\"option3\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Accessing metadata\n",
-                "\n",
-                "You can examine an individual data row, including its metadata."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_row = next(dataset.data_rows())\nfor metadata_field in data_row.metadata_fields:\n    print(metadata_field[\"name\"], \":\", metadata_field[\"value\"])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "You can bulk export metadata using data row IDs."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_rows_metadata = mdo.bulk_export([data_row.uid])\nlen(data_rows_metadata)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Delete custom metadata schema \n",
-                "You can delete custom metadata schema by name. If you wish to delete a metadata schema, uncomment the line below and insert the desired name."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# status = mdo.delete_schema(name=\"<metadata schema name>\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Data Row Metadata\n",
+        "\n",
+        "Metadata is useful to better understand data on the platform to help with labeling review, model diagnostics, and data selection. This **should not be confused with attachments**. Attachments provide additional context for labelers but is not searchable within Catalog."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Metadata ontology\n",
+        "\n",
+        "We use a similar system for managing metadata as we do feature schemas. Metadata schemas are strongly typed to ensure we can provide the best experience in the App. Each metadata field can be uniquely accessed by id. Names are unique within the kind of metadata, reserved or custom. A DataRow can have a maximum of 5 metadata fields at a time.\n",
+        "\n",
+        "### Metadata kinds\n",
+        "\n",
+        "* **Enum**: A classification with options, only one option can be selected at a time\n",
+        "* **DateTime**: A utc ISO datetime \n",
+        "* **String**: A string of less than 500 characters\n",
+        "\n",
+        "### Reserved fields\n",
+        "\n",
+        "* **tag**: a free text field\n",
+        "* **split**: enum of train-valid-test\n",
+        "* **captureDateTime**: ISO 8601 datetime field. All times must be in UTC\n",
+        "\n",
+        "### Custom fields\n",
+        "\n",
+        "* **Embedding**: 128 float 32 vector used for similarity. To upload custom embeddings use the following [tutorial](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/basics/custom_embeddings.ipynb)\n",
+        "* Any metadata kind can be customized"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom datetime import datetime\nfrom pprint import pprint\nfrom labelbox.schema.data_row_metadata import DataRowMetadataKind\nfrom uuid import uuid4",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get the current metadata ontology "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "mdo = client.get_data_row_metadata_ontology()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# list all your metadata ontology as a dictionary accessable by id\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Access metadata by name"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "split_field = mdo.reserved_by_name[\"split\"]\nsplit_field",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "tag_field = mdo.reserved_by_name[\"tag\"]\ntag_field",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "train_field = mdo.reserved_by_name[\"split\"][\"train\"]\ntrain_field",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Construct metadata fields for existing metadata schemas\n",
+        "\n",
+        "To construct a metadata field you must provide the name for the metadata field and the value that will be uploaded. You can either construct a DataRowMetadataField object or specify the name and value in a dictionary format.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option 1: Specify metadata with a list of `DataRowMetadataField` objects. This is the recommended option since it comes with validation for metadata fields."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Construct a metadata field of string kind\ntag_metadata_field = lb.DataRowMetadataField(\n    name=\"tag\",\n    value=\"tag_string\",\n)\n\n# Construct an metadata field of datetime kind\ncapture_datetime_field = lb.DataRowMetadataField(\n    name=\"captureDateTime\",\n    value=datetime.utcnow(),\n)\n\n# Construct a metadata field of Enums options\nsplit_metadata_field = lb.DataRowMetadataField(\n    name=\"split\",\n    value=\"train\",\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option 2: You can also specify the metadata fields with dictionary format without declaring the `DataRowMetadataField` objects.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Construct a dictionary of string metadata\ntag_metadata_field_dict = {\n    \"name\": \"tag\",\n    \"value\": \"tag_string\",\n}\n\n# Construct a dictionary of datetime metadata\ncapture_datetime_field_dict = {\n    \"name\": \"captureDateTime\",\n    \"value\": datetime.utcnow(),\n}\n\n# Construct a dictionary of Enums options metadata\nsplit_metadata_field_dict = {\n    \"name\": \"split\",\n    \"value\": \"train\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create a custom metadata schema with their corresponding fields\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Final\ncustom_metadata_fields = []\n\n# Create the schema for the metadata\nnumber_schema = mdo.create_schema(name=\"numberMetadataCustom\",\n                                  kind=DataRowMetadataKind.number)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_number = lb.DataRowMetadataField(\n    name=number_schema.name, value=5.0)\n\ncustom_metadata_fields.append(data_row_metadata_fields_number)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create the schema for an enum metadata\ncustom_metadata_fields = []\n\nenum_schema = mdo.create_schema(\n    name=\"enumMetadata\",\n    kind=DataRowMetadataKind.enum,\n    options=[\"option1\", \"option2\"],\n)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_enum_1 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option1\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_1)\n\ndata_row_metadata_fields_enum_2 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option2\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Inspect the newly created metadata schemas\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create data rows with metadata\n",
+        "\n",
+        "See our [documentation](https://docs.labelbox.com/docs/limits) for information on limits for uploading data rows in a single API operation."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# A simple example of uploading data rows with metadata\ndataset = client.create_dataset(\n    name=\"Simple Data Rows import with metadata example\")\nglobal_key = \"s_basic.jpg\"\ndata_row = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/basic.jpg\",\n    \"global_key\":\n        global_key,\n}\n# This line works with dictionaries as well as schemas and fields created with DataRowMetadataField\ndata_row[\"metadata_fields\"] = custom_metadata_fields + [\n    split_metadata_field,\n    capture_datetime_field_dict,\n    tag_metadata_field,\n]\n\ntask = dataset.create_data_rows([data_row])\ntask.wait_till_done()\nresult_task = task.result\nprint(result_task)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Update data row metadata"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get the data row that was uploaded in the previous cell\nnum_schema = mdo.get_by_name(\"numberMetadataCustom\")\n\n# Update the metadata\nupdated_metadata = lb.DataRowMetadataField(schema_id=num_schema.uid, value=10.2)\n\n# Create data row payload\ndata_row_payload = lb.DataRowMetadata(global_key=global_key,\n                                      fields=[updated_metadata])\n\n# Upsert the fields with the update metadata for number-metadata\nmdo.bulk_upsert([data_row_payload])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Update metadata schema"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# update a name\nnumber_schema = mdo.update_schema(name=\"numberMetadataCustom\",\n                                  new_name=\"numberMetadataCustomNew\")\n\n# update an Enum metadata schema option's name, this only applies to Enum metadata schema.\nenum_schema = mdo.update_enum_option(name=\"enumMetadata\",\n                                     option=\"option1\",\n                                     new_option=\"option3\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Accessing metadata\n",
+        "\n",
+        "You can examine an individual data row, including its metadata."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_row = next(dataset.data_rows())\nfor metadata_field in data_row.metadata_fields:\n    print(metadata_field[\"name\"], \":\", metadata_field[\"value\"])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "You can bulk export metadata using data row IDs."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_rows_metadata = mdo.bulk_export([data_row.uid])\nlen(data_rows_metadata)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Delete custom metadata schema \n",
+        "You can delete custom metadata schema by name. If you wish to delete a metadata schema, uncomment the line below and insert the desired name."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# status = mdo.delete_schema(name=\"<metadata schema name>\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/data_rows.ipynb b/examples/basics/data_rows.ipynb
index def791228..7c16b4eca 100644
--- a/examples/basics/data_rows.ipynb
+++ b/examples/basics/data_rows.ipynb
@@ -1,317 +1,315 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Data rows are the assets that are being labeled. We currently support the following asset types:\n",
-                "    * Image\n",
-                "    * Text\n",
-                "    * Video\n",
-                "    * Geospatial / Tiled Imagery\n",
-                "    * Audio\n",
-                "    * Documents \n",
-                "    * HTML \n",
-                "    * DICOM \n",
-                "    * Conversational\n",
-                "* A data row cannot exist without belonging to a dataset.\n",
-                "* Data rows are added to labeling tasks by first attaching them to datasets and then creating batches in projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install labelbox -q",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get data rows from projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Pick a project with batches that have data rows with global keys\nPROJECT_ID = \"<PROJECT-ID>\"\nproject = client.get_project(PROJECT_ID)\nbatches = list(project.batches())\nprint(batches)\n# This is the same as\n# -> dataset = client.get_dataset(dataset_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fetch data rows from project's batches\n",
-                "\n",
-                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nbatch_ids = [batch.uid for batch in batches]\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\":\n        batch_ids,  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Get single data row\ndata_row = data_rows[0]\nprint(data_row)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get labels from the data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Associated label(s)\", data_row[\"projects\"][project.uid][\"labels\"])\nprint(\"Global key\", data_row[\"data_row\"][\"global_key\"])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get data row ids by using global keys"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"<ENTER GLOBAL KEY>\"\ntask = client.get_data_row_ids_for_global_keys([global_key])\nprint(f\"Data row id: {task['results']}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create\n",
-                "We recommend the following methods to create data rows : `dataset.upsert_data_rows()`, and `dataset.create_data_rows()`, "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create data rows via `dataset.upsert_data_rows()`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"data_rows_demo_dataset_6\")\n# You can also upload metadata along with your data row\nmdo = client.get_data_row_metadata_ontology()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 8):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n        \"attachments\": [\n            {\n                \"type\":\n                    \"IMAGE_OVERLAY\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\": \"RAW_TEXT\",\n                \"value\": \"IOWA, Zone 2232, June 2022 [Text string]\",\n            },\n            {\n                \"type\":\n                    \"TEXT_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n            },\n            {\n                \"type\":\n                    \"IMAGE\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\":\n                    \"VIDEO\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4\",\n            },\n            {\n                \"type\":\n                    \"HTML\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/windy.html\",\n            },\n            {\n                \"type\":\n                    \"PDF_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n            },\n        ],\n    })\n\ntask1 = dataset.upsert_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Create data rows from data in your local path "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "from PIL import Image\n\n# Create dummy empty jpeg file\nwidth = 400\nheight = 300\ncolor = (255, 255, 255)  # White color\nimage = Image.new(\"RGB\", (width, height), color)\n\n# Save the image as a JPEG file\nimage.save(\"dummy.jpg\")\n\nlocal_data_path = \"dummy.jpg\"\n\ndata = {\"row_data\": local_data_path, \"global_key\": str(uuid.uuid4())}\n\ntask3 = dataset.upsert_data_rows([data])\ntask3.wait_till_done()\nprint(\"ERRORS: \", task3.errors)\nprint(\"RESULTS:\", task3.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# You can mix local files with urls when creating data rows\ntask4 = dataset.upsert_data_rows([\n    {\n        \"row_data\":\n            \"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_0009.jpeg\",\n        \"global_key\":\n            str(uuid.uuid4()),\n    },\n    {\n        \"row_data\": local_data_path,\n        \"global_key\": str(uuid.uuid4())\n    },\n])\ntask4.wait_till_done()\nprint(\"ERRORS: \", task4.errors)\nprint(\"RESULTS:\", task4.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create data rows via `dataset.create_data_rows()`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset_2 = client.create_dataset(name=\"data_rows_demo_dataset_3\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n    })\n\ntask1_2 = dataset_2.create_data_rows(uploads)\ntask1_2.wait_till_done()\nprint(\"ERRORS: \", task1_2.errors)\nprint(\"RESULTS:\", task1_2.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update\n",
-                "`dataset.upsert_data_rows()` can also be use to update data rows\n",
-                "\n",
-                "To update data rows using this method, you need to pass a `key`, which can reference either a global key or a data row ID. Additionally, include any fields that you wish to update along with their new values.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Update the global key assodicated with the DATAROW_ID or GLOBAL_KEY, and include a additional metadata\ndata = {\n    \"key\":\n        lb.UniqueId(DATA_ROW_ID),\n    \"global_key\":\n        \"NEW-ID-%id\" % uuid.uuid1(),\n    \"metadata_fields\": [\n        # New metadata\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"captureDateTime\"].uid,\n            value=\"2000-01-01 00:00:00\",\n        ),\n        # Include original metadata otherwise it will be removed\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"tag\"].uid,\n            value=\"tag_string\",\n        ),\n    ],\n}\n\ntask5 = dataset_2.upsert_data_rows([data])\ntask5.wait_till_done()\nprint(\"ERRORS: \", task5.errors)\nprint(\"RESULTS:\", task5.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a single attachment on an existing data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# You can only create one attachment at the time.\nDATA_ROW_ID = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATA_ROW_ID)\nattachment = data_row.create_attachment(\n    attachment_type=\"RAW_TEXT\", attachment_value=\"LABELERS WILL SEE THIS\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Update a recently created attachment "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "attachment.update(type=\"RAW_TEXT\", value=\"NEW RAW TEXT\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Delete"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Delete a single data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATAROW_ID_TO_DELETE = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATAROW_ID_TO_DELETE)\ndata_row.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Bulk delete data row objects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Bulk delete a list of data_rows ( limit: 4K data rows per call)\nlb.DataRow.bulk_delete(list(dataset.data_rows()))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Data rows are the assets that are being labeled. We currently support the following asset types:\n",
+        "    * Image\n",
+        "    * Text\n",
+        "    * Video\n",
+        "    * Geospatial / Tiled Imagery\n",
+        "    * Audio\n",
+        "    * Documents \n",
+        "    * HTML \n",
+        "    * DICOM \n",
+        "    * Conversational\n",
+        "* A data row cannot exist without belonging to a dataset.\n",
+        "* Data rows are added to labeling tasks by first attaching them to datasets and then creating batches in projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install labelbox -q",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get data rows from projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Pick a project with batches that have data rows with global keys\nPROJECT_ID = \"<PROJECT-ID>\"\nproject = client.get_project(PROJECT_ID)\nbatches = list(project.batches())\nprint(batches)\n# This is the same as\n# -> dataset = client.get_dataset(dataset_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Fetch data rows from project's batches\n",
+        "\n",
+        "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\nbatch_ids = [batch.uid for batch in batches]\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\":\n        batch_ids,  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Get single data row\ndata_row = data_rows[0]\nprint(data_row)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get labels from the data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Associated label(s)\", data_row[\"projects\"][project.uid][\"labels\"])\nprint(\"Global key\", data_row[\"data_row\"][\"global_key\"])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get data row ids by using global keys"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"<ENTER GLOBAL KEY>\"\ntask = client.get_data_row_ids_for_global_keys([global_key])\nprint(f\"Data row id: {task['results']}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create\n",
+        "We recommend the following methods to create data rows : `dataset.upsert_data_rows()`, and `dataset.create_data_rows()`, "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create data rows via `dataset.upsert_data_rows()`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a dataset\ndataset = client.create_dataset(name=\"data_rows_demo_dataset_6\")\n# You can also upload metadata along with your data row\nmdo = client.get_data_row_metadata_ontology()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "uploads = []\n# Generate data rows\nfor i in range(1, 8):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n        \"attachments\": [\n            {\n                \"type\":\n                    \"IMAGE_OVERLAY\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\": \"RAW_TEXT\",\n                \"value\": \"IOWA, Zone 2232, June 2022 [Text string]\",\n            },\n            {\n                \"type\":\n                    \"TEXT_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n            },\n            {\n                \"type\":\n                    \"IMAGE\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\":\n                    \"VIDEO\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4\",\n            },\n            {\n                \"type\":\n                    \"HTML\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/windy.html\",\n            },\n            {\n                \"type\":\n                    \"PDF_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n            },\n        ],\n    })\n\ntask1 = dataset.upsert_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create data rows from data in your local path "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "from PIL import Image\n\n# Create dummy empty jpeg file\nwidth = 400\nheight = 300\ncolor = (255, 255, 255)  # White color\nimage = Image.new(\"RGB\", (width, height), color)\n\n# Save the image as a JPEG file\nimage.save(\"dummy.jpg\")\n\nlocal_data_path = \"dummy.jpg\"\n\ndata = {\"row_data\": local_data_path, \"global_key\": str(uuid.uuid4())}\n\ntask3 = dataset.upsert_data_rows([data])\ntask3.wait_till_done()\nprint(\"ERRORS: \", task3.errors)\nprint(\"RESULTS:\", task3.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# You can mix local files with urls when creating data rows\ntask4 = dataset.upsert_data_rows([\n    {\n        \"row_data\":\n            \"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_0009.jpeg\",\n        \"global_key\":\n            str(uuid.uuid4()),\n    },\n    {\n        \"row_data\": local_data_path,\n        \"global_key\": str(uuid.uuid4())\n    },\n])\ntask4.wait_till_done()\nprint(\"ERRORS: \", task4.errors)\nprint(\"RESULTS:\", task4.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create data rows via `dataset.create_data_rows()`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset_2 = client.create_dataset(name=\"data_rows_demo_dataset_3\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n    })\n\ntask1_2 = dataset_2.create_data_rows(uploads)\ntask1_2.wait_till_done()\nprint(\"ERRORS: \", task1_2.errors)\nprint(\"RESULTS:\", task1_2.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update\n",
+        "`dataset.upsert_data_rows()` can also be use to update data rows\n",
+        "\n",
+        "To update data rows using this method, you need to pass a `key`, which can reference either a global key or a data row ID. Additionally, include any fields that you wish to update along with their new values.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Update the global key assodicated with the DATAROW_ID or GLOBAL_KEY, and include a additional metadata\ndata = {\n    \"key\":\n        lb.UniqueId(DATA_ROW_ID),\n    \"global_key\":\n        \"NEW-ID-%id\" % uuid.uuid1(),\n    \"metadata_fields\": [\n        # New metadata\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"captureDateTime\"].uid,\n            value=\"2000-01-01 00:00:00\",\n        ),\n        # Include original metadata otherwise it will be removed\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"tag\"].uid,\n            value=\"tag_string\",\n        ),\n    ],\n}\n\ntask5 = dataset_2.upsert_data_rows([data])\ntask5.wait_till_done()\nprint(\"ERRORS: \", task5.errors)\nprint(\"RESULTS:\", task5.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a single attachment on an existing data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# You can only create one attachment at the time.\nDATA_ROW_ID = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATA_ROW_ID)\nattachment = data_row.create_attachment(\n    attachment_type=\"RAW_TEXT\", attachment_value=\"LABELERS WILL SEE THIS\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Update a recently created attachment "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "attachment.update(type=\"RAW_TEXT\", value=\"NEW RAW TEXT\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Delete"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Delete a single data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATAROW_ID_TO_DELETE = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATAROW_ID_TO_DELETE)\ndata_row.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Bulk delete data row objects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Bulk delete a list of data_rows ( limit: 4K data rows per call)\nlb.DataRow.bulk_delete(list(dataset.data_rows()))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
index 11073ccf4..0a76d7fd1 100644
--- a/examples/basics/export_v1_to_v2_migration_support.ipynb
+++ b/examples/basics/export_v1_to_v2_migration_support.ipynb
@@ -1,593 +1,591 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Export V1 to V2 migration "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "**Export V1 will no longer be available in any version of the SDK starting on April 2024**. We recommend users to plan accordingly. \n",
-                "\n",
-                "This notebook is designed to help users identify alternative V2 export methods that can serve as replacements for V1 methods."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Key changes included in export V2 methods ( ``export()`` and ``export_v2()``):\n",
-                "1. Added flexibility to only export the data that is needed. The new methods include parameters and filters to give you more granular control over your exports.\n",
-                "2. Added functionality to stream an **unlimited** number of data rows using ``export()`` (available on SDK >=3.56). Upgrading to `export()` is recommended as it is a more scalable solution.\n",
-                "\n",
-                "For complete details on how to use export V2 methods please see the [Export V2 methods](https://docs.labelbox.com/reference/label-export#export-v2-methods) documentation."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "###  Export V1 deprecated methods: \n",
-                "Project methods : \n",
-                "1. ```project.export_labels()```\n",
-                "2. ```project.label_generator()```\n",
-                "3. ```project.export_queued_data_rows()```\n",
-                "\n",
-                "Dataset methods: \n",
-                "1. ```dataset.export_data_rows()```\n",
-                "\n",
-                "Batch methods: \n",
-                "1. ```batch.export_data_rows()```\n",
-                "\n",
-                "Model methods :\n",
-                "1. ```model_run.export_labels()```\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport pprint\n\npp = pprint.PrettyPrinter(width=30, compact=True)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Exports V1 to V2 guidelines\n",
-                "The follow sections will demonstrate how to use the export V2 methods to fetch data from your projects, datasets, batches and model runs. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export labels from a project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "PROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V1 (deprecated)  \n",
-                "1. ```project.export_labels()``` \n",
-                "    - Parameters:  \n",
-                "        - ```download: bool = False```\n",
-                "        - ```timeout_seconds: int = 1800```\n",
-                "    - Output : (str | List[Dict[Any, Any]] | None)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "2. ```project.label_generator()```\n",
-                "    - Parameters: \n",
-                "        - ```timeout_seconds: int = 600```\n",
-                "    - Output: LabelGenerator"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single entry from the output of project.label_generator() (deprecated)\n# Label objects will not be deprecated.\nsingle_output_from_generator = \"\"\"\n\nLabel(\n    uid='clrf5csho2ihx07ilffgp2fzj',\n    data=ImageData(\n        im_bytes=None,\n        file_path=None,\n        url='https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg',\n        arr=None\n    ),\n    annotations=[\n        ObjectAnnotation(\n            confidence=None,\n            name='bounding_box',\n            feature_schema_id='clrf5ck4a0b9b071paa9ncu15',\n            extra={\n                'instanceURI': 'https://api.labelbox.com/masks/feature/clrf5csvi6ofm07lsf9pygwvi?token=<token>'\n                'color': '#ff0000',\n                'feature_id': 'clrf5csvi6ofm07lsf9pygwvi',\n                'value': 'bounding_box',\n                'page': None,\n                'unit': None\n            },\n            value=Rectangle(\n                extra={},\n                start=Point(extra={}, x=2096.0, y=1264.0),\n                end=Point(extra={}, x=2240.0, y=1689.0)\n            ),\n            classifications=[]\n        ),\n        # Add more annotations as needed\n        # ...\n    ],\n    extra={\n        'Created By': 'aovalle@labelbox.com',\n        'Project Name': 'Image Annotation Import Demo',\n        'Created At': '2024-01-15T16:35:59.000Z',\n        'Updated At': '2024-01-15T16:51:56.000Z',\n        'Seconds to Label': 66.0,\n        'Agreement': -1.0,\n        'Benchmark Agreement': -1.0,\n        'Benchmark ID': None,\n        'Dataset Name': 'image-demo-dataset',\n        'Reviews': [],\n        'View Label': 'https://editor.labelbox.com?project=clrf5ckex09m9070x1te223u5&label=clrf5csho2ihx07ilffgp2fzj',\n        'Has Open Issues': 0.0,\n        'Skipped': False,\n        'media_type': 'image',\n        'Data Split': None,\n        'Global Key': '2560px-Kitano_Street_Kobe01s5s41102.jpeg'\n    }\n)\n\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V2 \n",
-                "\n",
-                "For complete details on the supported filters abd parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Parameters:  \n",
-                "        - ```\"label_details\": True```\n",
-                "        - ```\"attachments\": True```\n",
-                "        - ```\"data_row_details\": True```\n",
-                "        - ```\"project_details\": True```\n",
-                "        - ```\"label_details\": True```\n",
-                "        - ```\"performance_details\": True```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Set the export parameters to only export labels\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n}\n# You also have the option to include additional filtering to narrow down the list of labels\nfilters = {}\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export queued (\"To Label\") data rows from a project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V1 (deprecated):  \n",
-                "1. ``project.export_queued_data_rows()`` :\n",
-                "    - Parameters: \n",
-                "        - ``timeout_seconds: int = 120``\n",
-                "        - ``include_metadata: bool = False``\n",
-                "    - Output: List[Dict[str, str]]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single entry from the output of project.export_queued_data_rows() (deprecated)\nsingle_output_example = \"\"\"\n[\n  {'id': 'clpouak6nap2g0783ajd1d6pf',\n 'createdAt': '2023-12-03T02:04:34.062Z',\n 'updatedAt': '2023-12-03T02:05:33.797Z',\n 'externalId': None,\n 'globalKey': 'b57c9ab2-304f-4c17-ba5f-c536f39a6a46',\n 'metadataFields': [],\n 'customMetadata': [],\n 'rowData': 'https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg',\n 'mediaAttributes': {'assetType': 'image',\n  'contentLength': 305973,\n  'height': 1333,\n  'mimeType': 'image/jpeg',\n  'subType': 'jpeg',\n  'superType': 'image',\n  'width': 2000}}\n]\n\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Parameters (Minimum required parameters):  \n",
-                "      - ```\"data_row_details\": True```\n",
-                "      - ```\"project_details\": True```\n",
-                "    - Required filters: \n",
-                "      - ``` \"workflow_status\": \"ToLabel\"```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n    \"project_details\": True,\n}\nfilters = {\n    \"workflow_status\":\n        \"ToLabel\"  ## Using this filter will only export queued data rows\n}\n\n# An ExportTask is returned, this provides additional information about the status of your task, such as\n# any errors encountered and includes additional methods to stream your data\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a Dataset "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V1 (deprecated): \n",
-                "\n",
-                "1. ```dataset.export_data_rows()```\n",
-                "  - Parameters:  \n",
-                "    - ``timeout_seconds=120``\n",
-                "    - ``include_metadata: bool = True``\n",
-                "  - Output: \n",
-                "    - Data row object generator\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single entry from the output of dataset.export_data_rows() (deprecated)\n# Data row objects will not be deprecated.\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Parameters (minimum required parameters):  \n",
-                "      - ``\"data_row_details\": True``\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a batch"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V1 (deprecated): \n",
-                "1. ```batch.export_data_rows()```\n",
-                "  - Parameters:  \n",
-                "    - ``timeout_seconds=120``\n",
-                "    - ``include_metadata: bool = True``\n",
-                "  - Output: \n",
-                "    - Data row object generator"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single output from batch.export_data_rows() method (deprecated)\n# Data row objects will not be deprecated\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Required parameters:  \n",
-                "      - ```\"data_row_details\": True```,\n",
-                "      - ```\"batch_ids\": [<batch_id>] ```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Find the batch ID by navigating to \"Batches\" -->  \"Manage batches\" --> \"Copy Batch ID\"\nBATCH_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        BATCH_ID\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a Model "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V1 (deprecated): \n",
-                "1. ```model_run.export_labels(downlaod=True)```\n",
-                "    - Parameters:  \n",
-                "        - ```download: bool = False```\n",
-                "        - ```timeout_seconds: int = 1800```\n",
-                "    - Output : (str | List[Dict[Any, Any]] | None)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single output from model_run.export_labels()\nsingle_output_example = \"\"\"\n[\n   {'ID': '1c48a7a0-3016-48e0-b0e3-47430f974869',\n   'Data Split': 'training',\n   'DataRow ID': 'clpqdyf650xd40712pycshy6a',\n   'External ID': './resume/BANKING/99124477.pdf',\n   'Labeled Data': 'https://storage.labelbox.com/cl5bn8qvq1av907xtb3bp8q60%2F8c6afc38-42a4-b2e1-a2e3-1e3b0c2998fc-99124477.pdf?Expires=1706637969726&KeyName=labelbox-assets-key-3&Signature=2nVt3sJ21CbjGS9I64yFquUELRw',\n   'Media Attributes': {'assetType': 'pdf',\n      'contentLength': 42535,\n      'mimeType': 'application/pdf',\n      'pageCount': 3,\n      'subType': 'pdf',\n      'superType': 'application'},\n   'Label': {'objects': [{'featureId': 'b9f3b584-0f45-050a-88d4-39c2a169c8e1',\n      'schemaId': 'clq1ckwbd08jp07z91q9mch5j',\n      'title': 'Test',\n      'value': 'test',\n      'color': '#1CE6FF',\n      'data': {'location': [{'text-bbox': {'page': 1,\n            'top': 158.44,\n            'left': 58.765,\n            'height': 13.691,\n            'width': 78.261}}],\n         'unit': 'POINTS'}}],\n      'classifications': [],\n      'relationships': []}}\n   ]\n   \"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```model_run.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Required parameters:  \n",
-                "      - ```\"data_row_details\": True```\n",
-                "      - ```\"project_details\": True```\n",
-                "      - ```\"label_details\": True```\n",
-                "    - Required filters: \n",
-                "      - N/A -> Filters not supported\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "MODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n}\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a video project\n",
-                "Video projects include additional fields. Please refer to the example below to extract specific fields from video exports.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V1 (deprecated)  \n",
-                "1. ```project.export_labels()``` \n",
-                "    - Parameters:  \n",
-                "        - ```download: bool = False```\n",
-                "        - ```timeout_seconds: int = 1800```\n",
-                "    - Output : (str | List[Dict[Any, Any]] | None)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "2. ```project.video_label_generator()```\n",
-                "    - Parameters: \n",
-                "        - ```timeout_seconds: int = 600```\n",
-                "    - Output: LabelGenerator"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V2\n",
-                "\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Required parameters:  \n",
-                "      - ```\"attachments\": True```\n",
-                "      - ```\"data_row_details\": True```\n",
-                "      - ```\"project_details\": True```\n",
-                "      - ```\"label_details\": True```\n",
-                "      - ```\"performance_details\": True```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "VIDEO_PROJECT_ID = \"\"\nproject = client.get_project(VIDEO_PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"label_details\": True,\n    \"interpolated_frames\":\n        True,  # For additional information on interpolated frames please visit our documentation https://docs.labelbox.com/docs/video-annotations#video-editor-components\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch frame specific objects and frame or global classifications"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import json\nimport pprint as pp  # Assuming pp is imported from pprint module\n\nframes_objects_class_list = []\nglobal_class_list = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n        for k, v in frames_data.items():\n            frames_objects_class_list.append({k: v})\n        global_class_list.extend(dr[\"annotations\"][\"classifications\"])\n\n    print(\"------- Frame specific classifications and objects -------\")\n    pp.pprint(frames_objects_class_list)\n\n    print(\"------ Global classifications -------\")\n    pp.pprint(global_class_list)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch key frame feature map "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "keyframe_map = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"key_frame_feature_map\"]\n        for key, value in annotations.items():\n            keyframe_map.append({key: value})\n\nprint(\"----- Keyframe Feature Map -----\")\npp.pprint(keyframe_map)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch segments "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "segments_map = []\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"segments\"]\n        for key, value in annotations.items():\n            segments_map.append({key: value})\n\nprint(\"----- Segments Feature Map -----\")\npp.pprint(segments_map)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Export V1 to V2 migration "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "**Export V1 will no longer be available in any version of the SDK starting on April 2024**. We recommend users to plan accordingly. \n",
+        "\n",
+        "This notebook is designed to help users identify alternative V2 export methods that can serve as replacements for V1 methods."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Key changes included in export V2 methods ( ``export()`` and ``export_v2()``):\n",
+        "1. Added flexibility to only export the data that is needed. The new methods include parameters and filters to give you more granular control over your exports.\n",
+        "2. Added functionality to stream an **unlimited** number of data rows using ``export()`` (available on SDK >=3.56). Upgrading to `export()` is recommended as it is a more scalable solution.\n",
+        "\n",
+        "For complete details on how to use export V2 methods please see the [Export V2 methods](https://docs.labelbox.com/reference/label-export#export-v2-methods) documentation."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "###  Export V1 deprecated methods: \n",
+        "Project methods : \n",
+        "1. ```project.export_labels()```\n",
+        "2. ```project.label_generator()```\n",
+        "3. ```project.export_queued_data_rows()```\n",
+        "\n",
+        "Dataset methods: \n",
+        "1. ```dataset.export_data_rows()```\n",
+        "\n",
+        "Batch methods: \n",
+        "1. ```batch.export_data_rows()```\n",
+        "\n",
+        "Model methods :\n",
+        "1. ```model_run.export_labels()```\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport pprint\n\npp = pprint.PrettyPrinter(width=30, compact=True)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Exports V1 to V2 guidelines\n",
+        "The follow sections will demonstrate how to use the export V2 methods to fetch data from your projects, datasets, batches and model runs. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export labels from a project\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "PROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V1 (deprecated)  \n",
+        "1. ```project.export_labels()``` \n",
+        "    - Parameters:  \n",
+        "        - ```download: bool = False```\n",
+        "        - ```timeout_seconds: int = 1800```\n",
+        "    - Output : (str | List[Dict[Any, Any]] | None)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "2. ```project.label_generator()```\n",
+        "    - Parameters: \n",
+        "        - ```timeout_seconds: int = 600```\n",
+        "    - Output: LabelGenerator"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single entry from the output of project.label_generator() (deprecated)\n# Label objects will not be deprecated.\nsingle_output_from_generator = \"\"\"\n\nLabel(\n    uid='clrf5csho2ihx07ilffgp2fzj',\n    data=ImageData(\n        im_bytes=None,\n        file_path=None,\n        url='https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg',\n        arr=None\n    ),\n    annotations=[\n        ObjectAnnotation(\n            confidence=None,\n            name='bounding_box',\n            feature_schema_id='clrf5ck4a0b9b071paa9ncu15',\n            extra={\n                'instanceURI': 'https://api.labelbox.com/masks/feature/clrf5csvi6ofm07lsf9pygwvi?token=<token>'\n                'color': '#ff0000',\n                'feature_id': 'clrf5csvi6ofm07lsf9pygwvi',\n                'value': 'bounding_box',\n                'page': None,\n                'unit': None\n            },\n            value=Rectangle(\n                extra={},\n                start=Point(extra={}, x=2096.0, y=1264.0),\n                end=Point(extra={}, x=2240.0, y=1689.0)\n            ),\n            classifications=[]\n        ),\n        # Add more annotations as needed\n        # ...\n    ],\n    extra={\n        'Created By': 'aovalle@labelbox.com',\n        'Project Name': 'Image Annotation Import Demo',\n        'Created At': '2024-01-15T16:35:59.000Z',\n        'Updated At': '2024-01-15T16:51:56.000Z',\n        'Seconds to Label': 66.0,\n        'Agreement': -1.0,\n        'Benchmark Agreement': -1.0,\n        'Benchmark ID': None,\n        'Dataset Name': 'image-demo-dataset',\n        'Reviews': [],\n        'View Label': 'https://editor.labelbox.com?project=clrf5ckex09m9070x1te223u5&label=clrf5csho2ihx07ilffgp2fzj',\n        'Has Open Issues': 0.0,\n        'Skipped': False,\n        'media_type': 'image',\n        'Data Split': None,\n        'Global Key': '2560px-Kitano_Street_Kobe01s5s41102.jpeg'\n    }\n)\n\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V2 \n",
+        "\n",
+        "For complete details on the supported filters abd parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Parameters:  \n",
+        "        - ```\"label_details\": True```\n",
+        "        - ```\"attachments\": True```\n",
+        "        - ```\"data_row_details\": True```\n",
+        "        - ```\"project_details\": True```\n",
+        "        - ```\"label_details\": True```\n",
+        "        - ```\"performance_details\": True```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Set the export parameters to only export labels\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n}\n# You also have the option to include additional filtering to narrow down the list of labels\nfilters = {}\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export queued (\"To Label\") data rows from a project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V1 (deprecated):  \n",
+        "1. ``project.export_queued_data_rows()`` :\n",
+        "    - Parameters: \n",
+        "        - ``timeout_seconds: int = 120``\n",
+        "        - ``include_metadata: bool = False``\n",
+        "    - Output: List[Dict[str, str]]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single entry from the output of project.export_queued_data_rows() (deprecated)\nsingle_output_example = \"\"\"\n[\n  {'id': 'clpouak6nap2g0783ajd1d6pf',\n 'createdAt': '2023-12-03T02:04:34.062Z',\n 'updatedAt': '2023-12-03T02:05:33.797Z',\n 'externalId': None,\n 'globalKey': 'b57c9ab2-304f-4c17-ba5f-c536f39a6a46',\n 'metadataFields': [],\n 'customMetadata': [],\n 'rowData': 'https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg',\n 'mediaAttributes': {'assetType': 'image',\n  'contentLength': 305973,\n  'height': 1333,\n  'mimeType': 'image/jpeg',\n  'subType': 'jpeg',\n  'superType': 'image',\n  'width': 2000}}\n]\n\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Parameters (Minimum required parameters):  \n",
+        "      - ```\"data_row_details\": True```\n",
+        "      - ```\"project_details\": True```\n",
+        "    - Required filters: \n",
+        "      - ``` \"workflow_status\": \"ToLabel\"```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n    \"project_details\": True,\n}\nfilters = {\n    \"workflow_status\":\n        \"ToLabel\"  ## Using this filter will only export queued data rows\n}\n\n# An ExportTask is returned, this provides additional information about the status of your task, such as\n# any errors encountered and includes additional methods to stream your data\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a Dataset "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V1 (deprecated): \n",
+        "\n",
+        "1. ```dataset.export_data_rows()```\n",
+        "  - Parameters:  \n",
+        "    - ``timeout_seconds=120``\n",
+        "    - ``include_metadata: bool = True``\n",
+        "  - Output: \n",
+        "    - Data row object generator\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single entry from the output of dataset.export_data_rows() (deprecated)\n# Data row objects will not be deprecated.\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Parameters (minimum required parameters):  \n",
+        "      - ``\"data_row_details\": True``\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a batch"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V1 (deprecated): \n",
+        "1. ```batch.export_data_rows()```\n",
+        "  - Parameters:  \n",
+        "    - ``timeout_seconds=120``\n",
+        "    - ``include_metadata: bool = True``\n",
+        "  - Output: \n",
+        "    - Data row object generator"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single output from batch.export_data_rows() method (deprecated)\n# Data row objects will not be deprecated\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Required parameters:  \n",
+        "      - ```\"data_row_details\": True```,\n",
+        "      - ```\"batch_ids\": [<batch_id>] ```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Find the batch ID by navigating to \"Batches\" -->  \"Manage batches\" --> \"Copy Batch ID\"\nBATCH_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        BATCH_ID\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a Model "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V1 (deprecated): \n",
+        "1. ```model_run.export_labels(downlaod=True)```\n",
+        "    - Parameters:  \n",
+        "        - ```download: bool = False```\n",
+        "        - ```timeout_seconds: int = 1800```\n",
+        "    - Output : (str | List[Dict[Any, Any]] | None)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single output from model_run.export_labels()\nsingle_output_example = \"\"\"\n[\n   {'ID': '1c48a7a0-3016-48e0-b0e3-47430f974869',\n   'Data Split': 'training',\n   'DataRow ID': 'clpqdyf650xd40712pycshy6a',\n   'External ID': './resume/BANKING/99124477.pdf',\n   'Labeled Data': 'https://storage.labelbox.com/cl5bn8qvq1av907xtb3bp8q60%2F8c6afc38-42a4-b2e1-a2e3-1e3b0c2998fc-99124477.pdf?Expires=1706637969726&KeyName=labelbox-assets-key-3&Signature=2nVt3sJ21CbjGS9I64yFquUELRw',\n   'Media Attributes': {'assetType': 'pdf',\n      'contentLength': 42535,\n      'mimeType': 'application/pdf',\n      'pageCount': 3,\n      'subType': 'pdf',\n      'superType': 'application'},\n   'Label': {'objects': [{'featureId': 'b9f3b584-0f45-050a-88d4-39c2a169c8e1',\n      'schemaId': 'clq1ckwbd08jp07z91q9mch5j',\n      'title': 'Test',\n      'value': 'test',\n      'color': '#1CE6FF',\n      'data': {'location': [{'text-bbox': {'page': 1,\n            'top': 158.44,\n            'left': 58.765,\n            'height': 13.691,\n            'width': 78.261}}],\n         'unit': 'POINTS'}}],\n      'classifications': [],\n      'relationships': []}}\n   ]\n   \"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```model_run.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Required parameters:  \n",
+        "      - ```\"data_row_details\": True```\n",
+        "      - ```\"project_details\": True```\n",
+        "      - ```\"label_details\": True```\n",
+        "    - Required filters: \n",
+        "      - N/A -> Filters not supported\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "MODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n}\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a video project\n",
+        "Video projects include additional fields. Please refer to the example below to extract specific fields from video exports.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V1 (deprecated)  \n",
+        "1. ```project.export_labels()``` \n",
+        "    - Parameters:  \n",
+        "        - ```download: bool = False```\n",
+        "        - ```timeout_seconds: int = 1800```\n",
+        "    - Output : (str | List[Dict[Any, Any]] | None)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "2. ```project.video_label_generator()```\n",
+        "    - Parameters: \n",
+        "        - ```timeout_seconds: int = 600```\n",
+        "    - Output: LabelGenerator"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V2\n",
+        "\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Required parameters:  \n",
+        "      - ```\"attachments\": True```\n",
+        "      - ```\"data_row_details\": True```\n",
+        "      - ```\"project_details\": True```\n",
+        "      - ```\"label_details\": True```\n",
+        "      - ```\"performance_details\": True```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "VIDEO_PROJECT_ID = \"\"\nproject = client.get_project(VIDEO_PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"label_details\": True,\n    \"interpolated_frames\":\n        True,  # For additional information on interpolated frames please visit our documentation https://docs.labelbox.com/docs/video-annotations#video-editor-components\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch frame specific objects and frame or global classifications"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import json\nimport pprint as pp  # Assuming pp is imported from pprint module\n\nframes_objects_class_list = []\nglobal_class_list = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n        for k, v in frames_data.items():\n            frames_objects_class_list.append({k: v})\n        global_class_list.extend(dr[\"annotations\"][\"classifications\"])\n\n    print(\"------- Frame specific classifications and objects -------\")\n    pp.pprint(frames_objects_class_list)\n\n    print(\"------ Global classifications -------\")\n    pp.pprint(global_class_list)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch key frame feature map "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "keyframe_map = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"key_frame_feature_map\"]\n        for key, value in annotations.items():\n            keyframe_map.append({key: value})\n\nprint(\"----- Keyframe Feature Map -----\")\npp.pprint(keyframe_map)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch segments "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "segments_map = []\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"segments\"]\n        for key, value in annotations.items():\n            segments_map.append({key: value})\n\nprint(\"----- Segments Feature Map -----\")\npp.pprint(segments_map)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/ontologies.ipynb b/examples/basics/ontologies.ipynb
index ca1447252..a576d39f8 100644
--- a/examples/basics/ontologies.ipynb
+++ b/examples/basics/ontologies.ipynb
@@ -1,304 +1,302 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Ontologies\n",
-                "* An ontology is a collection different tools and classifications that can be used within a project's editor. Each tool or classification is called a \"Feature Schema\". \n",
-                "* Feature Schemas contain information about the tool such as the kind, the name, all subclasses, and other information related to a tool. Feature Schemas can be shared between ontologies. \n",
-                "\n",
-                "* Helpful Links:\n",
-                "    * [Ontology documentation](https://docs.labelbox.com/docs/labelbox-ontology)\n",
-                "    * [Project Setup Using Ontologies](https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/project_setup.ipynb)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install labelbox -q",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Ontology From Normalized Data\n",
-                "* Users can create ontologies from a json definition of the ontology.\n",
-                "* See below `OntologyBuilder` section for more details on constructing the normalized ontology.\n",
-                "* Each tool type requires a specific value be passed:\n",
-                "\n",
-                "| Tool    | Value |\n",
-                "| :----------- | :----------- |\n",
-                "| Bounding box      | rectangle    |\n",
-                "| Polygon           | polygon      |\n",
-                "| Polyline          | line         |\n",
-                "| Point             | point        |\n",
-                "| Segmentation mask | raster-segmentation  |\n",
-                "| Entity            | named-entity |\n",
-                "| Relationship      | edge         |"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# This will automatically create new feature schema\nontology_name = \"sdk-ontology\"\nfeature_schema_cat_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"cat\",\n    \"color\": \"black\",\n}\n\nontology_normalized_json = {\n    \"tools\": [feature_schema_cat_normalized],\n    \"classifications\": [],\n}\nontology = client.create_ontology(name=ontology_name,\n                                  normalized=ontology_normalized_json)\nprint(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Ontology From Existing Feature Schemas\n",
-                "* It is often useful to support the same features in multiple ontologies. \n",
-                "* Labelbox supports this workflow by allowing users to create ontologies using existing feature schemas."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First create the feature schema\nfeature_schema_cat = client.create_feature_schema(feature_schema_cat_normalized)\n# When we create the ontology it will not re-create the feature schema\nprint(feature_schema_cat.uid)\nontology = client.create_ontology_from_feature_schemas(ontology_name,\n                                                       [feature_schema_cat.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Ontology From a Mix of New and Existing Feature Schemas\n",
-                "* If we want to create a new ontology that expands upon a previous ontology it is helpful to be able to share a portion of the features.\n",
-                "* To do this we will create the new schema ids that we want. Then we will create an ontology from the new list of ids.\n",
-                "* Note that for additional customization you can also combine the normalized json and use the create_ontology() method (not covered here)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create new dog schema id\nfeature_schema_dog_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"dog\",\n    \"color\": \"black\",\n    \"classifications\": [],\n}\nfeature_schema_dog = client.create_feature_schema(feature_schema_dog_normalized)\n# The cat is shared between this new ontology and the one we created previously\n# (ie. the cat feature schema will not be re-created)\nontology = client.create_ontology_from_feature_schemas(\n    ontology_name, [feature_schema_cat.uid, feature_schema_dog.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Read\n",
-                "* We can directly query by id for ontologies and feature schemas\n",
-                "* We also can search for both by name"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "#### Fetch by ID\nfeature_schema = client.get_feature_schema(feature_schema_cat.uid)\nontology = client.get_ontology(ontology.uid)\nprint(feature_schema)\nprint(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "#### Search by name\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nontology = next(client.get_ontologies(ontology_name))\nprint(feature_schema)\nprint(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update and Delete"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Check if feature is archived\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nclient.is_feature_schema_archived(ontology_id=ontology.uid,\n                                  feature_schema_id=feature_schema.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Update a feature's title\nclient.update_feature_schema_title(feature_schema_id=feature_schema.uid,\n                                   title=\"cat-2\")\nfeature = client.get_feature_schema(feature_schema_id=feature_schema.uid)\nprint(\"Feature: \", feature)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Replace a feature\ntool = lb.Tool(\n    feature_schema_id=feature_schema.uid,\n    name=\"tool-cat-upserted\",\n    tool=lb.Tool.Type.BBOX,\n    color=\"#FF0000\",\n)\nupserted_feature_schema_id = client.upsert_feature_schema(tool.asdict()).uid\nfeature = client.get_feature_schema(\n    feature_schema_id=upserted_feature_schema_id)\nprint(\"Updated feature: \", feature)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Insert a new feature\ntool = lb.Tool(name=\"tool-cat-2\", tool=lb.Tool.Type.RASTER_SEGMENTATION)\nfeature_schema_id_new = client.create_feature_schema(tool.asdict()).uid\nclient.insert_feature_schema_into_ontology(\n    feature_schema_id=feature_schema_id_new,\n    ontology_id=ontology.uid,\n    position=2,\n)\nprint(\"Updated ontology: \", client.get_ontology(ontology_id=ontology.uid))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Delete or Archived a feature:\n",
-                "\n",
-                "If the feature schema is a root level node with associated labels, it will be archived.\n",
-                "If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
-                "If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.delete_feature_schema_from_ontology(\n    ontology_id=ontology.uid, feature_schema_id=feature_schema_id_new)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Only features with annotations will be archived, features without annotations will be deleted.\nfeature_schema_id_with_annotations = \"\"\nontology_id = \"\"\nclient.unarchive_feature_schema_node(\n    ontology_id=ontology_id,\n    root_feature_schema_id=feature_schema_id_with_annotations,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Ontology Builder\n",
-                "* The ontology builder is a tool for creating and modifying normalized json"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create normalized json with a bounding box and segmentation tool\nontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n    lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\"),\n])\n# Creating an ontology from this is easy\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Alternative syntax for defining the ontology via the OntologyBuilder"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create\nontology_builder = lb.OntologyBuilder()\n# Append tools\ntool_dog = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\")\ntool_cat = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\")\nontology_builder.add_tool(tool_dog)\nontology_builder.add_tool(tool_cat)\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Classifications are supported too (Both for top level and as subclassifications)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"cat\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"name\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"image_quality\",\n            options=[lb.Option(value=\"clear\"),\n                     lb.Option(value=\"blurry\")],\n        )\n    ],\n)\nprint(json.dumps(ontology_builder.asdict(), indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Example of how to add sub-classfication within an option"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder()\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"Global classification\",\n    options=[\n        lb.Option(\n            \"1st option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 1st option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n        lb.Option(\n            \"2nd option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 2nd option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n    ],\n)\n\nontology_builder.add_classification(radio_classification)\n\nontology = client.create_ontology(\"example of nested classification\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* All Tool objects are constructed the same way:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "bbox_tool = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog_box\")\npoly_tool = lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"dog_poly\")\nseg_tool = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"dog_seg\")\npoint_tool = lb.Tool(tool=lb.Tool.Type.POINT, name=\"dog_center\")\nline_tool = lb.Tool(tool=lb.Tool.Type.LINE, name=\"dog_orientation\")\nner_tool = lb.Tool(tool=lb.Tool.Type.NER, name=\"dog_reference\")\nrelationship_tool = lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Classifications are all constructed the same way (except text which doesn't require options)\n",
-                "* Classifications can be global or subclasses to a tool (ie dog bounding box, with a breed classification)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                        name=\"dog_name\")\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"dog_breed\",\n    options=[lb.Option(\"poodle\")],\n)\nchecklist_classification = lb.Classification(\n    class_type=lb.Classification.Type.CHECKLIST,\n    name=\"background\",\n    options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")],\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Ontologies\n",
+        "* An ontology is a collection different tools and classifications that can be used within a project's editor. Each tool or classification is called a \"Feature Schema\". \n",
+        "* Feature Schemas contain information about the tool such as the kind, the name, all subclasses, and other information related to a tool. Feature Schemas can be shared between ontologies. \n",
+        "\n",
+        "* Helpful Links:\n",
+        "    * [Ontology documentation](https://docs.labelbox.com/docs/labelbox-ontology)\n",
+        "    * [Project Setup Using Ontologies](https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/project_setup.ipynb)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install labelbox -q",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Ontology From Normalized Data\n",
+        "* Users can create ontologies from a json definition of the ontology.\n",
+        "* See below `OntologyBuilder` section for more details on constructing the normalized ontology.\n",
+        "* Each tool type requires a specific value be passed:\n",
+        "\n",
+        "| Tool    | Value |\n",
+        "| :----------- | :----------- |\n",
+        "| Bounding box      | rectangle    |\n",
+        "| Polygon           | polygon      |\n",
+        "| Polyline          | line         |\n",
+        "| Point             | point        |\n",
+        "| Segmentation mask | raster-segmentation  |\n",
+        "| Entity            | named-entity |\n",
+        "| Relationship      | edge         |"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# This will automatically create new feature schema\nontology_name = \"sdk-ontology\"\nfeature_schema_cat_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"cat\",\n    \"color\": \"black\",\n}\n\nontology_normalized_json = {\n    \"tools\": [feature_schema_cat_normalized],\n    \"classifications\": [],\n}\nontology = client.create_ontology(name=ontology_name,\n                                  normalized=ontology_normalized_json)\nprint(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Ontology From Existing Feature Schemas\n",
+        "* It is often useful to support the same features in multiple ontologies. \n",
+        "* Labelbox supports this workflow by allowing users to create ontologies using existing feature schemas."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First create the feature schema\nfeature_schema_cat = client.create_feature_schema(feature_schema_cat_normalized)\n# When we create the ontology it will not re-create the feature schema\nprint(feature_schema_cat.uid)\nontology = client.create_ontology_from_feature_schemas(ontology_name,\n                                                       [feature_schema_cat.uid])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Ontology From a Mix of New and Existing Feature Schemas\n",
+        "* If we want to create a new ontology that expands upon a previous ontology it is helpful to be able to share a portion of the features.\n",
+        "* To do this we will create the new schema ids that we want. Then we will create an ontology from the new list of ids.\n",
+        "* Note that for additional customization you can also combine the normalized json and use the create_ontology() method (not covered here)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create new dog schema id\nfeature_schema_dog_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"dog\",\n    \"color\": \"black\",\n    \"classifications\": [],\n}\nfeature_schema_dog = client.create_feature_schema(feature_schema_dog_normalized)\n# The cat is shared between this new ontology and the one we created previously\n# (ie. the cat feature schema will not be re-created)\nontology = client.create_ontology_from_feature_schemas(\n    ontology_name, [feature_schema_cat.uid, feature_schema_dog.uid])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Read\n",
+        "* We can directly query by id for ontologies and feature schemas\n",
+        "* We also can search for both by name"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "#### Fetch by ID\nfeature_schema = client.get_feature_schema(feature_schema_cat.uid)\nontology = client.get_ontology(ontology.uid)\nprint(feature_schema)\nprint(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "#### Search by name\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nontology = next(client.get_ontologies(ontology_name))\nprint(feature_schema)\nprint(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update and Delete"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Check if feature is archived\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nclient.is_feature_schema_archived(ontology_id=ontology.uid,\n                                  feature_schema_id=feature_schema.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Update a feature's title\nclient.update_feature_schema_title(feature_schema_id=feature_schema.uid,\n                                   title=\"cat-2\")\nfeature = client.get_feature_schema(feature_schema_id=feature_schema.uid)\nprint(\"Feature: \", feature)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Replace a feature\ntool = lb.Tool(\n    feature_schema_id=feature_schema.uid,\n    name=\"tool-cat-upserted\",\n    tool=lb.Tool.Type.BBOX,\n    color=\"#FF0000\",\n)\nupserted_feature_schema_id = client.upsert_feature_schema(tool.asdict()).uid\nfeature = client.get_feature_schema(\n    feature_schema_id=upserted_feature_schema_id)\nprint(\"Updated feature: \", feature)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Insert a new feature\ntool = lb.Tool(name=\"tool-cat-2\", tool=lb.Tool.Type.RASTER_SEGMENTATION)\nfeature_schema_id_new = client.create_feature_schema(tool.asdict()).uid\nclient.insert_feature_schema_into_ontology(\n    feature_schema_id=feature_schema_id_new,\n    ontology_id=ontology.uid,\n    position=2,\n)\nprint(\"Updated ontology: \", client.get_ontology(ontology_id=ontology.uid))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Delete or Archived a feature:\n",
+        "\n",
+        "If the feature schema is a root level node with associated labels, it will be archived.\n",
+        "If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
+        "If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.delete_feature_schema_from_ontology(\n    ontology_id=ontology.uid, feature_schema_id=feature_schema_id_new)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Only features with annotations will be archived, features without annotations will be deleted.\nfeature_schema_id_with_annotations = \"\"\nontology_id = \"\"\nclient.unarchive_feature_schema_node(\n    ontology_id=ontology_id,\n    root_feature_schema_id=feature_schema_id_with_annotations,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Ontology Builder\n",
+        "* The ontology builder is a tool for creating and modifying normalized json"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create normalized json with a bounding box and segmentation tool\nontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n    lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\"),\n])\n# Creating an ontology from this is easy\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Alternative syntax for defining the ontology via the OntologyBuilder"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create\nontology_builder = lb.OntologyBuilder()\n# Append tools\ntool_dog = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\")\ntool_cat = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\")\nontology_builder.add_tool(tool_dog)\nontology_builder.add_tool(tool_cat)\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Classifications are supported too (Both for top level and as subclassifications)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"cat\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"name\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"image_quality\",\n            options=[lb.Option(value=\"clear\"),\n                     lb.Option(value=\"blurry\")],\n        )\n    ],\n)\nprint(json.dumps(ontology_builder.asdict(), indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Example of how to add sub-classfication within an option"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder()\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"Global classification\",\n    options=[\n        lb.Option(\n            \"1st option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 1st option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n        lb.Option(\n            \"2nd option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 2nd option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n    ],\n)\n\nontology_builder.add_classification(radio_classification)\n\nontology = client.create_ontology(\"example of nested classification\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* All Tool objects are constructed the same way:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "bbox_tool = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog_box\")\npoly_tool = lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"dog_poly\")\nseg_tool = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"dog_seg\")\npoint_tool = lb.Tool(tool=lb.Tool.Type.POINT, name=\"dog_center\")\nline_tool = lb.Tool(tool=lb.Tool.Type.LINE, name=\"dog_orientation\")\nner_tool = lb.Tool(tool=lb.Tool.Type.NER, name=\"dog_reference\")\nrelationship_tool = lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Classifications are all constructed the same way (except text which doesn't require options)\n",
+        "* Classifications can be global or subclasses to a tool (ie dog bounding box, with a breed classification)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                        name=\"dog_name\")\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"dog_breed\",\n    options=[lb.Option(\"poodle\")],\n)\nchecklist_classification = lb.Classification(\n    class_type=lb.Classification.Type.CHECKLIST,\n    name=\"background\",\n    options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")],\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index 79fe66336..74e9551dd 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -1,343 +1,341 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* A project can be thought of as a specific labeling task on a set of labels\n",
-                "* That set of labels is defined by the data rows attached to the project\n",
-                "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
-                "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
-                "* Also note that deprecated functions are not explained here."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a dataset with data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Add data rows to a project \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create tags and assign them to a project\n",
-                "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create a tag"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Assign the tag to a project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tags = project.update_project_resource_tags([tag.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get project tags"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tags = project.get_resource_tags()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Attach ontology and label data rows\n",
-                "\n",
-                "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create your ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Attach ontology to project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create labels and upload them to project as ground truths"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Move data rows in project to different task queues"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fetch project configuration"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Return number of labeled data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Number of labels:\", project.get_label_count())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Copy labels and data rows from one project to a different project\n",
-                "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Copy data rows and labels\n",
-                "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
-                "\n",
-                "##### Parameters\n",
-                "\n",
-                "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
-                "\n",
-                "* `source_project_id`\n",
-                "    - The id of the project were our data rows with labels will originate.\n",
-                "* `annotation_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Clean Up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* A project can be thought of as a specific labeling task on a set of labels\n",
+        "* That set of labels is defined by the data rows attached to the project\n",
+        "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
+        "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
+        "* Also note that deprecated functions are not explained here."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a project\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a dataset with data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Add data rows to a project \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create tags and assign them to a project\n",
+        "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create a tag"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Assign the tag to a project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tags = project.update_project_resource_tags([tag.uid])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Get project tags"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tags = project.get_resource_tags()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Attach ontology and label data rows\n",
+        "\n",
+        "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create your ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Attach ontology to project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create labels and upload them to project as ground truths"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Move data rows in project to different task queues"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Fetch project configuration"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Return number of labeled data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Number of labels:\", project.get_label_count())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Copy labels and data rows from one project to a different project\n",
+        "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Copy data rows and labels\n",
+        "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
+        "\n",
+        "##### Parameters\n",
+        "\n",
+        "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
+        "\n",
+        "* `source_project_id`\n",
+        "    - The id of the project were our data rows with labels will originate.\n",
+        "* `annotation_ontology_mapping`\n",
+        "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
+        "* `exclude_data_rows_in_project`\n",
+        "    - Excludes data rows that are already in the project. \n",
+        "* `override_existing_annotations_rule` \n",
+        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
+        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+        "        * Options include:\n",
+        "            * ConflictResolutionStrategy.KeepExisting\n",
+        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+        "* `param batch_priority`\n",
+        "    - The priority of the batch."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Clean Up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index ad1db4bf2..77845f620 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -1,248 +1,246 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# User Management\n",
-                "* This notebook covers the following:\n",
-                "    * create invites\n",
-                "    * query for remaining allowed invites to an organization\n",
-                "    * set and update organization roles\n",
-                "    * assign users to projects\n",
-                "        * set / update / revoke project role\n",
-                "    * delete users from org"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport os",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* You have to specifically enable experimental features to use this functionality. Notice the \n",
-                "`enable_experimental = True`\n",
-                "    * enables users to send invites and checking the number of seats available via the sdk"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Roles\n",
-                "* When inviting a new user to an organization, there are various roles to select from.\n",
-                "* All available roles to your org can be accessed via `client.get_roles()`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Above we printed out all of the roles available to the current org.\n",
-                "* Notice the `NONE`. That is for project level roles"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create\n",
-                "* Users are created by sending an invite\n",
-                "* An email will be sent to them and they will be asked to join your organization"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Organization Level Permissions\n",
-                "* Invite a new labeler with labeling permissions on all projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Project Level Permissions\n",
-                "* Invite a new labeler with labeling permissions specific to a set of projects\n",
-                "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Read\n",
-                "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
-                "* You are able to query for members once they have joined."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "users = list(organization.users())\nprint(users[0])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update\n",
-                "* There is no update on invites. Instead you must delete and resend them\n",
-                "* You can update User roles"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Delete"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Invites can only be deleted from the ui at this time. \n",
-                "* Deleting invites can be done in the members tab of the web app."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Delete the User\n",
-                "* Make sure you want to remove the user from the org:\n",
-                "* `>>> organization.remove_user(user)`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Cleanup\n",
-                "* We created an extra project. Let's delete it"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# User Management\n",
+        "* This notebook covers the following:\n",
+        "    * create invites\n",
+        "    * query for remaining allowed invites to an organization\n",
+        "    * set and update organization roles\n",
+        "    * assign users to projects\n",
+        "        * set / update / revoke project role\n",
+        "    * delete users from org"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport os",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* You have to specifically enable experimental features to use this functionality. Notice the \n",
+        "`enable_experimental = True`\n",
+        "    * enables users to send invites and checking the number of seats available via the sdk"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Roles\n",
+        "* When inviting a new user to an organization, there are various roles to select from.\n",
+        "* All available roles to your org can be accessed via `client.get_roles()`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Above we printed out all of the roles available to the current org.\n",
+        "* Notice the `NONE`. That is for project level roles"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create\n",
+        "* Users are created by sending an invite\n",
+        "* An email will be sent to them and they will be asked to join your organization"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Organization Level Permissions\n",
+        "* Invite a new labeler with labeling permissions on all projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Project Level Permissions\n",
+        "* Invite a new labeler with labeling permissions specific to a set of projects\n",
+        "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Read\n",
+        "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
+        "* You are able to query for members once they have joined."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "users = list(organization.users())\nprint(users[0])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update\n",
+        "* There is no update on invites. Instead you must delete and resend them\n",
+        "* You can update User roles"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Delete"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Invites can only be deleted from the ui at this time. \n",
+        "* Deleting invites can be done in the members tab of the web app."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Delete the User\n",
+        "* Make sure you want to remove the user from the org:\n",
+        "* `>>> organization.remove_user(user)`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Cleanup\n",
+        "* We created an extra project. Let's delete it"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/exports/composite_mask_export.ipynb b/examples/exports/composite_mask_export.ipynb
index 5c848da2b..fc21d0c85 100644
--- a/examples/exports/composite_mask_export.ipynb
+++ b/examples/exports/composite_mask_export.ipynb
@@ -1,257 +1,255 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Export composite masks \n",
-                "##### **Composite masks are only available on raster segmentation projects**\n",
-                "\n",
-                "Composite masks are a combination of mask instances grouped in a single mask URL. \n",
-                "\n",
-                "The purpose of this demo is to demonstrate how to transition from exporting single masks to exporting composite masks. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Key differences between single mask instance and composite mask."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Composite masks\n",
-                "**IMPORTANT :** The URL for the ```composite_mask``` from exports older than 30 days will no longer be accessible. To obtain a functional URL after this period, please generate a new export for the mask.\n",
-                "1. A single mask url contains all mask instances from a single label. For videos a composite mask contains all mask instances for the frame in each label. \n",
-                "2. The export and mask URL adhere to the following convention:\n",
-                "  - ***Image example***\n",
-                "```json \n",
-                "  {\n",
-                "    \"composite_mask\": {\n",
-                "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
-                "      \"color_rgb\": [\n",
-                "          142,\n",
-                "          220,\n",
-                "          196\n",
-                "      ]\n",
-                "    }\n",
-                "  }\n",
-                "```\n",
-                "  - ***Video example*** :\n",
-                "  The export will adhere to the following URL convention by default.\n",
-                "```json\n",
-                "  {\n",
-                "    \"composite_mask\": {\n",
-                "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/{frame_number}\",\n",
-                "      \"color_rgb\": [\n",
-                "        224,\n",
-                "        17,\n",
-                "        103\n",
-                "      ]\n",
-                "    }\n",
-                "  }\n",
-                "```\n",
-                "3. A unique RGB color is assigned to each mask instance. The example below shows a composite mask of a label, and while it contains all mask instances, only the RGB color associated with this particular annotation will be filled in under the ```color_rgb``` field."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Example on how to fetch a composite mask\n# The mask here shows all the mask instances associated with a label\ntask_id = \"\"\ncomposite_mask_id = \"\"\n\nmask_url = f\"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\"\nreq = urllib.request.Request(mask_url, headers=client.headers)\nimage = Image.open(urllib.request.urlopen(req))\nw, h = image.size\nnew_w = w // 4\nnew_h = h // 4\n\nimage.resize((new_w, new_h), Image.BICUBIC)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Here's an example of an entry featuring a composite mask (see image above) containing the mask instance's RGB color uniquely associated with the annotation.\n",
-                "\n",
-                "```json\n",
-                "    {\n",
-                "      \"feature_id\": \"clpk3ow9u006f14vs2w5qa9l3\",\n",
-                "      \"feature_schema_id\": \"clpk3nvrv05bh08ua8fwqavng\",\n",
-                "      \"name\": \"mask\",\n",
-                "      \"value\": \"mask\",\n",
-                "      \"annotation_kind\": \"ImageSegmentationMask\",\n",
-                "      \"classifications\": [],\n",
-                "      \"composite_mask\": {\n",
-                "          \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
-                "          \"color_rgb\": [\n",
-                "              123,\n",
-                "              103,\n",
-                "              152\n",
-                "          ]\n",
-                "      }\n",
-                "    }\n",
-                "```\n",
-                "- rgb(123,103,152) = Purple\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "---"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Single mask instance:\n",
-                "We are planning on removing single mask instances, but for now they will be displayed adjecent to the composite mask.\n",
-                "1. A single mask instance and mask url is generated for each individual annotation for each label.\n",
-                "2. The export and mask URL adhere to the following convention:  \n",
-                "```json\n",
-                "      {\n",
-                "        \"mask\": {\n",
-                "              \"url\": \"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
-                "          }\n",
-                "      }\n",
-                "\n",
-                "```\n",
-                "3. RGB color is not present"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create an export from an Image project with mask annotations\n",
-                "To better showcase composite masks, make sure you have different mask tools and mask annotations in your project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.has_result():\n    print(export_task.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "stream = export_task.get_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json_str)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create an export from a Video project with mask annotations "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "VIDEO_PROJECT_ID = \"\"\nproject_video = client.get_project(VIDEO_PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task_video = project_video.export(params=export_params, filters=filters)\nexport_task_video.wait_till_done()\n\nif export_task_video.has_result():\n    print(export_task_video.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool from each frame"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tools_frames_color = {}\nstream = export_task_video.get_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json_str)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Export composite masks \n",
+        "##### **Composite masks are only available on raster segmentation projects**\n",
+        "\n",
+        "Composite masks are a combination of mask instances grouped in a single mask URL. \n",
+        "\n",
+        "The purpose of this demo is to demonstrate how to transition from exporting single masks to exporting composite masks. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Key differences between single mask instance and composite mask."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Composite masks\n",
+        "**IMPORTANT :** The URL for the ```composite_mask``` from exports older than 30 days will no longer be accessible. To obtain a functional URL after this period, please generate a new export for the mask.\n",
+        "1. A single mask url contains all mask instances from a single label. For videos a composite mask contains all mask instances for the frame in each label. \n",
+        "2. The export and mask URL adhere to the following convention:\n",
+        "  - ***Image example***\n",
+        "```json \n",
+        "  {\n",
+        "    \"composite_mask\": {\n",
+        "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
+        "      \"color_rgb\": [\n",
+        "          142,\n",
+        "          220,\n",
+        "          196\n",
+        "      ]\n",
+        "    }\n",
+        "  }\n",
+        "```\n",
+        "  - ***Video example*** :\n",
+        "  The export will adhere to the following URL convention by default.\n",
+        "```json\n",
+        "  {\n",
+        "    \"composite_mask\": {\n",
+        "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/{frame_number}\",\n",
+        "      \"color_rgb\": [\n",
+        "        224,\n",
+        "        17,\n",
+        "        103\n",
+        "      ]\n",
+        "    }\n",
+        "  }\n",
+        "```\n",
+        "3. A unique RGB color is assigned to each mask instance. The example below shows a composite mask of a label, and while it contains all mask instances, only the RGB color associated with this particular annotation will be filled in under the ```color_rgb``` field."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Example on how to fetch a composite mask\n# The mask here shows all the mask instances associated with a label\ntask_id = \"\"\ncomposite_mask_id = \"\"\n\nmask_url = f\"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\"\nreq = urllib.request.Request(mask_url, headers=client.headers)\nimage = Image.open(urllib.request.urlopen(req))\nw, h = image.size\nnew_w = w // 4\nnew_h = h // 4\n\nimage.resize((new_w, new_h), Image.BICUBIC)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Here's an example of an entry featuring a composite mask (see image above) containing the mask instance's RGB color uniquely associated with the annotation.\n",
+        "\n",
+        "```json\n",
+        "    {\n",
+        "      \"feature_id\": \"clpk3ow9u006f14vs2w5qa9l3\",\n",
+        "      \"feature_schema_id\": \"clpk3nvrv05bh08ua8fwqavng\",\n",
+        "      \"name\": \"mask\",\n",
+        "      \"value\": \"mask\",\n",
+        "      \"annotation_kind\": \"ImageSegmentationMask\",\n",
+        "      \"classifications\": [],\n",
+        "      \"composite_mask\": {\n",
+        "          \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
+        "          \"color_rgb\": [\n",
+        "              123,\n",
+        "              103,\n",
+        "              152\n",
+        "          ]\n",
+        "      }\n",
+        "    }\n",
+        "```\n",
+        "- rgb(123,103,152) = Purple\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "---"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Single mask instance:\n",
+        "We are planning on removing single mask instances, but for now they will be displayed adjecent to the composite mask.\n",
+        "1. A single mask instance and mask url is generated for each individual annotation for each label.\n",
+        "2. The export and mask URL adhere to the following convention:  \n",
+        "```json\n",
+        "      {\n",
+        "        \"mask\": {\n",
+        "              \"url\": \"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
+        "          }\n",
+        "      }\n",
+        "\n",
+        "```\n",
+        "3. RGB color is not present"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create an export from an Image project with mask annotations\n",
+        "To better showcase composite masks, make sure you have different mask tools and mask annotations in your project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.has_result():\n    print(export_task.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "stream = export_task.get_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json_str)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create an export from a Video project with mask annotations "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "VIDEO_PROJECT_ID = \"\"\nproject_video = client.get_project(VIDEO_PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task_video = project_video.export(params=export_params, filters=filters)\nexport_task_video.wait_till_done()\n\nif export_task_video.has_result():\n    print(export_task_video.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool from each frame"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tools_frames_color = {}\nstream = export_task_video.get_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json_str)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/exports/export_data.ipynb b/examples/exports/export_data.ipynb
index 6b5820e7e..0f31eb823 100644
--- a/examples/exports/export_data.ipynb
+++ b/examples/exports/export_data.ipynb
@@ -1,479 +1,477 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Export data\n",
-                "How to export data for projects, datasets, slices, data rows and models, with examples for each type of v2 export along with details on optional parameters and filters.\n",
-                "\n",
-                "***Beginning with SDK version 3.68, the `export_v2()` method has been enhanced to incorporate streamable functionality.***"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q urllib3",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport time",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a project\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When you export data rows from a project, you may choose to include or exclude certain attributes, including:\n",
-                "- `attachments`\n",
-                "- `metadata_fields`\n",
-                "- `data_row_details`\n",
-                "- `project_details`\n",
-                "- `label_details`\n",
-                "- `performance_details`\n",
-                "- `interpolated_frames`\n",
-                "    - Only applicable for video data rows.\n",
-                "\n",
-                "### Filters\n",
-                "When you export data rows from a project, you can specify the included data rows with the following filters:\n",
-                "- `last_activity_at`\n",
-                "- `label_created_at`\n",
-                "- `data_row_ids`\n",
-                "- `global_keys`\n",
-                "- `batch_ids`\n",
-                "- `workflow_status`\n",
-                "\n",
-                "#### Filter details\n",
-                "You can set the range for `last_activity_at` and `label_created_at` in the following formats: \n",
-                "- `YYYY-MM-DD`\n",
-                "- `YYYY-MM-DD hh:mm:ss`\n",
-                "- `YYYY-MM-DDThh:mm:ss\u00b1hhmm` (ISO 8601)\n",
-                "\n",
-                "The ISO 8061 format allows you to specify the timezone, while the other two formats assume timezone from the user's workspace settings.\n",
-                "\n",
-                "The `last_activity_at` filter captures the creation and modification of labels, metadata, workflow status, comments, and reviews.\n",
-                "\n",
-                "If you wish to specify data rows to export, uncomment the `data_row_ids` or `global_keys` filter and provide a list of applicable IDs. The data rows must be part of a batch attached to the project in question. You can provide up to 2,000 data row IDs.\n",
-                "\n",
-                "The `batch_ids` filter allows you to specify data rows for export based on their associated batch ID. This is particularly useful when `data_row_ids` is not sufficient due to 2,000 data row IDs limit. \n",
-                "\n",
-                "\n",
-                "The `workflow_status` filter allows you to specify data rows in a given work flow step. This filter only accepts one value. For example, `filters = {\"workflow_status\": \"InReview\"}`. The filer accepts the following:\n",
-                "- `ToLabel`\n",
-                "- `InReview`\n",
-                "- `InRework`\n",
-                "- `Done`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nexport_task = project.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nclient.enable_experimental = True\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Uncomment to get stream results as a written file\n\n# Provide results with file converter\n\n# if export_task.has_errors():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./errors.txt\"),\n#         stream_type=lb.StreamType.ERRORS\n#     ).start()\n\n# if export_task.has_result():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./result.txt\"),\n#         stream_type=lb.StreamType.RESULT\n#     ).start()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a dataset\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When you export data rows from a dataset, you may choose to include or exclude certain attributes, including:\n",
-                "- `attachments`\n",
-                "- `metadata_fields`\n",
-                "- `data_row_details`\n",
-                "- `project_details`\n",
-                "- `label_details`\n",
-                "- `performance_details`\n",
-                "- `interpolated_frames`\n",
-                "    - Only applicable for video data rows.\n",
-                "- `project_ids`\n",
-                "    - Accepts a list of project IDs. If provided, the labels created _in these projects_ on the exported data rows will be included. \n",
-                "- `model_run_ids`\n",
-                "    - Accepts a list of model run IDs. If provided, the labels and predicitions created _in these model runs_ will be included. \n",
-                "\n",
-                "### Filters\n",
-                "When you export data rows from a dataset, you can specify the included data rows with the following filters:\n",
-                "- `last_activity_at`\n",
-                "- `label_created_at`\n",
-                "- `data_row_ids`\n",
-                "- `global_keys`\n",
-                "\n",
-                "See the _Export data rows from a project_ section above for additional details on each filter. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the dataset ID of the dataset from which you wish to export data rows.\nDATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nexport_task = dataset.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nclient.enable_experimental = True\n\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from Catalog\n",
-                "`get_catalog()` method allows you to fetch all your data from catalog\n",
-                "\n",
-                "### Parameters\n",
-                "When exporting from catalog, you can apply the same parameters as exporting from a project.\n",
-                "\n",
-                "### Filters\n",
-                "When exporting from catalog, you can apply the same filters as exporting from a dataset.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "catalog = client.get_catalog()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\nexport_task = catalog.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\n\nclient.enable_experimental = True\nexport_task = catalog.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "def json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a slice\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When exporting from a slice, you can apply the same parameters as exporting from a dataset.\n",
-                "\n",
-                "### Filters\n",
-                "No filters are applicable to exports from a slice. All the data rows of the slice must be exported."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the Catalog slice ID of the slice from which you wish to export data rows.\nCATALOG_SLICE_ID = \"\"\ncatalog_slice = client.get_catalog_slice(CATALOG_SLICE_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nexport_task = catalog_slice.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nclient.enable_experimental = True\n\nexport_task = catalog_slice.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a model run\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "- `attachments`\n",
-                "- `metadata_fields`\n",
-                "- `data_row_details`\n",
-                "- `interpolated_frames`\n",
-                "    - Only applicable for video data rows.\n",
-                "- `predictions`\n",
-                "    - If true, all predictions made in the model run will be included for each data row in the export.\n",
-                "\n",
-                "### Filters\n",
-                "No filters are applicable to exports from a model run. All the data rows of the model run must be exported.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the model run ID of the model run from which you wish to export data rows.\nMODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nexport_task = model_run.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export Data Row\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When exporting data rows, you can apply the same parameters as exporting from a project.\n",
-                "\n",
-                "### Filters\n",
-                "No filters are applicable to export data rows. All the data rows specified in the export task are included."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the global key of the data row you wish to export\nDATA_ROW_GLOBAL_KEY = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export_v2(client=client,\n                                   global_keys=[DATA_ROW_GLOBAL_KEY],\n                                   params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export(client=client,\n                                global_keys=[DATA_ROW_GLOBAL_KEY],\n                                params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Export data\n",
+        "How to export data for projects, datasets, slices, data rows and models, with examples for each type of v2 export along with details on optional parameters and filters.\n",
+        "\n",
+        "***Beginning with SDK version 3.68, the `export_v2()` method has been enhanced to incorporate streamable functionality.***"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q urllib3",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport time",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a project\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When you export data rows from a project, you may choose to include or exclude certain attributes, including:\n",
+        "- `attachments`\n",
+        "- `metadata_fields`\n",
+        "- `data_row_details`\n",
+        "- `project_details`\n",
+        "- `label_details`\n",
+        "- `performance_details`\n",
+        "- `interpolated_frames`\n",
+        "    - Only applicable for video data rows.\n",
+        "\n",
+        "### Filters\n",
+        "When you export data rows from a project, you can specify the included data rows with the following filters:\n",
+        "- `last_activity_at`\n",
+        "- `label_created_at`\n",
+        "- `data_row_ids`\n",
+        "- `global_keys`\n",
+        "- `batch_ids`\n",
+        "- `workflow_status`\n",
+        "\n",
+        "#### Filter details\n",
+        "You can set the range for `last_activity_at` and `label_created_at` in the following formats: \n",
+        "- `YYYY-MM-DD`\n",
+        "- `YYYY-MM-DD hh:mm:ss`\n",
+        "- `YYYY-MM-DDThh:mm:ss\u00b1hhmm` (ISO 8601)\n",
+        "\n",
+        "The ISO 8061 format allows you to specify the timezone, while the other two formats assume timezone from the user's workspace settings.\n",
+        "\n",
+        "The `last_activity_at` filter captures the creation and modification of labels, metadata, workflow status, comments, and reviews.\n",
+        "\n",
+        "If you wish to specify data rows to export, uncomment the `data_row_ids` or `global_keys` filter and provide a list of applicable IDs. The data rows must be part of a batch attached to the project in question. You can provide up to 2,000 data row IDs.\n",
+        "\n",
+        "The `batch_ids` filter allows you to specify data rows for export based on their associated batch ID. This is particularly useful when `data_row_ids` is not sufficient due to 2,000 data row IDs limit. \n",
+        "\n",
+        "\n",
+        "The `workflow_status` filter allows you to specify data rows in a given work flow step. This filter only accepts one value. For example, `filters = {\"workflow_status\": \"InReview\"}`. The filer accepts the following:\n",
+        "- `ToLabel`\n",
+        "- `InReview`\n",
+        "- `InRework`\n",
+        "- `Done`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nexport_task = project.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nclient.enable_experimental = True\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Uncomment to get stream results as a written file\n\n# Provide results with file converter\n\n# if export_task.has_errors():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./errors.txt\"),\n#         stream_type=lb.StreamType.ERRORS\n#     ).start()\n\n# if export_task.has_result():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./result.txt\"),\n#         stream_type=lb.StreamType.RESULT\n#     ).start()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a dataset\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When you export data rows from a dataset, you may choose to include or exclude certain attributes, including:\n",
+        "- `attachments`\n",
+        "- `metadata_fields`\n",
+        "- `data_row_details`\n",
+        "- `project_details`\n",
+        "- `label_details`\n",
+        "- `performance_details`\n",
+        "- `interpolated_frames`\n",
+        "    - Only applicable for video data rows.\n",
+        "- `project_ids`\n",
+        "    - Accepts a list of project IDs. If provided, the labels created _in these projects_ on the exported data rows will be included. \n",
+        "- `model_run_ids`\n",
+        "    - Accepts a list of model run IDs. If provided, the labels and predicitions created _in these model runs_ will be included. \n",
+        "\n",
+        "### Filters\n",
+        "When you export data rows from a dataset, you can specify the included data rows with the following filters:\n",
+        "- `last_activity_at`\n",
+        "- `label_created_at`\n",
+        "- `data_row_ids`\n",
+        "- `global_keys`\n",
+        "\n",
+        "See the _Export data rows from a project_ section above for additional details on each filter. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the dataset ID of the dataset from which you wish to export data rows.\nDATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nexport_task = dataset.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nclient.enable_experimental = True\n\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from Catalog\n",
+        "`get_catalog()` method allows you to fetch all your data from catalog\n",
+        "\n",
+        "### Parameters\n",
+        "When exporting from catalog, you can apply the same parameters as exporting from a project.\n",
+        "\n",
+        "### Filters\n",
+        "When exporting from catalog, you can apply the same filters as exporting from a dataset.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "catalog = client.get_catalog()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\nexport_task = catalog.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\n\nclient.enable_experimental = True\nexport_task = catalog.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "def json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a slice\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When exporting from a slice, you can apply the same parameters as exporting from a dataset.\n",
+        "\n",
+        "### Filters\n",
+        "No filters are applicable to exports from a slice. All the data rows of the slice must be exported."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the Catalog slice ID of the slice from which you wish to export data rows.\nCATALOG_SLICE_ID = \"\"\ncatalog_slice = client.get_catalog_slice(CATALOG_SLICE_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nexport_task = catalog_slice.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nclient.enable_experimental = True\n\nexport_task = catalog_slice.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a model run\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "- `attachments`\n",
+        "- `metadata_fields`\n",
+        "- `data_row_details`\n",
+        "- `interpolated_frames`\n",
+        "    - Only applicable for video data rows.\n",
+        "- `predictions`\n",
+        "    - If true, all predictions made in the model run will be included for each data row in the export.\n",
+        "\n",
+        "### Filters\n",
+        "No filters are applicable to exports from a model run. All the data rows of the model run must be exported.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the model run ID of the model run from which you wish to export data rows.\nMODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nexport_task = model_run.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export Data Row\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When exporting data rows, you can apply the same parameters as exporting from a project.\n",
+        "\n",
+        "### Filters\n",
+        "No filters are applicable to export data rows. All the data rows specified in the export task are included."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the global key of the data row you wish to export\nDATA_ROW_GLOBAL_KEY = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export_v2(client=client,\n                                   global_keys=[DATA_ROW_GLOBAL_KEY],\n                                   params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export(client=client,\n                                global_keys=[DATA_ROW_GLOBAL_KEY],\n                                params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
index b366c3931..1eea6203f 100644
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ b/examples/extras/classification_confusion_matrix.ipynb
@@ -1,109 +1,107 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Classification Confusion matrix\n",
-                "\n",
-                "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
-                "\n",
-                "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
-                "\n",
-                "## Installs"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\" scikit-learn matplotlib ipywidgets",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Imports and etc\n\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.metrics import classification_report\nimport matplotlib.pyplot as plt\nimport ipywidgets as widgets\nimport labelbox as lb\n\n\ndef get_labelers():\n    labels = project.label_generator()\n    labelers = []\n\n    for l in labels:\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    return labelers\n\n\ndef get_paired_labels(labeler1, labeler2):\n    labels = project.label_generator()\n    l1 = []\n    l2 = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        # Check if benchmark\n        if l.extra[\"Benchmark ID\"]:\n            label_dict[\n                l.data.uid][\"Benchmarks\"] = l.annotations[0].value.answer.name\n\n        # Skip labels not created by either of our labelers\n        if (l.extra[\"Created By\"] != labeler1 and\n                l.extra[\"Created By\"] != labeler2):\n            continue\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n    for uid in label_dict.keys():\n        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n            l1.append(label_dict[uid][labeler1])\n            l2.append(label_dict[uid][labeler2])\n\n    return l1, l2\n\n\ndef get_label_dict():\n    labels = project.label_generator()\n    labelers = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    print(labelers)\n    return label_dict, labelers",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Labelbox API + project and dataset\n\n# Add your api key\nAPI_KEY = None\nlb = lb.Client(api_key=API_KEY)\n\n# Add your project and dataset uids here\nproject = lb.get_project(\"xxx\")\ndataset = lb.get_dataset(\"xxx\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Labeler Selection"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Selectors\n\nlabelers = get_labelers()\n\nitems = [\n    widgets.Dropdown(\n        options=[\"Benchmarks\"] + labelers,\n        description=\"Compare this:\",\n        disabled=False,\n    ),\n    widgets.Dropdown(\n        options=labelers,\n        description=\"Against this:\",\n        disabled=False,\n    ),\n]\n\nwidgets.VBox(items)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Confusion Matrix"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Plot Matrix\n\nl1, l2 = get_paired_labels(items[0].value, items[1].value)\n\nfig, ax = plt.subplots(figsize=(15, 15))\nc = ConfusionMatrixDisplay.from_predictions(\n    l1,\n    l2,\n    labels=list(set(l1)),\n    sample_weight=None,\n    normalize=None,\n    display_labels=None,\n    include_values=True,\n    xticks_rotation=\"vertical\",\n    values_format=None,\n    cmap=\"Blues\",\n    ax=ax,\n    colorbar=False,\n)\n\n_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n\ncr = classification_report(l1,\n                           l2,\n                           output_dict=False,\n                           target_names=list(set(l1)),\n                           zero_division=1)\n\nprint(cr)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Classification Confusion matrix\n",
+        "\n",
+        "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
+        "\n",
+        "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
+        "\n",
+        "## Installs"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\" scikit-learn matplotlib ipywidgets",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Imports and etc\n\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.metrics import classification_report\nimport matplotlib.pyplot as plt\nimport ipywidgets as widgets\nimport labelbox as lb\n\n\ndef get_labelers():\n    labels = project.label_generator()\n    labelers = []\n\n    for l in labels:\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    return labelers\n\n\ndef get_paired_labels(labeler1, labeler2):\n    labels = project.label_generator()\n    l1 = []\n    l2 = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        # Check if benchmark\n        if l.extra[\"Benchmark ID\"]:\n            label_dict[\n                l.data.uid][\"Benchmarks\"] = l.annotations[0].value.answer.name\n\n        # Skip labels not created by either of our labelers\n        if (l.extra[\"Created By\"] != labeler1 and\n                l.extra[\"Created By\"] != labeler2):\n            continue\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n    for uid in label_dict.keys():\n        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n            l1.append(label_dict[uid][labeler1])\n            l2.append(label_dict[uid][labeler2])\n\n    return l1, l2\n\n\ndef get_label_dict():\n    labels = project.label_generator()\n    labelers = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    print(labelers)\n    return label_dict, labelers",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Labelbox API + project and dataset\n\n# Add your api key\nAPI_KEY = None\nlb = lb.Client(api_key=API_KEY)\n\n# Add your project and dataset uids here\nproject = lb.get_project(\"xxx\")\ndataset = lb.get_dataset(\"xxx\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Labeler Selection"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Selectors\n\nlabelers = get_labelers()\n\nitems = [\n    widgets.Dropdown(\n        options=[\"Benchmarks\"] + labelers,\n        description=\"Compare this:\",\n        disabled=False,\n    ),\n    widgets.Dropdown(\n        options=labelers,\n        description=\"Against this:\",\n        disabled=False,\n    ),\n]\n\nwidgets.VBox(items)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Confusion Matrix"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Plot Matrix\n\nl1, l2 = get_paired_labels(items[0].value, items[1].value)\n\nfig, ax = plt.subplots(figsize=(15, 15))\nc = ConfusionMatrixDisplay.from_predictions(\n    l1,\n    l2,\n    labels=list(set(l1)),\n    sample_weight=None,\n    normalize=None,\n    display_labels=None,\n    include_values=True,\n    xticks_rotation=\"vertical\",\n    values_format=None,\n    cmap=\"Blues\",\n    ax=ax,\n    colorbar=False,\n)\n\n_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n\ncr = classification_report(l1,\n                           l2,\n                           output_dict=False,\n                           target_names=list(set(l1)),\n                           zero_division=1)\n\nprint(cr)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/foundry/object_detection.ipynb b/examples/foundry/object_detection.ipynb
index d1a6a6144..7c04afaca 100644
--- a/examples/foundry/object_detection.ipynb
+++ b/examples/foundry/object_detection.ipynb
@@ -1,260 +1,258 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Foundry overview\n",
-                "\n",
-                "This notebook is used to go over the basic of foundry through the Python SDK"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:\n",
-                "\n",
-                "* Predict (infer) labels from your data\n",
-                "* Compare the performance of different foundational models with your data and ontologies.\n",
-                "* Prototype, diagnose, and refine a machine learning app to solve specific business needs.\n",
-                "\n",
-                "Foundry creates model runs that predict data row annotations based on your input."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q labelbox",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# End-to-end example: Run foundry and send to annotate from catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into catelog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology that matches model\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your model and data type.\n",
-                "\n",
-                "For example, when using Amazon Rekognition you would need to create a bounding box annotation for your ontology since it only supports object detection. Likewise when using YOLOv8 you would need to create a classification annotation for your ontology since it only supports image classification. \n",
-                "\n",
-                "In this tutorial, we will use Amazon Rekognition to detect objects in an image dataset. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create ontology with two bounding boxes that is included with Amazon Rekognition: Car and Person\nontology_builder = lb.OntologyBuilder(\n    classifications=[],\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Car\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Person\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Bounding Box Annotation Demo Foundry\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a labeling project\n",
-                "\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Foundry Image Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Create foundry application in UI\n",
-                "\n",
-                "Currently we do not support this workflow through the SDK\n",
-                "#### Workflow:\n",
-                "\n",
-                "1. Navigate to model and select ***Create*** > ***App***\n",
-                "\n",
-                "2. Select ***Amazon Rekognition*** and name your foundry application\n",
-                "\n",
-                "3. Customize your perimeters and then select ***Save & Create***"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Select your foundry application inside the UI and copy the APP ID from the top right corner\nAMAZON_REKOGNITION_APP_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Run foundry app on data rows\n",
-                "\n",
-                "This step is meant to generate annotations that can later be reused as pre-labels in a project. You must provide your app ID from the previous step for this method to run, please see the [Foundry Apps Guide](https://docs.labelbox.com/docs/foundry-apps#run-app-using-sdk) for more information.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "task = client.run_foundry_app(\n    model_run_name=f\"Amazon-{str(uuid.uuid4())}\",\n    data_rows=lb.GlobalKeys([global_key]),  # Provide a list of global keys\n    app_id=AMAZON_REKOGNITION_APP_ID,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\n\n# Obtain model run ID from task\nMODEL_RUN_ID = task.metadata[\"modelRunId\"]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Map ontology through the UI\n",
-                "\n",
-                "Mapping a model's ontology to a project's ontology is currently not supported through the SDK, however, to showcase how to send foundry predictions to a project, we are going to generate the mapping of the foundry app ontology to the project ontology through the UI.\n",
-                "\n",
-                "#### Workflow\n",
-                "\n",
-                "1. Navigate to your dataset you created for your model run\n",
-                "2. Select ***Select all*** in the top right corner\n",
-                "3. Select ***Manage selection*** > ***Send to Annotate***\n",
-                "4. Specify the project we created from the project dropdown menu\n",
-                "5. Selecting a workflow step is not required since we are not sending annotations from the UI to a project using this notebook \n",
-                "6. Mark ***Include model predictions*** then scroll down and select ***Map***\n",
-                "7. Select the incoming ontology and matching ontology feature for both Car and Person\n",
-                "8. Once both features are mapped press the ***Copy ontology mapping as JSON*** in the top right corner\n",
-                "9. Do not save this configuration, since we are not sending predictions to a project using this UI modal. We will be sending predictions in the following steps using the SDK"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Copy map ontology through the UI then paste JSON file here\nPREDICTIONS_ONTOLOGY_MAPPING = {}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send model generated annotations from catalog to annotate\n",
-                "\n",
-                "### Parameters\n",
-                "\n",
-                "When you send predicted data rows to annotate from catalog, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
-                "\n",
-                "* `predictions_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run = client.get_model_run(MODEL_RUN_ID)\n\nsend_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    None,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths or obtain task queue id through project.task_queues().\n    batch_name=\"Foundry Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [],
-            "cell_type": "markdown"
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Foundry overview\n",
+        "\n",
+        "This notebook is used to go over the basic of foundry through the Python SDK"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:\n",
+        "\n",
+        "* Predict (infer) labels from your data\n",
+        "* Compare the performance of different foundational models with your data and ontologies.\n",
+        "* Prototype, diagnose, and refine a machine learning app to solve specific business needs.\n",
+        "\n",
+        "Foundry creates model runs that predict data row annotations based on your input."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q labelbox",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# End-to-end example: Run foundry and send to annotate from catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into catelog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology that matches model\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your model and data type.\n",
+        "\n",
+        "For example, when using Amazon Rekognition you would need to create a bounding box annotation for your ontology since it only supports object detection. Likewise when using YOLOv8 you would need to create a classification annotation for your ontology since it only supports image classification. \n",
+        "\n",
+        "In this tutorial, we will use Amazon Rekognition to detect objects in an image dataset. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create ontology with two bounding boxes that is included with Amazon Rekognition: Car and Person\nontology_builder = lb.OntologyBuilder(\n    classifications=[],\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Car\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Person\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Bounding Box Annotation Demo Foundry\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a labeling project\n",
+        "\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Foundry Image Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Create foundry application in UI\n",
+        "\n",
+        "Currently we do not support this workflow through the SDK\n",
+        "#### Workflow:\n",
+        "\n",
+        "1. Navigate to model and select ***Create*** > ***App***\n",
+        "\n",
+        "2. Select ***Amazon Rekognition*** and name your foundry application\n",
+        "\n",
+        "3. Customize your perimeters and then select ***Save & Create***"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Select your foundry application inside the UI and copy the APP ID from the top right corner\nAMAZON_REKOGNITION_APP_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Run foundry app on data rows\n",
+        "\n",
+        "This step is meant to generate annotations that can later be reused as pre-labels in a project. You must provide your app ID from the previous step for this method to run, please see the [Foundry Apps Guide](https://docs.labelbox.com/docs/foundry-apps#run-app-using-sdk) for more information.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "task = client.run_foundry_app(\n    model_run_name=f\"Amazon-{str(uuid.uuid4())}\",\n    data_rows=lb.GlobalKeys([global_key]),  # Provide a list of global keys\n    app_id=AMAZON_REKOGNITION_APP_ID,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\n\n# Obtain model run ID from task\nMODEL_RUN_ID = task.metadata[\"modelRunId\"]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Map ontology through the UI\n",
+        "\n",
+        "Mapping a model's ontology to a project's ontology is currently not supported through the SDK, however, to showcase how to send foundry predictions to a project, we are going to generate the mapping of the foundry app ontology to the project ontology through the UI.\n",
+        "\n",
+        "#### Workflow\n",
+        "\n",
+        "1. Navigate to your dataset you created for your model run\n",
+        "2. Select ***Select all*** in the top right corner\n",
+        "3. Select ***Manage selection*** > ***Send to Annotate***\n",
+        "4. Specify the project we created from the project dropdown menu\n",
+        "5. Selecting a workflow step is not required since we are not sending annotations from the UI to a project using this notebook \n",
+        "6. Mark ***Include model predictions*** then scroll down and select ***Map***\n",
+        "7. Select the incoming ontology and matching ontology feature for both Car and Person\n",
+        "8. Once both features are mapped press the ***Copy ontology mapping as JSON*** in the top right corner\n",
+        "9. Do not save this configuration, since we are not sending predictions to a project using this UI modal. We will be sending predictions in the following steps using the SDK"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Copy map ontology through the UI then paste JSON file here\nPREDICTIONS_ONTOLOGY_MAPPING = {}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send model generated annotations from catalog to annotate\n",
+        "\n",
+        "### Parameters\n",
+        "\n",
+        "When you send predicted data rows to annotate from catalog, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
+        "\n",
+        "* `predictions_ontology_mapping`\n",
+        "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
+        "* `exclude_data_rows_in_project`\n",
+        "    - Excludes data rows that are already in the project. \n",
+        "* `override_existing_annotations_rule` \n",
+        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
+        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+        "        * Options include:\n",
+        "            * ConflictResolutionStrategy.KeepExisting\n",
+        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+        "* `param batch_priority`\n",
+        "    - The priority of the batch.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run = client.get_model_run(MODEL_RUN_ID)\n\nsend_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    None,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths or obtain task queue id through project.task_queues().\n    batch_name=\"Foundry Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Clean up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [],
+      "cell_type": "markdown"
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index c2647cd87..eb955cef6 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -1,152 +1,150 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Install required libraries"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q transformers",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Labelbox Credentials"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Select data rows in Labelbox for custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.get_dataset(DATASET_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Get a HuggingFace Model to generate custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Generate and upload custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Install required libraries"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q transformers",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Labelbox Credentials"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Select data rows in Labelbox for custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.get_dataset(DATASET_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Get a HuggingFace Model to generate custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Generate and upload custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/langchain/langchain.ipynb b/examples/integrations/langchain/langchain.ipynb
index d07681a63..c893efebc 100644
--- a/examples/integrations/langchain/langchain.ipynb
+++ b/examples/integrations/langchain/langchain.ipynb
@@ -1,178 +1,176 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LangChain -> Labelbox\n",
-                "This notebook is used to show an example workflow of getting LangChain traces into Labelbox conversation data format. Please review the [associated written guide](https://labelbox.com/guides/turn-langchain-logs-into-conversational-data-with-labelbox/) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install --upgrade --quiet  langchain langsmith langchainhub\n%pip install --upgrade --quiet  langchain-openai tiktoken pandas duckduckgo-search\n%pip install --upgrade --quiet  \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom uuid import uuid4\nimport os\nimport functools\n\n# LangSmith Imports\nfrom langsmith.client import Client\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor\nfrom langchain.agents.format_scratchpad.openai_tools import (\n    format_to_openai_tool_messages,)\nfrom langchain.agents.output_parsers.openai_tools import (\n    OpenAIToolsAgentOutputParser,)\nfrom langchain_community.tools import DuckDuckGoSearchResults\nfrom langchain_openai import ChatOpenAI\nfrom langsmith.evaluation import EvaluationResult\nfrom langsmith.schemas import Example, Run, DataType\nfrom langchain.smith import run_on_dataset\nfrom langchain.evaluation import EvaluatorType\nfrom langchain.smith import RunEvalConfig",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Setup\n",
-                "Provide a valid API key below for Labelbox, LangSmith and OpenAI in order for the notebook to work correctly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "LB_API_KEY = \"\"\nLS_API_KEY = \"\"\nOPENAI_API_KEY = \"\"\n\nunique_id = uuid4().hex[0:8]\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = f\"Tracing Walkthrough - {unique_id}\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = LS_API_KEY\n\nos.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n\nlb_client = lb.Client(LB_API_KEY)\nclient = Client()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### LangSmith Dataset Name\n",
-                "Create a sample chat data set with an example chat based run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset_name = f\"Sample Dataset - {str(uuid4())}\"\ndataset = client.create_dataset(\n    dataset_name,\n    description=\"A sample dataset in LangSmith.\",\n    data_type=DataType.chat,\n)\nclient.create_chat_example(\n    messages=[\n        {\n            \"type\": \"ai\",\n            \"data\": {\n                \"content\": \"hi how are you\"\n            }\n        },\n        {\n            \"type\": \"human\",\n            \"data\": {\n                \"content\": \"Im doing great how about you\"\n            }\n        },\n    ],\n    generations={\n        \"type\": \"ai\",\n        \"data\": {\n            \"content\": \"Im doing great\"\n        },\n    },  # Custom model output\n    dataset_id=dataset.id,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### LangSmith\n",
-                "Below is an example of running a list of raw text evaluation strings and a LangSmith example run with Chat Gpt 3.5. Please review [LangSmith Docs](https://docs.smith.langchain.com/) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tools = [\n    DuckDuckGoSearchResults(\n        name=\"duck_duck_go\"),  # General internet search using DuckDuckGo\n]\n\nllm = ChatOpenAI(\n    model=\"gpt-3.5-turbo-16k\",\n    temperature=0,\n)\n\n\n# Since chains can be stateful (e.g. they can have memory), we provide\n# a way to initialize a new chain for each row in the dataset. This is done\n# by passing in a factory function that returns a new chain for each row.\ndef create_agent(prompt, llm_with_tools):\n    runnable_agent = ({\n        \"input\":\n            lambda x: x[\"input\"],\n        \"agent_scratchpad\":\n            lambda x: format_to_openai_tool_messages(x[\"intermediate_steps\"]),\n    } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser())\n    return AgentExecutor(agent=runnable_agent,\n                         tools=tools,\n                         handle_parsing_errors=True)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "def max_pred_length(runs, examples):\n    predictions = [len(run.outputs[\"output\"]) for run in runs]\n    return EvaluationResult(key=\"max_pred_length\", score=max(predictions))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "def check_not_idk(run: Run, example: Example):\n    \"\"\"Illustration of a custom evaluator.\"\"\"\n    agent_response = run.outputs[\"output\"]\n    if \"don't know\" in agent_response or \"not sure\" in agent_response:\n        score = 0\n    else:\n        score = 1\n    # You can access the dataset labels in example.outputs[key]\n    # You can also access the model inputs in run.inputs[key]\n    return EvaluationResult(\n        key=\"not_uncertain\",\n        score=score,\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "evaluation_config = RunEvalConfig(\n    # Evaluators can either be an evaluator type (e.g., \"qa\", \"criteria\", \"embedding_distance\", etc.) or a configuration for that evaluator\n    evaluators=[\n        check_not_idk,\n        # Measures whether a QA response is \"Correct\", based on a reference answer\n        # You can also select via the raw string \"qa\"\n        EvaluatorType.QA,\n        # Measure the embedding distance between the output and the reference answer\n        # Equivalent to: EvalConfig.EmbeddingDistance(embeddings=OpenAIEmbeddings())\n        EvaluatorType.EMBEDDING_DISTANCE,\n        # Grade whether the output satisfies the stated criteria.\n        # You can select a default one such as \"helpfulness\" or provide your own.\n        RunEvalConfig.LabeledCriteria(\"helpfulness\"),\n        # The LabeledScoreString evaluator outputs a score on a scale from 1-10.\n        # You can use default criteria or write our own rubric\n        RunEvalConfig.LabeledScoreString(\n            {\n                \"accuracy\":\n                    \"\"\"\nScore 1: The answer is completely unrelated to the reference.\nScore 3: The answer has minor relevance but does not align with the reference.\nScore 5: The answer has moderate relevance but contains inaccuracies.\nScore 7: The answer aligns with the reference but has minor errors or omissions.\nScore 10: The answer is completely accurate and aligns perfectly with the reference.\"\"\"\n            },\n            normalize_by=10,\n        ),\n    ],\n    batch_evaluators=[max_pred_length],\n)\n\nllm_with_tools = llm.bind_tools(tools)\nprompt = hub.pull(\"gabe/labelboxtutorialdemo\"\n                 )  # Change prompt in LangSmith hub to reflect example run\n\nchain_results = run_on_dataset(\n    dataset_name=dataset_name,\n    llm_or_chain_factory=functools.partial(create_agent,\n                                           prompt=prompt,\n                                           llm_with_tools=llm_with_tools),\n    evaluation=evaluation_config,\n    verbose=True,\n    client=client,\n    project_name=f\"tools-agent-test-5d466cbc-{unique_id}\",\n    # Project metadata communicates the experiment parameters,\n    # Useful for reviewing the test results\n    project_metadata={\n        \"env\": \"testing-notebook\",\n        \"model\": \"gpt-3.5-turbo\",\n        \"prompt\": \"5d466cbc\",\n    },\n)\n\n# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.\n# These are logged as warnings here and captured as errors in the tracing UI.",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Labelbox\n",
-                "Below converts the results of the above LangSmith run to Labelbox conversation text. Please review [Labelbox conversation data docs](https://docs.labelbox.com/docs/llm-human-preference) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "def import_conversational(\n        chain_results: dict[str:str],\n        user_id_dict: dict[str:dict[str:str]]) -> dict[str:str]:\n    \"\"\"Converts LangSmith chain_results from model invocation to Labelbox conversation data for model response comparison. Output is based on popular model response and custom model response towards prompts.\n\n    Args:\n      chain_results(dict[str:str]): Results from LangSmith model invocation against example dataset runs.\n      user_id_dict(dict[str:dict[str:str]]): Dictionary mapping of LangSmith example run type to Labelbox chat names and alignment.\n\n    Returns:\n        dict[str:str]: Labelbox conversation text format\n    \"\"\"\n    lb_conversations = []\n    for key, conversational in chain_results[\"results\"].items():\n        lb_conversation = {\n            \"row_data\": {\n                \"type\": \"application/vnd.labelbox.conversational\",\n                \"version\": 1,\n                \"messages\": [],\n                \"modelOutputs\": [],\n            },\n            \"global_key\": key,\n            \"media_type\": \"CONVERSATIONAL\",\n        }\n        if \"input\" in conversational[\"output\"]:\n            for i, input in enumerate(conversational[\"output\"][\"input\"]):\n                lb_conversation[\"row_data\"][\"messages\"].append({\n                    \"content\": input[\"data\"][\"content\"],\n                    \"timestampUsec\": i + 1,\n                    \"user\": {\n                        \"userId\": user_id_dict[input[\"type\"]][\"id\"],\n                        \"name\": input[\"type\"],\n                    },\n                    \"canLabel\": True,\n                    \"align\": user_id_dict[input[\"type\"]][\"align\"],\n                    \"messageId\": str(uuid4()),\n                })\n\n        # Custom model output\n        if \"reference\" in conversational:\n            reference = conversational[\"reference\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Custom Model Response\",\n                \"content\": reference[\"data\"][\"content\"],\n                \"modelConfigName\": \"Custom Model - Example Config\",\n            })\n\n        # Popular model output\n        if \"output\" in conversational[\"output\"]:\n            output = conversational[\"output\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Popular LLM Response\",\n                \"content\": output,\n                \"modelConfigName\": \"GPT-3.5 - Example Config\",\n            })\n\n        lb_conversations.append(lb_conversation)\n    return lb_conversations",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Labelbox Dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = lb_client.create_dataset(name=\"demo_langchain\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Attach Conversation Text to Dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "task = dataset.create_data_rows(\n    import_conversational(\n        chain_results,\n        {\n            \"human\": {\n                \"id\": \"human\",\n                \"align\": \"right\"\n            },\n            \"ai\": {\n                \"id\": \"ai\",\n                \"align\": \"left\"\n            },\n        },\n    ))\ntask.wait_till_done()\n\nprint(task.errors)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LangChain -> Labelbox\n",
+        "This notebook is used to show an example workflow of getting LangChain traces into Labelbox conversation data format. Please review the [associated written guide](https://labelbox.com/guides/turn-langchain-logs-into-conversational-data-with-labelbox/) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install --upgrade --quiet  langchain langsmith langchainhub\n%pip install --upgrade --quiet  langchain-openai tiktoken pandas duckduckgo-search\n%pip install --upgrade --quiet  \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom uuid import uuid4\nimport os\nimport functools\n\n# LangSmith Imports\nfrom langsmith.client import Client\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor\nfrom langchain.agents.format_scratchpad.openai_tools import (\n    format_to_openai_tool_messages,)\nfrom langchain.agents.output_parsers.openai_tools import (\n    OpenAIToolsAgentOutputParser,)\nfrom langchain_community.tools import DuckDuckGoSearchResults\nfrom langchain_openai import ChatOpenAI\nfrom langsmith.evaluation import EvaluationResult\nfrom langsmith.schemas import Example, Run, DataType\nfrom langchain.smith import run_on_dataset\nfrom langchain.evaluation import EvaluatorType\nfrom langchain.smith import RunEvalConfig",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Setup\n",
+        "Provide a valid API key below for Labelbox, LangSmith and OpenAI in order for the notebook to work correctly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "LB_API_KEY = \"\"\nLS_API_KEY = \"\"\nOPENAI_API_KEY = \"\"\n\nunique_id = uuid4().hex[0:8]\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = f\"Tracing Walkthrough - {unique_id}\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = LS_API_KEY\n\nos.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n\nlb_client = lb.Client(LB_API_KEY)\nclient = Client()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### LangSmith Dataset Name\n",
+        "Create a sample chat data set with an example chat based run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset_name = f\"Sample Dataset - {str(uuid4())}\"\ndataset = client.create_dataset(\n    dataset_name,\n    description=\"A sample dataset in LangSmith.\",\n    data_type=DataType.chat,\n)\nclient.create_chat_example(\n    messages=[\n        {\n            \"type\": \"ai\",\n            \"data\": {\n                \"content\": \"hi how are you\"\n            }\n        },\n        {\n            \"type\": \"human\",\n            \"data\": {\n                \"content\": \"Im doing great how about you\"\n            }\n        },\n    ],\n    generations={\n        \"type\": \"ai\",\n        \"data\": {\n            \"content\": \"Im doing great\"\n        },\n    },  # Custom model output\n    dataset_id=dataset.id,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### LangSmith\n",
+        "Below is an example of running a list of raw text evaluation strings and a LangSmith example run with Chat Gpt 3.5. Please review [LangSmith Docs](https://docs.smith.langchain.com/) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tools = [\n    DuckDuckGoSearchResults(\n        name=\"duck_duck_go\"),  # General internet search using DuckDuckGo\n]\n\nllm = ChatOpenAI(\n    model=\"gpt-3.5-turbo-16k\",\n    temperature=0,\n)\n\n\n# Since chains can be stateful (e.g. they can have memory), we provide\n# a way to initialize a new chain for each row in the dataset. This is done\n# by passing in a factory function that returns a new chain for each row.\ndef create_agent(prompt, llm_with_tools):\n    runnable_agent = ({\n        \"input\":\n            lambda x: x[\"input\"],\n        \"agent_scratchpad\":\n            lambda x: format_to_openai_tool_messages(x[\"intermediate_steps\"]),\n    } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser())\n    return AgentExecutor(agent=runnable_agent,\n                         tools=tools,\n                         handle_parsing_errors=True)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "def max_pred_length(runs, examples):\n    predictions = [len(run.outputs[\"output\"]) for run in runs]\n    return EvaluationResult(key=\"max_pred_length\", score=max(predictions))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "def check_not_idk(run: Run, example: Example):\n    \"\"\"Illustration of a custom evaluator.\"\"\"\n    agent_response = run.outputs[\"output\"]\n    if \"don't know\" in agent_response or \"not sure\" in agent_response:\n        score = 0\n    else:\n        score = 1\n    # You can access the dataset labels in example.outputs[key]\n    # You can also access the model inputs in run.inputs[key]\n    return EvaluationResult(\n        key=\"not_uncertain\",\n        score=score,\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "evaluation_config = RunEvalConfig(\n    # Evaluators can either be an evaluator type (e.g., \"qa\", \"criteria\", \"embedding_distance\", etc.) or a configuration for that evaluator\n    evaluators=[\n        check_not_idk,\n        # Measures whether a QA response is \"Correct\", based on a reference answer\n        # You can also select via the raw string \"qa\"\n        EvaluatorType.QA,\n        # Measure the embedding distance between the output and the reference answer\n        # Equivalent to: EvalConfig.EmbeddingDistance(embeddings=OpenAIEmbeddings())\n        EvaluatorType.EMBEDDING_DISTANCE,\n        # Grade whether the output satisfies the stated criteria.\n        # You can select a default one such as \"helpfulness\" or provide your own.\n        RunEvalConfig.LabeledCriteria(\"helpfulness\"),\n        # The LabeledScoreString evaluator outputs a score on a scale from 1-10.\n        # You can use default criteria or write our own rubric\n        RunEvalConfig.LabeledScoreString(\n            {\n                \"accuracy\":\n                    \"\"\"\nScore 1: The answer is completely unrelated to the reference.\nScore 3: The answer has minor relevance but does not align with the reference.\nScore 5: The answer has moderate relevance but contains inaccuracies.\nScore 7: The answer aligns with the reference but has minor errors or omissions.\nScore 10: The answer is completely accurate and aligns perfectly with the reference.\"\"\"\n            },\n            normalize_by=10,\n        ),\n    ],\n    batch_evaluators=[max_pred_length],\n)\n\nllm_with_tools = llm.bind_tools(tools)\nprompt = hub.pull(\"gabe/labelboxtutorialdemo\"\n                 )  # Change prompt in LangSmith hub to reflect example run\n\nchain_results = run_on_dataset(\n    dataset_name=dataset_name,\n    llm_or_chain_factory=functools.partial(create_agent,\n                                           prompt=prompt,\n                                           llm_with_tools=llm_with_tools),\n    evaluation=evaluation_config,\n    verbose=True,\n    client=client,\n    project_name=f\"tools-agent-test-5d466cbc-{unique_id}\",\n    # Project metadata communicates the experiment parameters,\n    # Useful for reviewing the test results\n    project_metadata={\n        \"env\": \"testing-notebook\",\n        \"model\": \"gpt-3.5-turbo\",\n        \"prompt\": \"5d466cbc\",\n    },\n)\n\n# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.\n# These are logged as warnings here and captured as errors in the tracing UI.",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Labelbox\n",
+        "Below converts the results of the above LangSmith run to Labelbox conversation text. Please review [Labelbox conversation data docs](https://docs.labelbox.com/docs/llm-human-preference) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "def import_conversational(\n        chain_results: dict[str:str],\n        user_id_dict: dict[str:dict[str:str]]) -> dict[str:str]:\n    \"\"\"Converts LangSmith chain_results from model invocation to Labelbox conversation data for model response comparison. Output is based on popular model response and custom model response towards prompts.\n\n    Args:\n      chain_results(dict[str:str]): Results from LangSmith model invocation against example dataset runs.\n      user_id_dict(dict[str:dict[str:str]]): Dictionary mapping of LangSmith example run type to Labelbox chat names and alignment.\n\n    Returns:\n        dict[str:str]: Labelbox conversation text format\n    \"\"\"\n    lb_conversations = []\n    for key, conversational in chain_results[\"results\"].items():\n        lb_conversation = {\n            \"row_data\": {\n                \"type\": \"application/vnd.labelbox.conversational\",\n                \"version\": 1,\n                \"messages\": [],\n                \"modelOutputs\": [],\n            },\n            \"global_key\": key,\n            \"media_type\": \"CONVERSATIONAL\",\n        }\n        if \"input\" in conversational[\"output\"]:\n            for i, input in enumerate(conversational[\"output\"][\"input\"]):\n                lb_conversation[\"row_data\"][\"messages\"].append({\n                    \"content\": input[\"data\"][\"content\"],\n                    \"timestampUsec\": i + 1,\n                    \"user\": {\n                        \"userId\": user_id_dict[input[\"type\"]][\"id\"],\n                        \"name\": input[\"type\"],\n                    },\n                    \"canLabel\": True,\n                    \"align\": user_id_dict[input[\"type\"]][\"align\"],\n                    \"messageId\": str(uuid4()),\n                })\n\n        # Custom model output\n        if \"reference\" in conversational:\n            reference = conversational[\"reference\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Custom Model Response\",\n                \"content\": reference[\"data\"][\"content\"],\n                \"modelConfigName\": \"Custom Model - Example Config\",\n            })\n\n        # Popular model output\n        if \"output\" in conversational[\"output\"]:\n            output = conversational[\"output\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Popular LLM Response\",\n                \"content\": output,\n                \"modelConfigName\": \"GPT-3.5 - Example Config\",\n            })\n\n        lb_conversations.append(lb_conversation)\n    return lb_conversations",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Labelbox Dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset = lb_client.create_dataset(name=\"demo_langchain\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Attach Conversation Text to Dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "task = dataset.create_data_rows(\n    import_conversational(\n        chain_results,\n        {\n            \"human\": {\n                \"id\": \"human\",\n                \"align\": \"right\"\n            },\n            \"ai\": {\n                \"id\": \"ai\",\n                \"align\": \"left\"\n            },\n        },\n    ))\ntask.wait_till_done()\n\nprint(task.errors)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/sam/meta_sam.ipynb b/examples/integrations/sam/meta_sam.ipynb
index 1f2f9b788..0bcc92203 100644
--- a/examples/integrations/sam/meta_sam.ipynb
+++ b/examples/integrations/sam/meta_sam.ipynb
@@ -1,251 +1,249 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup\n",
-                "This notebook is used to show how to use Meta's Segment Anything model to create masks that can then be uploaded to a Labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "from IPython import display\n\ndisplay.clear_output()\n\nimport ultralytics\n\nultralytics.checks()\n\nimport cv2\nimport numpy as np\nfrom ultralytics import YOLO\nfrom IPython.display import display, Image\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import (\n    sam_model_registry,\n    SamAutomaticMaskGenerator,\n    SamPredictor,\n)\nimport os\nimport urllib.request\nimport uuid\n\nimport labelbox as lb\nimport labelbox.types as lb_types\n\nHOME = os.getcwd()\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Predicting bounding boxes around common objects using YOLOv8\n",
-                "\n",
-                "First, we start with loading the YOLOv8 model, getting a sample image, and running the model on it to generate bounding boxes around some common objects."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Utilize YOLOV8 to Create Bounding Boxes\n",
-                "\n",
-                "We use YOLOV8 in this demo to obtain bounding boxes around our images that we can later feed into SAM for our masks."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Below we run inference on a image using the YOLOv8 model."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# You can also use the Labelbox Client API to get specific images or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n\nIMAGE_PATH = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/chairs.jpeg\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "model = YOLO(f\"{HOME}/yolov8n.pt\")\nresults = model.predict(source=IMAGE_PATH, conf=0.25)\n\n# print(results[0].boxes.xyxy) # print bounding box coordinates\n\n# print(results[0].boxes.conf) # print confidence scores\n\n# for c in results[0].boxes.cls:\n# print(model.names[int(c)]) # print predicted classes",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Below we visualize the bounding boxes on the image using CV2."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "image_bgr = cv2.imread(\"./chairs.jpeg\")\n\nfor box in results[0].boxes.xyxy:\n    cv2.rectangle(\n        image_bgr,\n        (int(box[0]), int(box[1])),\n        (int(box[2]), int(box[3])),\n        (0, 255, 0),\n        2,\n    )\n\nif IN_COLAB:\n    cv2_imshow(image_bgr)\nelse:\n    cv2.imshow(\"demo\", image_bgr)\n    cv2.waitKey()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Predicting segmentation masks using Meta's Segment Anything model\n",
-                "\n",
-                "Now we load Meta's Segment Anything model and feed the bounding boxes to it, so it can generate segmentation masks within them."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Download SAM model weights\n\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nMODEL_TYPE = \"vit_h\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(\n    device=DEVICE)\nmask_predictor = SamPredictor(sam)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n    results[0].boxes.xyxy, image_bgr.shape[:2])\n\nmask_predictor.set_image(image_bgr)\n\nmasks, scores, logits = mask_predictor.predict_torch(\n    boxes=transformed_boxes,\n    multimask_output=False,\n    point_coords=None,\n    point_labels=None,\n)\nmasks = np.array(masks.cpu())\n\n# print(masks)\n# print(scores)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Here we visualize the segmentation masks drawn on the image."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n\nfinal_mask = None\nfor i in range(len(masks) - 1):\n    if final_mask is None:\n        final_mask = np.bitwise_or(masks[i][0], masks[i + 1][0])\n    else:\n        final_mask = np.bitwise_or(final_mask, masks[i + 1][0])\n\nplt.figure(figsize=(10, 10))\nplt.imshow(image_rgb)\nplt.axis(\"off\")\nplt.imshow(final_mask, cmap=\"gray\", alpha=0.7)\n\nplt.show()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Uploading predicted segmentation masks with class names to Labelbox using Python SDK"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox ObjectAnnotation of type mask for each predicted mask\n\n# Identifying what values in the numpy array correspond to the mask annotation\ncolor = (1, 1, 1)\n\nclass_names = []\nfor c in results[0].boxes.cls:\n    class_names.append(model.names[int(c)])\n\nannotations = []\nfor idx, mask in enumerate(masks):\n    mask_data = lb_types.MaskData.from_2D_arr(np.asarray(mask[0],\n                                                         dtype=\"uint8\"))\n    mask_annotation = lb_types.ObjectAnnotation(\n        name=class_names[\n            idx],  # this is the class predicted in Step 1 (object detector)\n        value=lb_types.Mask(mask=mask_data, color=color),\n    )\n    annotations.append(mask_annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new dataset\n\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = \"my_unique_global_key\"\n\ntest_img_url = {\"row_data\": IMAGE_PATH, \"global_key\": global_key}\n\ndataset = client.create_dataset(name=\"auto-mask-classification-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new ontology if you don't have one\n\n# Add all unique classes detected in Step 1\ntools = []\nfor name in set(class_names):\n    tools.append(lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=name))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"auto-mask-classification-ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"Demo Chair\").get_one()\n\n# ontology = client.get_ontology(\"clhee8kzt049v094h7stq7v25\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"auto-mask-classification-project\",\n                                media_type=lb.MediaType.Image)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new batch of data for the project you specified above\n\ndata_row_ids = client.get_data_row_ids_for_global_keys([global_key])[\"results\"]\n\nbatch = project.create_batch(\n    \"auto-mask-classification-batch\",  # each batch in a project must have a unique name\n    data_rows=data_row_ids,\n    # you can also specify global_keys instead of data_rows\n    # global_keys=[global_key],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the predictions to your specified project and data rows as pre-labels\n\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# dataset.delete()\n# project.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup\n",
+        "This notebook is used to show how to use Meta's Segment Anything model to create masks that can then be uploaded to a Labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "from IPython import display\n\ndisplay.clear_output()\n\nimport ultralytics\n\nultralytics.checks()\n\nimport cv2\nimport numpy as np\nfrom ultralytics import YOLO\nfrom IPython.display import display, Image\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import (\n    sam_model_registry,\n    SamAutomaticMaskGenerator,\n    SamPredictor,\n)\nimport os\nimport urllib.request\nimport uuid\n\nimport labelbox as lb\nimport labelbox.types as lb_types\n\nHOME = os.getcwd()\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Predicting bounding boxes around common objects using YOLOv8\n",
+        "\n",
+        "First, we start with loading the YOLOv8 model, getting a sample image, and running the model on it to generate bounding boxes around some common objects."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Utilize YOLOV8 to Create Bounding Boxes\n",
+        "\n",
+        "We use YOLOV8 in this demo to obtain bounding boxes around our images that we can later feed into SAM for our masks."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Below we run inference on a image using the YOLOv8 model."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# You can also use the Labelbox Client API to get specific images or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n\nIMAGE_PATH = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/chairs.jpeg\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "model = YOLO(f\"{HOME}/yolov8n.pt\")\nresults = model.predict(source=IMAGE_PATH, conf=0.25)\n\n# print(results[0].boxes.xyxy) # print bounding box coordinates\n\n# print(results[0].boxes.conf) # print confidence scores\n\n# for c in results[0].boxes.cls:\n# print(model.names[int(c)]) # print predicted classes",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Below we visualize the bounding boxes on the image using CV2."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "image_bgr = cv2.imread(\"./chairs.jpeg\")\n\nfor box in results[0].boxes.xyxy:\n    cv2.rectangle(\n        image_bgr,\n        (int(box[0]), int(box[1])),\n        (int(box[2]), int(box[3])),\n        (0, 255, 0),\n        2,\n    )\n\nif IN_COLAB:\n    cv2_imshow(image_bgr)\nelse:\n    cv2.imshow(\"demo\", image_bgr)\n    cv2.waitKey()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Predicting segmentation masks using Meta's Segment Anything model\n",
+        "\n",
+        "Now we load Meta's Segment Anything model and feed the bounding boxes to it, so it can generate segmentation masks within them."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Download SAM model weights\n\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nMODEL_TYPE = \"vit_h\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(\n    device=DEVICE)\nmask_predictor = SamPredictor(sam)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n    results[0].boxes.xyxy, image_bgr.shape[:2])\n\nmask_predictor.set_image(image_bgr)\n\nmasks, scores, logits = mask_predictor.predict_torch(\n    boxes=transformed_boxes,\n    multimask_output=False,\n    point_coords=None,\n    point_labels=None,\n)\nmasks = np.array(masks.cpu())\n\n# print(masks)\n# print(scores)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Here we visualize the segmentation masks drawn on the image."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n\nfinal_mask = None\nfor i in range(len(masks) - 1):\n    if final_mask is None:\n        final_mask = np.bitwise_or(masks[i][0], masks[i + 1][0])\n    else:\n        final_mask = np.bitwise_or(final_mask, masks[i + 1][0])\n\nplt.figure(figsize=(10, 10))\nplt.imshow(image_rgb)\nplt.axis(\"off\")\nplt.imshow(final_mask, cmap=\"gray\", alpha=0.7)\n\nplt.show()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Uploading predicted segmentation masks with class names to Labelbox using Python SDK"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox ObjectAnnotation of type mask for each predicted mask\n\n# Identifying what values in the numpy array correspond to the mask annotation\ncolor = (1, 1, 1)\n\nclass_names = []\nfor c in results[0].boxes.cls:\n    class_names.append(model.names[int(c)])\n\nannotations = []\nfor idx, mask in enumerate(masks):\n    mask_data = lb_types.MaskData.from_2D_arr(np.asarray(mask[0],\n                                                         dtype=\"uint8\"))\n    mask_annotation = lb_types.ObjectAnnotation(\n        name=class_names[\n            idx],  # this is the class predicted in Step 1 (object detector)\n        value=lb_types.Mask(mask=mask_data, color=color),\n    )\n    annotations.append(mask_annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new dataset\n\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = \"my_unique_global_key\"\n\ntest_img_url = {\"row_data\": IMAGE_PATH, \"global_key\": global_key}\n\ndataset = client.create_dataset(name=\"auto-mask-classification-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new ontology if you don't have one\n\n# Add all unique classes detected in Step 1\ntools = []\nfor name in set(class_names):\n    tools.append(lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=name))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"auto-mask-classification-ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"Demo Chair\").get_one()\n\n# ontology = client.get_ontology(\"clhee8kzt049v094h7stq7v25\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"auto-mask-classification-project\",\n                                media_type=lb.MediaType.Image)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new batch of data for the project you specified above\n\ndata_row_ids = client.get_data_row_ids_for_global_keys([global_key])[\"results\"]\n\nbatch = project.create_batch(\n    \"auto-mask-classification-batch\",  # each batch in a project must have a unique name\n    data_rows=data_row_ids,\n    # you can also specify global_keys instead of data_rows\n    # global_keys=[global_key],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the predictions to your specified project and data rows as pre-labels\n\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# dataset.delete()\n# project.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/sam/meta_sam_video.ipynb b/examples/integrations/sam/meta_sam_video.ipynb
index b928cbb9b..ac14a0c0d 100644
--- a/examples/integrations/sam/meta_sam_video.ipynb
+++ b/examples/integrations/sam/meta_sam_video.ipynb
@@ -1,235 +1,233 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup\n",
-                "\n",
-                "This notebook is used to show how to use Meta's Segment Anything model and YOLO to create masks for videos that can then be uploaded to a Labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### General dependencies"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import os\nimport urllib\nimport cv2\nimport PIL\nfrom PIL import Image\nimport numpy as np\nimport uuid\nimport tempfile\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow\n\nfrom IPython import display\n\ndisplay.clear_output()\nfrom IPython.display import display, Image\nfrom io import BytesIO\n\n# YOLOv8 dependencies\nimport ultralytics\n\nultralytics.checks()\nfrom ultralytics import YOLO\n\n# SAM dependencies\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Labelbox dependencies\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# You can also use the Labelbox Client API to get specific videos or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\nHOME = os.getcwd()\nVIDEO_PATH = os.path.join(HOME, \"skateboarding.mp4\")\n\nif not os.path.isfile(VIDEO_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/skateboarding.mp4\",\n        \"skateboarding.mp4\",\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### YOLOv8 setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Instantiate YOLOv8 model\nmodel = YOLO(f\"{HOME}/yolov8n.pt\")\ncolors = np.random.randint(0, 256, size=(len(model.names), 3))\n\nprint(model.names)\n\n# Specify which classes you care about. The rest of classes will be filtered out.\nchosen_class_ids = [0]  # person",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### SAM setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Download SAM model weights\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Instantiate SAM model\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nsam = sam_model_registry[\"vit_h\"](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\nmask_predictor = SamPredictor(sam)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Labelbox setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Helper functions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Cast color to ints\ndef get_color(color):\n    return (int(color[0]), int(color[1]), int(color[2]))\n\n\n# Get video dimensions\ndef get_video_dimensions(input_cap):\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    return height, width\n\n\n# Get output video writer with same dimensions and fps as input video\ndef get_output_video_writer(input_cap, output_path):\n    # Get the video's properties (width, height, FPS)\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = int(input_cap.get(cv2.CAP_PROP_FPS))\n\n    # Define the output video file\n    output_codec = cv2.VideoWriter_fourcc(*\"mp4v\")  # MP4 codec\n    output_video = cv2.VideoWriter(output_path, output_codec, fps,\n                                   (width, height))\n\n    return output_video\n\n\n# Visualize a video frame with bounding boxes, classes and confidence scores\ndef visualize_detections(frame, boxes, conf_thresholds, class_ids):\n    frame_copy = np.copy(frame)\n    for idx in range(len(boxes)):\n        class_id = int(class_ids[idx])\n        conf = float(conf_thresholds[idx])\n        x1, y1, x2, y2 = (\n            int(boxes[idx][0]),\n            int(boxes[idx][1]),\n            int(boxes[idx][2]),\n            int(boxes[idx][3]),\n        )\n        color = colors[class_id]\n        label = f\"{model.names[class_id]}: {conf:.2f}\"\n        cv2.rectangle(frame_copy, (x1, y1), (x2, y2), get_color(color), 2)\n        cv2.putText(\n            frame_copy,\n            label,\n            (x1, y1 - 10),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            0.9,\n            get_color(color),\n            2,\n        )\n    return frame_copy\n\n\ndef add_color_to_mask(mask, color):\n    next_mask = mask.astype(np.uint8)\n    next_mask = np.expand_dims(next_mask, 0).repeat(3, axis=0)\n    next_mask = np.moveaxis(next_mask, 0, -1)\n    return next_mask * color\n\n\n# Merge masks into a single, multi-colored mask\ndef merge_masks_colored(masks, class_ids):\n    filtered_class_ids = []\n    filtered_masks = []\n    for idx, cid in enumerate(class_ids):\n        if int(cid) in chosen_class_ids:\n            filtered_class_ids.append(cid)\n            filtered_masks.append(masks[idx])\n\n    merged_with_colors = add_color_to_mask(\n        filtered_masks[0][0],\n        get_color(colors[int(filtered_class_ids[0])])).astype(np.uint8)\n\n    if len(filtered_masks) == 1:\n        return merged_with_colors\n\n    for i in range(1, len(filtered_masks)):\n        curr_mask_with_colors = add_color_to_mask(\n            filtered_masks[i][0], get_color(colors[int(filtered_class_ids[i])]))\n        merged_with_colors = np.bitwise_or(merged_with_colors,\n                                           curr_mask_with_colors)\n\n    return merged_with_colors.astype(np.uint8)\n\n\ndef get_instance_uri(client: lb.Client, global_key, array):\n    \"\"\"Reads a numpy array into a temp Labelbox data row to-be-uploaded to Labelbox\n    Args:\n        client        :   Required (lb.Client) - Labelbox Client object\n        global_key    :   Required (str) - Data row global key\n        array         :   Required (np.ndarray) - NumPy ndarray representation of an image\n    Returns:\n        Temp Labelbox data row to-be-uploaded to Labelbox as row data\n    \"\"\"\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n    # Convert PIL image to PNG file bytes\n    image_as_bytes = BytesIO()\n    image_as_pil.save(image_as_bytes, format=\"PNG\")\n    image_as_bytes = image_as_bytes.getvalue()\n    # Convert PNG file bytes to a temporary Labelbox URL\n    url = client.upload_data(\n        content=image_as_bytes,\n        filename=f\"{uuid.uuid4()}{global_key}\",\n        content_type=\"image/jpeg\",\n        sign=True,\n    )\n    # Return the URL\n    return url\n\n\ndef get_local_instance_uri(array):\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n\n    with tempfile.NamedTemporaryFile(suffix=\".png\",\n                                     dir=\"/content\",\n                                     delete=False) as temp_file:\n        image_as_pil.save(temp_file)\n        file_name = temp_file.name\n\n    # Return the URL\n    return file_name\n\n\ndef create_mask_frame(frame_num, instance_uri):\n    return lb_types.MaskFrame(index=frame_num, instance_uri=instance_uri)\n\n\ndef create_mask_instances(class_ids):\n    instances = []\n    for cid in list(set(class_ids)):  # get unique class ids\n        if int(cid) in chosen_class_ids:\n            color = get_color(colors[int(cid)])\n            name = model.names[int(cid)]\n            instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n    return instances\n\n\ndef create_video_mask_annotation(frames, instance):\n    return lb_types.VideoMaskAnnotation(frames=frames, instances=[instance])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Labelbox create dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new dataset\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = os.path.basename(VIDEO_PATH)\n\nasset = {\n    \"row_data\": VIDEO_PATH,\n    \"global_key\": global_key,\n    \"media_type\": \"VIDEO\",\n}\n\ndataset = client.create_dataset(name=\"yolo-sam-video-masks-dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Run through YOLOv8 on the video once quickly to get unique class ids present\n# This will inform which classes we add to the ontology\n\ncap = cv2.VideoCapture(VIDEO_PATH)\n\nunique_class_ids = set()\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frame number\", frame_num)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 and get class ids predicted\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n    for cid in detections[0].boxes.cls:\n        unique_class_ids.add(int(cid))\n    frame_num += 1\n\ncap.release()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "unique_class_ids",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new ontology if you don't have one\n\n# Add all chosen classes into the ontology\ntools = []\nfor cls in chosen_class_ids:\n    tools.append(\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=model.names[cls]))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"yolo-sam-video-masks-ontology\",\n    ontology_builder.asdict(),\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"yolo-sam-video-masks-ontology\").get_one()\n\n# ontology = client.get_ontology(\"\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"yolo-sam-video-masks-project\",\n                                media_type=lb.MediaType.Video)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new batch of data for the project you specified above\n\n# Uncomment if you are using `data_rows` parameter below\n# data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n\nbatch = project.create_batch(\n    \"yolo-sam-video-masks-project\",  # each batch in a project must have a unique name\n    # you can also specify global_keys instead of data_rows\n    global_keys=[global_key],\n    # you can also specify data_rows instead of global_keys\n    # data_rows=data_row_ids,\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "tools = ontology.tools()\n\nfeature_schema_ids = dict()\nfor tool in tools:\n    feature_schema_ids[tool.name] = tool.feature_schema_id\n\nprint(feature_schema_ids)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Loop through each frame of video and process it\n",
-                "* Run YOLOv8 and then SAM on each frame, and write visualization videos to disk\n",
-                "* This might take a few minutes to run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "cap = cv2.VideoCapture(VIDEO_PATH)\n\noutput_video_boxes = get_output_video_writer(\n    cap, \"/content/skateboarding_boxes.mp4\")\noutput_video_masks = get_output_video_writer(\n    cap, \"/content/skateboarding_masks.mp4\")\nmask_frames = []\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frames\", frame_num, \"-\", frame_num + 29)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 to get detections\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n\n    # Write detections to output video\n    frame_with_detections = visualize_detections(\n        frame,\n        detections[0].boxes.cpu().xyxy,\n        detections[0].boxes.cpu().conf,\n        detections[0].boxes.cpu().cls,\n    )\n    output_video_boxes.write(frame_with_detections)\n\n    # Run frame and detections through SAM to get masks\n    transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n        detections[0].boxes.xyxy, list(get_video_dimensions(cap)))\n    if len(transformed_boxes) == 0:\n        print(\"No boxes found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    mask_predictor.set_image(frame)\n    masks, scores, logits = mask_predictor.predict_torch(\n        boxes=transformed_boxes,\n        multimask_output=False,\n        point_coords=None,\n        point_labels=None,\n    )\n    masks = np.array(masks.cpu())\n    if masks is None or len(masks) == 0:\n        print(\"No masks found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    merged_colored_mask = merge_masks_colored(masks, detections[0].boxes.cls)\n\n    # Write masks to output video\n    image_combined = cv2.addWeighted(frame, 0.7, merged_colored_mask, 0.7, 0)\n    output_video_masks.write(image_combined)\n\n    # Create video mask annotation for upload to Labelbox\n    instance_uri = get_instance_uri(client, global_key, merged_colored_mask)\n    mask_frame = create_mask_frame(frame_num, instance_uri)\n    mask_frames.append(mask_frame)\n    print(\"Boxes found on frame\", frame_num)\n    frame_num += 1\n\n    # For the purposes of this demo, only look at the first 80 frames\n    if frame_num > 80:\n        break\n\ncap.release()\noutput_video_boxes.release()\noutput_video_masks.release()\ncv2.destroyAllWindows()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create annotations for LB upload\nmask_instances = create_mask_instances(unique_class_ids)\nannotations = []\nfor instance in mask_instances:\n    annotations.append(create_video_mask_annotation(mask_frames, instance))\n\nlabels = []\nlabels.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Upload the predictions to your specified project and data rows as pre-labels\n",
-                "\n",
-                "Note: This may take a few minutes, depending on size of video and number of masks"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup\n",
+        "\n",
+        "This notebook is used to show how to use Meta's Segment Anything model and YOLO to create masks for videos that can then be uploaded to a Labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### General dependencies"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import os\nimport urllib\nimport cv2\nimport PIL\nfrom PIL import Image\nimport numpy as np\nimport uuid\nimport tempfile\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow\n\nfrom IPython import display\n\ndisplay.clear_output()\nfrom IPython.display import display, Image\nfrom io import BytesIO\n\n# YOLOv8 dependencies\nimport ultralytics\n\nultralytics.checks()\nfrom ultralytics import YOLO\n\n# SAM dependencies\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Labelbox dependencies\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# You can also use the Labelbox Client API to get specific videos or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\nHOME = os.getcwd()\nVIDEO_PATH = os.path.join(HOME, \"skateboarding.mp4\")\n\nif not os.path.isfile(VIDEO_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/skateboarding.mp4\",\n        \"skateboarding.mp4\",\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### YOLOv8 setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Instantiate YOLOv8 model\nmodel = YOLO(f\"{HOME}/yolov8n.pt\")\ncolors = np.random.randint(0, 256, size=(len(model.names), 3))\n\nprint(model.names)\n\n# Specify which classes you care about. The rest of classes will be filtered out.\nchosen_class_ids = [0]  # person",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### SAM setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Download SAM model weights\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Instantiate SAM model\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nsam = sam_model_registry[\"vit_h\"](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\nmask_predictor = SamPredictor(sam)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Labelbox setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Helper functions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Cast color to ints\ndef get_color(color):\n    return (int(color[0]), int(color[1]), int(color[2]))\n\n\n# Get video dimensions\ndef get_video_dimensions(input_cap):\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    return height, width\n\n\n# Get output video writer with same dimensions and fps as input video\ndef get_output_video_writer(input_cap, output_path):\n    # Get the video's properties (width, height, FPS)\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = int(input_cap.get(cv2.CAP_PROP_FPS))\n\n    # Define the output video file\n    output_codec = cv2.VideoWriter_fourcc(*\"mp4v\")  # MP4 codec\n    output_video = cv2.VideoWriter(output_path, output_codec, fps,\n                                   (width, height))\n\n    return output_video\n\n\n# Visualize a video frame with bounding boxes, classes and confidence scores\ndef visualize_detections(frame, boxes, conf_thresholds, class_ids):\n    frame_copy = np.copy(frame)\n    for idx in range(len(boxes)):\n        class_id = int(class_ids[idx])\n        conf = float(conf_thresholds[idx])\n        x1, y1, x2, y2 = (\n            int(boxes[idx][0]),\n            int(boxes[idx][1]),\n            int(boxes[idx][2]),\n            int(boxes[idx][3]),\n        )\n        color = colors[class_id]\n        label = f\"{model.names[class_id]}: {conf:.2f}\"\n        cv2.rectangle(frame_copy, (x1, y1), (x2, y2), get_color(color), 2)\n        cv2.putText(\n            frame_copy,\n            label,\n            (x1, y1 - 10),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            0.9,\n            get_color(color),\n            2,\n        )\n    return frame_copy\n\n\ndef add_color_to_mask(mask, color):\n    next_mask = mask.astype(np.uint8)\n    next_mask = np.expand_dims(next_mask, 0).repeat(3, axis=0)\n    next_mask = np.moveaxis(next_mask, 0, -1)\n    return next_mask * color\n\n\n# Merge masks into a single, multi-colored mask\ndef merge_masks_colored(masks, class_ids):\n    filtered_class_ids = []\n    filtered_masks = []\n    for idx, cid in enumerate(class_ids):\n        if int(cid) in chosen_class_ids:\n            filtered_class_ids.append(cid)\n            filtered_masks.append(masks[idx])\n\n    merged_with_colors = add_color_to_mask(\n        filtered_masks[0][0],\n        get_color(colors[int(filtered_class_ids[0])])).astype(np.uint8)\n\n    if len(filtered_masks) == 1:\n        return merged_with_colors\n\n    for i in range(1, len(filtered_masks)):\n        curr_mask_with_colors = add_color_to_mask(\n            filtered_masks[i][0], get_color(colors[int(filtered_class_ids[i])]))\n        merged_with_colors = np.bitwise_or(merged_with_colors,\n                                           curr_mask_with_colors)\n\n    return merged_with_colors.astype(np.uint8)\n\n\ndef get_instance_uri(client: lb.Client, global_key, array):\n    \"\"\"Reads a numpy array into a temp Labelbox data row to-be-uploaded to Labelbox\n    Args:\n        client        :   Required (lb.Client) - Labelbox Client object\n        global_key    :   Required (str) - Data row global key\n        array         :   Required (np.ndarray) - NumPy ndarray representation of an image\n    Returns:\n        Temp Labelbox data row to-be-uploaded to Labelbox as row data\n    \"\"\"\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n    # Convert PIL image to PNG file bytes\n    image_as_bytes = BytesIO()\n    image_as_pil.save(image_as_bytes, format=\"PNG\")\n    image_as_bytes = image_as_bytes.getvalue()\n    # Convert PNG file bytes to a temporary Labelbox URL\n    url = client.upload_data(\n        content=image_as_bytes,\n        filename=f\"{uuid.uuid4()}{global_key}\",\n        content_type=\"image/jpeg\",\n        sign=True,\n    )\n    # Return the URL\n    return url\n\n\ndef get_local_instance_uri(array):\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n\n    with tempfile.NamedTemporaryFile(suffix=\".png\",\n                                     dir=\"/content\",\n                                     delete=False) as temp_file:\n        image_as_pil.save(temp_file)\n        file_name = temp_file.name\n\n    # Return the URL\n    return file_name\n\n\ndef create_mask_frame(frame_num, instance_uri):\n    return lb_types.MaskFrame(index=frame_num, instance_uri=instance_uri)\n\n\ndef create_mask_instances(class_ids):\n    instances = []\n    for cid in list(set(class_ids)):  # get unique class ids\n        if int(cid) in chosen_class_ids:\n            color = get_color(colors[int(cid)])\n            name = model.names[int(cid)]\n            instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n    return instances\n\n\ndef create_video_mask_annotation(frames, instance):\n    return lb_types.VideoMaskAnnotation(frames=frames, instances=[instance])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Labelbox create dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new dataset\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = os.path.basename(VIDEO_PATH)\n\nasset = {\n    \"row_data\": VIDEO_PATH,\n    \"global_key\": global_key,\n    \"media_type\": \"VIDEO\",\n}\n\ndataset = client.create_dataset(name=\"yolo-sam-video-masks-dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Run through YOLOv8 on the video once quickly to get unique class ids present\n# This will inform which classes we add to the ontology\n\ncap = cv2.VideoCapture(VIDEO_PATH)\n\nunique_class_ids = set()\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frame number\", frame_num)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 and get class ids predicted\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n    for cid in detections[0].boxes.cls:\n        unique_class_ids.add(int(cid))\n    frame_num += 1\n\ncap.release()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "unique_class_ids",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new ontology if you don't have one\n\n# Add all chosen classes into the ontology\ntools = []\nfor cls in chosen_class_ids:\n    tools.append(\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=model.names[cls]))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"yolo-sam-video-masks-ontology\",\n    ontology_builder.asdict(),\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"yolo-sam-video-masks-ontology\").get_one()\n\n# ontology = client.get_ontology(\"\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"yolo-sam-video-masks-project\",\n                                media_type=lb.MediaType.Video)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new batch of data for the project you specified above\n\n# Uncomment if you are using `data_rows` parameter below\n# data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n\nbatch = project.create_batch(\n    \"yolo-sam-video-masks-project\",  # each batch in a project must have a unique name\n    # you can also specify global_keys instead of data_rows\n    global_keys=[global_key],\n    # you can also specify data_rows instead of global_keys\n    # data_rows=data_row_ids,\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "tools = ontology.tools()\n\nfeature_schema_ids = dict()\nfor tool in tools:\n    feature_schema_ids[tool.name] = tool.feature_schema_id\n\nprint(feature_schema_ids)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Loop through each frame of video and process it\n",
+        "* Run YOLOv8 and then SAM on each frame, and write visualization videos to disk\n",
+        "* This might take a few minutes to run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "cap = cv2.VideoCapture(VIDEO_PATH)\n\noutput_video_boxes = get_output_video_writer(\n    cap, \"/content/skateboarding_boxes.mp4\")\noutput_video_masks = get_output_video_writer(\n    cap, \"/content/skateboarding_masks.mp4\")\nmask_frames = []\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frames\", frame_num, \"-\", frame_num + 29)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 to get detections\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n\n    # Write detections to output video\n    frame_with_detections = visualize_detections(\n        frame,\n        detections[0].boxes.cpu().xyxy,\n        detections[0].boxes.cpu().conf,\n        detections[0].boxes.cpu().cls,\n    )\n    output_video_boxes.write(frame_with_detections)\n\n    # Run frame and detections through SAM to get masks\n    transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n        detections[0].boxes.xyxy, list(get_video_dimensions(cap)))\n    if len(transformed_boxes) == 0:\n        print(\"No boxes found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    mask_predictor.set_image(frame)\n    masks, scores, logits = mask_predictor.predict_torch(\n        boxes=transformed_boxes,\n        multimask_output=False,\n        point_coords=None,\n        point_labels=None,\n    )\n    masks = np.array(masks.cpu())\n    if masks is None or len(masks) == 0:\n        print(\"No masks found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    merged_colored_mask = merge_masks_colored(masks, detections[0].boxes.cls)\n\n    # Write masks to output video\n    image_combined = cv2.addWeighted(frame, 0.7, merged_colored_mask, 0.7, 0)\n    output_video_masks.write(image_combined)\n\n    # Create video mask annotation for upload to Labelbox\n    instance_uri = get_instance_uri(client, global_key, merged_colored_mask)\n    mask_frame = create_mask_frame(frame_num, instance_uri)\n    mask_frames.append(mask_frame)\n    print(\"Boxes found on frame\", frame_num)\n    frame_num += 1\n\n    # For the purposes of this demo, only look at the first 80 frames\n    if frame_num > 80:\n        break\n\ncap.release()\noutput_video_boxes.release()\noutput_video_masks.release()\ncv2.destroyAllWindows()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create annotations for LB upload\nmask_instances = create_mask_instances(unique_class_ids)\nannotations = []\nfor instance in mask_instances:\n    annotations.append(create_video_mask_annotation(mask_frames, instance))\n\nlabels = []\nlabels.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Upload the predictions to your specified project and data rows as pre-labels\n",
+        "\n",
+        "Note: This may take a few minutes, depending on size of video and number of masks"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/custom_metrics_basics.ipynb b/examples/model_experiments/custom_metrics_basics.ipynb
index 9b1c78f58..a7278447c 100644
--- a/examples/model_experiments/custom_metrics_basics.ipynb
+++ b/examples/model_experiments/custom_metrics_basics.ipynb
@@ -1,257 +1,255 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "----\n",
-                "\n",
-                "# Model Diagnostics - Custom Metrics Basics\n",
-                "\n",
-                "\n",
-                "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
-                "    * Iterate faster\n",
-                "    * Measure and report on model quality\n",
-                "    * Understand marginal value of additional labels and modeling efforts\n",
-                "\n",
-                "\n",
-                "* For an end-to-end demo of diagnostics using custom metrics checkout this [notebook](custom_metrics_demo.ipynb)\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Environment Setup\n",
-                "\n",
-                "Install dependencies"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Import libraries"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox.types as lb_types\nimport labelbox as lb\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Custom Metrics\n",
-                "* Users can provide metrics at the following levels of granularity:\n",
-                "    1. data rows\n",
-                "    2. features\n",
-                "    3. subclasses\n",
-                "* Additionally, metrics can be given custom names to best describe what they are measuring.\n",
-                "    \n",
-                "* Limits and Behavior:\n",
-                "    * At a data row cannot have more than 20 metrics\n",
-                "    * Metrics are upserted, so if a metric already exists, its value will be replaced\n",
-                "    * Metrics can have values in the range [0,100000]\n",
-                "* Currently `ScalarMetric`s and `ConfusionMatrixMetric`s are supported. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### ScalarMetric\n",
-                "    * A `ScalarMetric` is a metric with just a single scalar value."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "from labelbox.data.annotation_types import (\n    ScalarMetric,\n    ScalarMetricAggregation,\n    ConfusionMatrixMetric,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_row_metric = ScalarMetric(metric_name=\"iou_custom\", value=0.5)\n\nfeature_metric = ScalarMetric(metric_name=\"iou_custom\",\n                              feature_name=\"cat\",\n                              value=0.5)\n\nsubclass_metric = ScalarMetric(\n    metric_name=\"iou_custom\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=0.5,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### ConfusionMatrixMetric\n",
-                "- A `ConfusionMatrixMetric` contains 4 numbers [True postivie, False Postive, True Negative, False Negateive]\n",
-                "- Confidence is also supported a key value pairs, where the score is the key and the value is the metric value.\n",
-                "- In the user interface, these metrics are used to derive precision,recall, and f1 scores. The reason these are not directly uploaded is that the raw data allows us to do processing on the front end.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_row_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nfeature_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nsubclass_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Confidence\n",
-                "* Users can provide confidence scores along with metrics\n",
-                "* This enables them to explore their model performance without necessarily knowing the optimal thresholds for each class.\n",
-                "* Users can filter on confidence and value in the UI to perform powerful queries.\n",
-                "* The keys represent a confidence score (must be between 0 and 1) and the values represent either a scalar metric or for confusion matrix metrics [TP,FP,TN,FN]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "confusion_matrix_metric_with_confidence = ConfusionMatrixMetric(\n    metric_name=\"confusion_matrix_50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value={\n        0.1: [1, 0, 1, 0],\n        0.3: [1, 0, 1, 0],\n        0.5: [1, 0, 1, 0],\n        0.7: [1, 0, 1, 0],\n        0.9: [1, 0, 1, 0],\n    },\n)\n\nscalar_metric_with_confidence = ScalarMetric(\n    metric_name=\"iou_custom\",\n    value={\n        0.1: 0.2,\n        0.3: 0.25,\n        0.5: 0.3,\n        0.7: 0.4,\n        0.9: 0.3\n    },\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Aggregations\n",
-                "* This is an optional field on the `ScalarMetric` object (by default it uses Arithmetic Mean).\n",
-                "* Aggregations occur in two cases:\n",
-                "    1. When a user provides a feature or subclass level metric, Labelbox automatically aggregates all metrics with the same parent to create a value for that parent.\n",
-                "        * E.g. A user provides cat and dog iou. The data row level metric for iou is the average of both of those.\n",
-                "        * The exception to this is when the data row level iou is explicitly set, then the aggregation will not take effect (on a per data row basis). \n",
-                "    2. When users create slices or want aggregate statistics on their models, the selected aggregation is applied."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "\"\"\"\nIf the following metrics are uploaded then\nin the web app, users will see:\ntrue positives dog = 4\ntrue positives cat = 3\ntrue positives = 7\n\"\"\"\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"cat\",\n    value=3,\n    aggregation=ScalarMetricAggregation.SUM,\n)\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"dog\",\n    value=4,\n    aggregation=ScalarMetricAggregation.SUM,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Built-in Metrics:\n",
-                "* The SDK Provides a set of default metrics that make metrics easy to use.\n",
-                "1. `confusion_matrix_metric()`\n",
-                "    * Computes a single confusion matrix metric for all the predictions and labels provided. \n",
-                "2. `miou_metric()`\n",
-                "    * Computes a single iou score for all predictions and labels provided    \n",
-                "3. `feature_confusion_matrix_metric()`\n",
-                "    * Computes the iou score for each of the classes found in the predictions and labels\n",
-                "4. `feature_miou_metric()`\n",
-                "     * Computes a confusion matrix metric for each of the classes found in the predictions and labels\n",
-                "------\n",
-                "* Note that all of these functions expect the prediction and ground truth annotations to correspond to the same data row. These functions should be called for each data row that you need metrics for."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "from labelbox.data.metrics import (\n    feature_miou_metric,\n    miou_metric,\n    confusion_matrix_metric,\n    feature_confusion_matrix_metric,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "predictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=8, y=8)),\n    )\n]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(feature_miou_metric(ground_truths, predictions))\nprint(miou_metric(ground_truths, predictions))\nprint(confusion_matrix_metric(ground_truths, predictions))\nprint(feature_confusion_matrix_metric(ground_truths, predictions))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Adjust iou for iou calcuations.\n# Set it higher than 0.64 and we get a false postive and a false negative for the other ground truth object.\nprint(feature_confusion_matrix_metric(ground_truths, predictions, iou=0.9))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# subclasses are included by default\npredictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"tall\")),\n            )\n        ],\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"short\")),\n            )\n        ],\n    )\n]\nconf_matrix_metrics = feature_confusion_matrix_metric(ground_truths,\n                                                      predictions)\niou_metrics = feature_confusion_matrix_metric(ground_truths,\n                                              predictions,\n                                              include_subclasses=False)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Subclasses:\", conf_matrix_metrics[0].value)\nprint(\"Excluding Subclasses:\", iou_metrics[0].value)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Uploading Custom Metrics\n",
-                "* Custom metrics are uploaded the same way as any MEA upload. NDJson must be created. Fortunately this is made easy with converter functions.\n",
-                "* First construct a metric annotation in one of two ways:\n",
-                "    1. Manually\n",
-                "    2. Using one of the provided functions `feature_miou_metric`, `miou_metric`, `confusion_matrix_metric`, `feature_confusion_matrix_metric`.\n",
-                "* Then add the metric annotation to a label ( This step associates the metrics with a data row)\n",
-                "* Convert to ndjson and upload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Continuing with the last example:\nglobal_key = \"<global_key>\"\nmetrics = [*conf_matrix_metrics, *iou_metrics]\nlabels = [\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=metrics)\n]\n# We can upload these metric with other annotations\n# model_run.add_predictions(f'diagnostics-import-{uuid.uuid4()}', labels)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "----\n",
+        "\n",
+        "# Model Diagnostics - Custom Metrics Basics\n",
+        "\n",
+        "\n",
+        "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
+        "    * Iterate faster\n",
+        "    * Measure and report on model quality\n",
+        "    * Understand marginal value of additional labels and modeling efforts\n",
+        "\n",
+        "\n",
+        "* For an end-to-end demo of diagnostics using custom metrics checkout this [notebook](custom_metrics_demo.ipynb)\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Environment Setup\n",
+        "\n",
+        "Install dependencies"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Import libraries"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox.types as lb_types\nimport labelbox as lb\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Custom Metrics\n",
+        "* Users can provide metrics at the following levels of granularity:\n",
+        "    1. data rows\n",
+        "    2. features\n",
+        "    3. subclasses\n",
+        "* Additionally, metrics can be given custom names to best describe what they are measuring.\n",
+        "    \n",
+        "* Limits and Behavior:\n",
+        "    * At a data row cannot have more than 20 metrics\n",
+        "    * Metrics are upserted, so if a metric already exists, its value will be replaced\n",
+        "    * Metrics can have values in the range [0,100000]\n",
+        "* Currently `ScalarMetric`s and `ConfusionMatrixMetric`s are supported. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### ScalarMetric\n",
+        "    * A `ScalarMetric` is a metric with just a single scalar value."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "from labelbox.data.annotation_types import (\n    ScalarMetric,\n    ScalarMetricAggregation,\n    ConfusionMatrixMetric,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_row_metric = ScalarMetric(metric_name=\"iou_custom\", value=0.5)\n\nfeature_metric = ScalarMetric(metric_name=\"iou_custom\",\n                              feature_name=\"cat\",\n                              value=0.5)\n\nsubclass_metric = ScalarMetric(\n    metric_name=\"iou_custom\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=0.5,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### ConfusionMatrixMetric\n",
+        "- A `ConfusionMatrixMetric` contains 4 numbers [True postivie, False Postive, True Negative, False Negateive]\n",
+        "- Confidence is also supported a key value pairs, where the score is the key and the value is the metric value.\n",
+        "- In the user interface, these metrics are used to derive precision,recall, and f1 scores. The reason these are not directly uploaded is that the raw data allows us to do processing on the front end.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_row_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nfeature_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nsubclass_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Confidence\n",
+        "* Users can provide confidence scores along with metrics\n",
+        "* This enables them to explore their model performance without necessarily knowing the optimal thresholds for each class.\n",
+        "* Users can filter on confidence and value in the UI to perform powerful queries.\n",
+        "* The keys represent a confidence score (must be between 0 and 1) and the values represent either a scalar metric or for confusion matrix metrics [TP,FP,TN,FN]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "confusion_matrix_metric_with_confidence = ConfusionMatrixMetric(\n    metric_name=\"confusion_matrix_50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value={\n        0.1: [1, 0, 1, 0],\n        0.3: [1, 0, 1, 0],\n        0.5: [1, 0, 1, 0],\n        0.7: [1, 0, 1, 0],\n        0.9: [1, 0, 1, 0],\n    },\n)\n\nscalar_metric_with_confidence = ScalarMetric(\n    metric_name=\"iou_custom\",\n    value={\n        0.1: 0.2,\n        0.3: 0.25,\n        0.5: 0.3,\n        0.7: 0.4,\n        0.9: 0.3\n    },\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Aggregations\n",
+        "* This is an optional field on the `ScalarMetric` object (by default it uses Arithmetic Mean).\n",
+        "* Aggregations occur in two cases:\n",
+        "    1. When a user provides a feature or subclass level metric, Labelbox automatically aggregates all metrics with the same parent to create a value for that parent.\n",
+        "        * E.g. A user provides cat and dog iou. The data row level metric for iou is the average of both of those.\n",
+        "        * The exception to this is when the data row level iou is explicitly set, then the aggregation will not take effect (on a per data row basis). \n",
+        "    2. When users create slices or want aggregate statistics on their models, the selected aggregation is applied."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "\"\"\"\nIf the following metrics are uploaded then\nin the web app, users will see:\ntrue positives dog = 4\ntrue positives cat = 3\ntrue positives = 7\n\"\"\"\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"cat\",\n    value=3,\n    aggregation=ScalarMetricAggregation.SUM,\n)\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"dog\",\n    value=4,\n    aggregation=ScalarMetricAggregation.SUM,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Built-in Metrics:\n",
+        "* The SDK Provides a set of default metrics that make metrics easy to use.\n",
+        "1. `confusion_matrix_metric()`\n",
+        "    * Computes a single confusion matrix metric for all the predictions and labels provided. \n",
+        "2. `miou_metric()`\n",
+        "    * Computes a single iou score for all predictions and labels provided    \n",
+        "3. `feature_confusion_matrix_metric()`\n",
+        "    * Computes the iou score for each of the classes found in the predictions and labels\n",
+        "4. `feature_miou_metric()`\n",
+        "     * Computes a confusion matrix metric for each of the classes found in the predictions and labels\n",
+        "------\n",
+        "* Note that all of these functions expect the prediction and ground truth annotations to correspond to the same data row. These functions should be called for each data row that you need metrics for."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "from labelbox.data.metrics import (\n    feature_miou_metric,\n    miou_metric,\n    confusion_matrix_metric,\n    feature_confusion_matrix_metric,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "predictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=8, y=8)),\n    )\n]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(feature_miou_metric(ground_truths, predictions))\nprint(miou_metric(ground_truths, predictions))\nprint(confusion_matrix_metric(ground_truths, predictions))\nprint(feature_confusion_matrix_metric(ground_truths, predictions))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Adjust iou for iou calcuations.\n# Set it higher than 0.64 and we get a false postive and a false negative for the other ground truth object.\nprint(feature_confusion_matrix_metric(ground_truths, predictions, iou=0.9))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# subclasses are included by default\npredictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"tall\")),\n            )\n        ],\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"short\")),\n            )\n        ],\n    )\n]\nconf_matrix_metrics = feature_confusion_matrix_metric(ground_truths,\n                                                      predictions)\niou_metrics = feature_confusion_matrix_metric(ground_truths,\n                                              predictions,\n                                              include_subclasses=False)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Subclasses:\", conf_matrix_metrics[0].value)\nprint(\"Excluding Subclasses:\", iou_metrics[0].value)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Uploading Custom Metrics\n",
+        "* Custom metrics are uploaded the same way as any MEA upload. NDJson must be created. Fortunately this is made easy with converter functions.\n",
+        "* First construct a metric annotation in one of two ways:\n",
+        "    1. Manually\n",
+        "    2. Using one of the provided functions `feature_miou_metric`, `miou_metric`, `confusion_matrix_metric`, `feature_confusion_matrix_metric`.\n",
+        "* Then add the metric annotation to a label ( This step associates the metrics with a data row)\n",
+        "* Convert to ndjson and upload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Continuing with the last example:\nglobal_key = \"<global_key>\"\nmetrics = [*conf_matrix_metrics, *iou_metrics]\nlabels = [\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=metrics)\n]\n# We can upload these metric with other annotations\n# model_run.add_predictions(f'diagnostics-import-{uuid.uuid4()}', labels)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/custom_metrics_demo.ipynb b/examples/model_experiments/custom_metrics_demo.ipynb
index 54be24129..311e1bb56 100644
--- a/examples/model_experiments/custom_metrics_demo.ipynb
+++ b/examples/model_experiments/custom_metrics_demo.ipynb
@@ -1,426 +1,424 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "----\n",
-                "\n",
-                "# Model Diagnostics - Custom Metrics Demo\n",
-                "\n",
-                "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
-                "    * Iterate faster\n",
-                "    * Measure and report on model quality\n",
-                "    * Understand marginal value of additional labels and modeling efforts\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Environment setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    )),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n        lb_types.ClassificationAnswer(\n            name=\"second_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n    ]),\n)\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\":\n                \"first_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n        {\n            \"name\":\n                \"second_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=0.5,\n                    custom_metrics=[\n                        {\n                            \"name\": \"iou\",\n                            \"value\": 0.5\n                        },\n                        {\n                            \"name\": \"f1\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"precision\",\n                            \"value\": 0.55\n                        },\n                        {\n                            \"name\": \"recall\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"tagsCount\",\n                            \"value\": 43\n                        },\n                        {\n                            \"name\": \"metric_with_a_very_long_name\",\n                            \"value\": 0.334332,\n                        },\n                    ],\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\":\n        \"nested_radio_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                            custom_metrics=[\n                                {\n                                    \"name\": \"iou\",\n                                    \"value\": 0.5\n                                },\n                                {\n                                    \"name\": \"f1\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"precision\",\n                                    \"value\": 0.55\n                                },\n                                {\n                                    \"name\": \"recall\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"tagsCount\",\n                                    \"value\": 43\n                                },\n                                {\n                                    \"name\": \"metric_with_a_very_long_name\",\n                                    \"value\": 0.334332,\n                                },\n                            ],\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\":\n                    \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,\n                \"customMetrics\": [\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.55\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 43\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding Box"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "bbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with nested classification "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                custom_metrics=[\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.2\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.1\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.3\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 23\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            )),\n        )\n    ],\n)\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.2\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.1\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.3\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 23\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polygon"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",\n    value=lb_types.Text(\n        answer=\"sample text\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    ),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "polyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\ntest_img_urls = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"Custom metrics demo\",\n                                iam_integration=None)\ntask = dataset.create_data_rows([test_img_urls])\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(\n    name=\"model_with_aggregated_custom_metrics\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the prediction payload using the snippets of code in ***Supported Predictions*** section.\n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to a model run\n",
-                "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations.\n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"image_prediction_many_kinds\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "----\n",
+        "\n",
+        "# Model Diagnostics - Custom Metrics Demo\n",
+        "\n",
+        "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
+        "    * Iterate faster\n",
+        "    * Measure and report on model quality\n",
+        "    * Understand marginal value of additional labels and modeling efforts\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Environment setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    )),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n        lb_types.ClassificationAnswer(\n            name=\"second_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n    ]),\n)\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\":\n                \"first_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n        {\n            \"name\":\n                \"second_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=0.5,\n                    custom_metrics=[\n                        {\n                            \"name\": \"iou\",\n                            \"value\": 0.5\n                        },\n                        {\n                            \"name\": \"f1\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"precision\",\n                            \"value\": 0.55\n                        },\n                        {\n                            \"name\": \"recall\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"tagsCount\",\n                            \"value\": 43\n                        },\n                        {\n                            \"name\": \"metric_with_a_very_long_name\",\n                            \"value\": 0.334332,\n                        },\n                    ],\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\":\n        \"nested_radio_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                            custom_metrics=[\n                                {\n                                    \"name\": \"iou\",\n                                    \"value\": 0.5\n                                },\n                                {\n                                    \"name\": \"f1\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"precision\",\n                                    \"value\": 0.55\n                                },\n                                {\n                                    \"name\": \"recall\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"tagsCount\",\n                                    \"value\": 43\n                                },\n                                {\n                                    \"name\": \"metric_with_a_very_long_name\",\n                                    \"value\": 0.334332,\n                                },\n                            ],\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\":\n                    \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,\n                \"customMetrics\": [\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.55\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 43\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding Box"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "bbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with nested classification "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                custom_metrics=[\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.2\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.1\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.3\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 23\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            )),\n        )\n    ],\n)\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.2\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.1\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.3\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 23\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polygon"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",\n    value=lb_types.Text(\n        answer=\"sample text\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    ),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "polyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\ntest_img_urls = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"Custom metrics demo\",\n                                iam_integration=None)\ntask = dataset.create_data_rows([test_img_urls])\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(\n    name=\"model_with_aggregated_custom_metrics\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the prediction payload using the snippets of code in ***Supported Predictions*** section.\n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to a model run\n",
+        "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations.\n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"image_prediction_many_kinds\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/model_predictions_to_project.ipynb b/examples/model_experiments/model_predictions_to_project.ipynb
index 542bdef53..a9476c791 100644
--- a/examples/model_experiments/model_predictions_to_project.ipynb
+++ b/examples/model_experiments/model_predictions_to_project.ipynb
@@ -1,272 +1,270 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Import Model Run Predictions to a Project\n",
-                "Throughout the process of training your machine learning (ML) model, you may want to export your model-run predictions and import them to your new project. In this notebook, we will demonstrate the process on how to get those predictions moved over."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating Model Experiment\n",
-                "\n",
-                "In order to interact with Model Run predictions, you must create a Model Experiment with a Model Run and then add predictions. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information.\n",
-                "\n",
-                "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "In this example we are making a simple ontology with a classification feature. The classification feature has two options: option 1 and option 2."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Demo Feature\",\n        options=[lb.Option(value=\"option 1\"),\n                 lb.Option(value=\"option 2\")],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\"Demo Ontology\",\n                                  ontology_builder.asdict(),\n                                  media_type=lb.MediaType.Image)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Experiment"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model = client.create_model(name=f\"Model Experiment Demo {str(uuid.uuid4())}\",\n                            ontology_id=ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating a Model Run from Model Experiment\n",
-                "\n",
-                "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Dataset and Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as data row for a dataset\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create Model Run and Attach Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run_name = f\"Model Run Demo {str(uuid.uuid4())}\"\n\nmodel_run = model.create_model_run(name=model_run_name)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Add Predictions\n",
-                "In the below code snippet we are adding a sample predictions and attaching them to our data row inside our model run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"Demo Feature\",\n    value=lb_types.Checklist(\n        answer=[lb_types.ClassificationAnswer(name=\"option 1\", confidence=0.5)\n               ]),\n)\n\n# Create prediction label\nlabel_prediction = [\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[checklist_prediction],\n    )\n]\n\n# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup Project and Add Predictions\n",
-                "In the steps below we will be creating our target project and setting up the project with the ontology we used with our model run. See [Project](https://docs.labelbox.com/reference/dataset) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new project\nproject = client.create_project(name=\"Model Run Import Demo Project\",\n                                media_type=lb.MediaType.Image)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Ontology\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Ontology Mapping\n",
-                "To send prediction to your annotate project you will need to provide a ontology mapping python dictionary item. This matches ontology feature id to another. You would use this if your ontology was different from your model run to your project. In our case, since we are using the same ontology, you would just need to map the same feature id to each other."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ontology dictionary to obtain featureSchemaIds\nontology_normalized = ontology.normalized\n\nPREDICTIONS_ONTOLOGY_MAPPING = {\n    ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Send Model Predictions from Model Run to Annotate\n",
-                "To send our predictions to our project we will be using the `send_to_annotate_from_model` method from our project. See [Foundry apps](https://docs.labelbox.com/reference/foundry-2#send-foundry-generated-annotations-from-catalog-to-annotate) for more information.\n",
-                "##### Parameters\n",
-                "\n",
-                "When you send predicted data rows to annotate from a model run, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
-                "\n",
-                "* `predictions_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Import Predictions as pre-labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "send_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Send the predictions as pre-labels\nqueue_id = [\n    queue.uid\n    for queue in project.task_queues()\n    if queue.queue_type == \"INITIAL_LABELING_QUEUE\"\n][0]\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths .\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Import Model Run Predictions to a Project\n",
+        "Throughout the process of training your machine learning (ML) model, you may want to export your model-run predictions and import them to your new project. In this notebook, we will demonstrate the process on how to get those predictions moved over."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating Model Experiment\n",
+        "\n",
+        "In order to interact with Model Run predictions, you must create a Model Experiment with a Model Run and then add predictions. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information.\n",
+        "\n",
+        "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "In this example we are making a simple ontology with a classification feature. The classification feature has two options: option 1 and option 2."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Demo Feature\",\n        options=[lb.Option(value=\"option 1\"),\n                 lb.Option(value=\"option 2\")],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\"Demo Ontology\",\n                                  ontology_builder.asdict(),\n                                  media_type=lb.MediaType.Image)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Experiment"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model = client.create_model(name=f\"Model Experiment Demo {str(uuid.uuid4())}\",\n                            ontology_id=ontology.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating a Model Run from Model Experiment\n",
+        "\n",
+        "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Dataset and Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as data row for a dataset\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create Model Run and Attach Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run_name = f\"Model Run Demo {str(uuid.uuid4())}\"\n\nmodel_run = model.create_model_run(name=model_run_name)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Add Predictions\n",
+        "In the below code snippet we are adding a sample predictions and attaching them to our data row inside our model run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"Demo Feature\",\n    value=lb_types.Checklist(\n        answer=[lb_types.ClassificationAnswer(name=\"option 1\", confidence=0.5)\n               ]),\n)\n\n# Create prediction label\nlabel_prediction = [\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[checklist_prediction],\n    )\n]\n\n# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup Project and Add Predictions\n",
+        "In the steps below we will be creating our target project and setting up the project with the ontology we used with our model run. See [Project](https://docs.labelbox.com/reference/dataset) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new project\nproject = client.create_project(name=\"Model Run Import Demo Project\",\n                                media_type=lb.MediaType.Image)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Ontology\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Ontology Mapping\n",
+        "To send prediction to your annotate project you will need to provide a ontology mapping python dictionary item. This matches ontology feature id to another. You would use this if your ontology was different from your model run to your project. In our case, since we are using the same ontology, you would just need to map the same feature id to each other."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get ontology dictionary to obtain featureSchemaIds\nontology_normalized = ontology.normalized\n\nPREDICTIONS_ONTOLOGY_MAPPING = {\n    ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Send Model Predictions from Model Run to Annotate\n",
+        "To send our predictions to our project we will be using the `send_to_annotate_from_model` method from our project. See [Foundry apps](https://docs.labelbox.com/reference/foundry-2#send-foundry-generated-annotations-from-catalog-to-annotate) for more information.\n",
+        "##### Parameters\n",
+        "\n",
+        "When you send predicted data rows to annotate from a model run, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
+        "\n",
+        "* `predictions_ontology_mapping`\n",
+        "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
+        "* `exclude_data_rows_in_project`\n",
+        "    - Excludes data rows that are already in the project. \n",
+        "* `override_existing_annotations_rule` \n",
+        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
+        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+        "        * Options include:\n",
+        "            * ConflictResolutionStrategy.KeepExisting\n",
+        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+        "* `param batch_priority`\n",
+        "    - The priority of the batch.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Import Predictions as pre-labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "send_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Send the predictions as pre-labels\nqueue_id = [\n    queue.uid\n    for queue in project.task_queues()\n    if queue.queue_type == \"INITIAL_LABELING_QUEUE\"\n][0]\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths .\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/model_slices.ipynb b/examples/model_experiments/model_slices.ipynb
index b4e96c4b9..3da98ef68 100644
--- a/examples/model_experiments/model_slices.ipynb
+++ b/examples/model_experiments/model_slices.ipynb
@@ -1,247 +1,245 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Model Slices\n",
-                "\n",
-                "Model Slice functions similarly to Catalog Slice, with both essentially being saved searches. However, there are key differences in their functionalities. While Catalog Slice searches within a specific data catalog, Model Slice extends its data row search across a model run in a model. You can construct a Model Slice by using one or more filters to curate a collection of data rows. Often users will combine filters to surface high-impact data and then save the results as a Model Slice.\n",
-                "\n",
-                "This notebook is used to go over some common Labelbox SDK methods to interact with Model Slices created through the Labelbox platform.\n",
-                "\n",
-                "See [Slices](https://docs.labelbox.com/docs/slices-1) for more information on modifying Model Slices."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install labelbox",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create Model Slice\n",
-                "\n",
-                "In order to interact with model slices, you must create a Model Experiment with a Model Run and then create a Model Slice through the platform. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating Model Experiment\n",
-                "\n",
-                "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Experiment"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model = client.create_model(name=\"Model Slice Demo\", ontology_id=ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating a Model Run from Model Experiment\n",
-                "\n",
-                "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Dataset and Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Run and Attach Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run_name = \"Model Slice Demo\"\nexample_config = {\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n}\nmodel_run = model.create_model_run(name=model_run_name, config=example_config)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Make Model Slice Inside UI\n",
-                "\n",
-                "Creating a Model Slice is currently not supported through the SDK, however, to showcase how to interact with Model Slice, we are going to generate a Model Slice through the UI.\n",
-                "\n",
-                "#### Workflow\n",
-                "\n",
-                "1. Navigate to ***Model*** section of the Labelbox Platform, select the ***Experiment*** type, and select the Model Experiment that was created.\n",
-                "2. You must have a filter created in order to save a slice. For this example, click ***Search your data*** dropdown and then ***Data row***.\n",
-                "3. Change ***is one of*** dropdown to ***is not one of*** then type \"test\" into the ***Search for an id*** search box.\n",
-                "4. Hit ***Enter*** and select ***Save slice***.\n",
-                "5. Give the slice a name and select ***Save***.\n",
-                "6. Above the ***Search your data*** dropdown you will see your slice's name. Select that dropdown and click ***Copy slice ID***.\n",
-                "7. Paste the ***Slice ID*** below."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "SLICE_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get Model Slice"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_slice = client.get_model_slice(SLICE_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Obtain Data Row IDs from Model Slice"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_row_ids = model_slice.get_data_row_ids(model_run.uid)\n\nfor data_row_id in data_row_ids:\n    print(data_row_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Obtain Data Row Identifiers Objects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = model_slice.get_data_row_identifiers(model_run.uid)\n\nfor data_row in data_rows:\n    print(data_row)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Model Slice Attributes"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# name (str)\nmodel_slice.name\n\n# description (str)\nmodel_slice.description\n\n# updated at (datetime)\nmodel_slice.updated_at\n\n# created at (datetime)\nmodel_slice.created_at\n\n# filter (list[dict])\nmodel_slice.filter",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Model Slices\n",
+        "\n",
+        "Model Slice functions similarly to Catalog Slice, with both essentially being saved searches. However, there are key differences in their functionalities. While Catalog Slice searches within a specific data catalog, Model Slice extends its data row search across a model run in a model. You can construct a Model Slice by using one or more filters to curate a collection of data rows. Often users will combine filters to surface high-impact data and then save the results as a Model Slice.\n",
+        "\n",
+        "This notebook is used to go over some common Labelbox SDK methods to interact with Model Slices created through the Labelbox platform.\n",
+        "\n",
+        "See [Slices](https://docs.labelbox.com/docs/slices-1) for more information on modifying Model Slices."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install labelbox",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create Model Slice\n",
+        "\n",
+        "In order to interact with model slices, you must create a Model Experiment with a Model Run and then create a Model Slice through the platform. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating Model Experiment\n",
+        "\n",
+        "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Experiment"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model = client.create_model(name=\"Model Slice Demo\", ontology_id=ontology.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating a Model Run from Model Experiment\n",
+        "\n",
+        "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Dataset and Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Run and Attach Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run_name = \"Model Slice Demo\"\nexample_config = {\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n}\nmodel_run = model.create_model_run(name=model_run_name, config=example_config)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Make Model Slice Inside UI\n",
+        "\n",
+        "Creating a Model Slice is currently not supported through the SDK, however, to showcase how to interact with Model Slice, we are going to generate a Model Slice through the UI.\n",
+        "\n",
+        "#### Workflow\n",
+        "\n",
+        "1. Navigate to ***Model*** section of the Labelbox Platform, select the ***Experiment*** type, and select the Model Experiment that was created.\n",
+        "2. You must have a filter created in order to save a slice. For this example, click ***Search your data*** dropdown and then ***Data row***.\n",
+        "3. Change ***is one of*** dropdown to ***is not one of*** then type \"test\" into the ***Search for an id*** search box.\n",
+        "4. Hit ***Enter*** and select ***Save slice***.\n",
+        "5. Give the slice a name and select ***Save***.\n",
+        "6. Above the ***Search your data*** dropdown you will see your slice's name. Select that dropdown and click ***Copy slice ID***.\n",
+        "7. Paste the ***Slice ID*** below."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "SLICE_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get Model Slice"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_slice = client.get_model_slice(SLICE_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Obtain Data Row IDs from Model Slice"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_row_ids = model_slice.get_data_row_ids(model_run.uid)\n\nfor data_row_id in data_row_ids:\n    print(data_row_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Obtain Data Row Identifiers Objects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = model_slice.get_data_row_identifiers(model_run.uid)\n\nfor data_row in data_rows:\n    print(data_row)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Model Slice Attributes"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# name (str)\nmodel_slice.name\n\n# description (str)\nmodel_slice.description\n\n# updated at (datetime)\nmodel_slice.updated_at\n\n# created at (datetime)\nmodel_slice.created_at\n\n# filter (list[dict])\nmodel_slice.filter",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index 006035826..69e73fc89 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -1,388 +1,386 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LLM pairwise comparison with Conversational text using Model\n",
-                "\n",
-                "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Set up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API Key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Entity"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
-                "\n",
-                "```\n",
-                "\"modelOutputs\" : [\n",
-                "  {\n",
-                "      \"title\": \"Name of the response option\",\n",
-                "      \"content\": \"Content of the response\",\n",
-                "      \"modelConfigName\": \"Name of model configuration\"\n",
-                "  }\n",
-                "]\n",
-                "```\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Example of row_data with model outputs"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Step 5: Create the predictions payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Setup the payload with the annotations that were created in Step 1."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.1 Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.2 Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.4 Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.5 Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LLM pairwise comparison with Conversational text using Model\n",
+        "\n",
+        "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Set up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API Key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Supported annotations for conversational text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Entity"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
+        "\n",
+        "```\n",
+        "\"modelOutputs\" : [\n",
+        "  {\n",
+        "      \"title\": \"Name of the response option\",\n",
+        "      \"content\": \"Content of the response\",\n",
+        "      \"modelConfigName\": \"Name of model configuration\"\n",
+        "  }\n",
+        "]\n",
+        "```\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Example of row_data with model outputs"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Step 5: Create the predictions payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Setup the payload with the annotations that were created in Step 1."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.1 Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.2 Create a batch to send to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.4 Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.5 Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Option deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_predictions.ipynb b/examples/prediction_upload/conversational_predictions.ipynb
index 3f82a49a9..97c65659c 100644
--- a/examples/prediction_upload/conversational_predictions.ipynb
+++ b/examples/prediction_upload/conversational_predictions.ipynb
@@ -1,359 +1,357 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Conversational Text Prediction Import\n",
-                "* This notebook will provide examples of each supported prediction type for conversational text assets, and also cover MAL and Label Import methods:\n",
-                "\n",
-                "Suported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "* NER\n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Bouding box \n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask \n",
-                "* Relationships\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification ###########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# message based classifications\nner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n# Confidence scores are not supported for text predictions\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions are right here\",\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"2\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n    message_id=\"0\",\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n\n# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\")],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Mode and Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(\n    name=\"Conversational_model_run_\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the predictions payload\n",
-                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting payload should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Python annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "if using NDJSON : "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7 : Send annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.1 Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(\n    name=\"Conversational Text Prediction Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.2 Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.4 Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.5 Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.6 Send the annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Conversational Text Prediction Import\n",
+        "* This notebook will provide examples of each supported prediction type for conversational text assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Suported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "* NER\n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Bouding box \n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask \n",
+        "* Relationships\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification ###########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# message based classifications\nner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n# Confidence scores are not supported for text predictions\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions are right here\",\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"2\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n    message_id=\"0\",\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n\n# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\")],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Mode and Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(\n    name=\"Conversational_model_run_\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the predictions payload\n",
+        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting payload should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Python annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "if using NDJSON : "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7 : Send annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.1 Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(\n    name=\"Conversational Text Prediction Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.2 Create a batch to send to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.4 Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.5 Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.6 Send the annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Option deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/geospatial_predictions.ipynb b/examples/prediction_upload/geospatial_predictions.ipynb
index 5cc985ee4..df131c510 100644
--- a/examples/prediction_upload/geospatial_predictions.ipynb
+++ b/examples/prediction_upload/geospatial_predictions.ipynb
@@ -1,381 +1,379 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Geospatial Prediction Import \n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for tiled imagery assets.\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n",
-                "**Supported annotations that can be uploaded through the SDK**\n",
-                "- Bounding box\n",
-                "- Point\n",
-                "- Polygons \n",
-                "- Polyline\n",
-                "- Free form text classifications\n",
-                "- Classification - radio\n",
-                "- Classification - checklist\n",
-                "\n",
-                "**NOT** supported:\n",
-                "- Segmentation masks\n",
-                "\n",
-                "\n",
-                "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import os\n\nimport uuid\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions\n",
-                "- Each cell shows the python annotation and the NDJson annotation for each annotation type."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Point #######\n\n# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    confidence=0.4,\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point_geo\",\n    \"confidence\": 0.4,\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    confidence=0.5,\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"confidence\": 0.5,\n    \"line\": line_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_prediction_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"confidence\": 0.5,\n    \"polygon\": polygon_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"confidence\": 0.5,\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"third_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.2)),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.2,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.3\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"geospatial_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=23,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_prediction_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\",\n                                                   confidence=0.5)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            bbox_prediction,\n            radio_prediction,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_prediction,\n            polygon_prediction_two,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# If using NDJSON"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor prediction in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        polygon_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    prediction.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(prediction)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(name=\"prediction_upload_job\" +\n                                                  str(uuid.uuid4()),\n                                                  predictions=labels)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"geospatial_prediction_demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_geospatial_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key],  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n####### Polyline #######\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data=lb_types.TiledImageData(\n            global_key=global_key,\n            tile_layer=tile_layer,\n            tile_bounds=bounds,\n            zoom_levels=[12, 20],\n        ),\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"geospatial_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6. Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# upload_job\n# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Geospatial Prediction Import \n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for tiled imagery assets.\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n",
+        "**Supported annotations that can be uploaded through the SDK**\n",
+        "- Bounding box\n",
+        "- Point\n",
+        "- Polygons \n",
+        "- Polyline\n",
+        "- Free form text classifications\n",
+        "- Classification - radio\n",
+        "- Classification - checklist\n",
+        "\n",
+        "**NOT** supported:\n",
+        "- Segmentation masks\n",
+        "\n",
+        "\n",
+        "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import os\n\nimport uuid\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions\n",
+        "- Each cell shows the python annotation and the NDJson annotation for each annotation type."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Point #######\n\n# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    confidence=0.4,\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point_geo\",\n    \"confidence\": 0.4,\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    confidence=0.5,\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"confidence\": 0.5,\n    \"line\": line_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_prediction_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"confidence\": 0.5,\n    \"polygon\": polygon_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"confidence\": 0.5,\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"third_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.2)),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.2,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.3\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"geospatial_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=23,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_prediction_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\",\n                                                   confidence=0.5)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            bbox_prediction,\n            radio_prediction,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_prediction,\n            polygon_prediction_two,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# If using NDJSON"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor prediction in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        polygon_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    prediction.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(prediction)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(name=\"prediction_upload_job\" +\n                                                  str(uuid.uuid4()),\n                                                  predictions=labels)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"geospatial_prediction_demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_geospatial_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key],  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n####### Polyline #######\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data=lb_types.TiledImageData(\n            global_key=global_key,\n            tile_layer=tile_layer,\n            tile_bounds=bounds,\n            zoom_levels=[12, 20],\n        ),\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"geospatial_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6. Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# upload_job\n# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/html_predictions.ipynb b/examples/prediction_upload/html_predictions.ipynb
index f9627dcff..c46ca81da 100644
--- a/examples/prediction_upload/html_predictions.ipynb
+++ b/examples/prediction_upload/html_predictions.ipynb
@@ -1,339 +1,337 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# HTML Prediction Import\n",
-                "\n",
-                "This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for HTML assets.\n",
-                "\n",
-                "**Supported predictions**\n",
-                "- Radio Classification \n",
-                "- Checklist Classification\n",
-                "- free-text Classification\n",
-                "\n",
-                "**Not supported:**\n",
-                "- Bounding Box\n",
-                "- Polygon\n",
-                "- Point\n",
-                "- Polyline\n",
-                "- Masks\n",
-                "- NER\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport numpy as np",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification ###########\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",  # Should match the name in the ontology\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "#### Nested Classifications ######\n\n# Python annotation\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification Free-Form text  ##########\n## Text classifications do not support confidence values\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"sample_html_2.html\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"html prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names should match the name field in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\n            \"radio_question\",  # name matching the tool used in the annotation\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ])\n\nontology = client.create_ontology(\n    \"Ontology HTML Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"HTML_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.HTMLData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            checklist_prediction,\n            text_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\nfor annot in [\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_prediction_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"HTML prediction import demo\",\n                                media_type=lb.MediaType.Html)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_prediction_html\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######  Annotations ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"html_annotation_import\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# HTML Prediction Import\n",
+        "\n",
+        "This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for HTML assets.\n",
+        "\n",
+        "**Supported predictions**\n",
+        "- Radio Classification \n",
+        "- Checklist Classification\n",
+        "- free-text Classification\n",
+        "\n",
+        "**Not supported:**\n",
+        "- Bounding Box\n",
+        "- Polygon\n",
+        "- Point\n",
+        "- Polyline\n",
+        "- Masks\n",
+        "- NER\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport numpy as np",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification ###########\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",  # Should match the name in the ontology\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "#### Nested Classifications ######\n\n# Python annotation\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification Free-Form text  ##########\n## Text classifications do not support confidence values\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"sample_html_2.html\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"html prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names should match the name field in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\n            \"radio_question\",  # name matching the tool used in the annotation\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ])\n\nontology = client.create_ontology(\n    \"Ontology HTML Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"HTML_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.HTMLData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            checklist_prediction,\n            text_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\nfor annot in [\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_prediction_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"HTML prediction import demo\",\n                                media_type=lb.MediaType.Html)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_prediction_html\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######  Annotations ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"html_annotation_import\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/image_predictions.ipynb b/examples/prediction_upload/image_predictions.ipynb
index d3158884a..d81e9be8b 100644
--- a/examples/prediction_upload/image_predictions.ipynb
+++ b/examples/prediction_upload/image_predictions.ipynb
@@ -1,473 +1,471 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Image Prediction Import\n",
-                "\n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for image assets. \n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n",
-                "**Supported annotations that can be uploaded through the SDK**\n",
-                "\n",
-                "- Bounding box \n",
-                "- Polygon\n",
-                "- Point\n",
-                "- Polyline \n",
-                "- Raster Segmentation\n",
-                "- Classification free-text\n",
-                "- Classification - radio\n",
-                "- Classification - checklist\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * If you are importing more than 1,000 mask predictions at a time, consider submitting separate jobs, as they can take longer than other prediction types to import.\n",
-                "    * After the execution of this notebook a complete Model Run with predictions will be created in your organization. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotations\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding Box"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with nested classification "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding box with nested classification #######\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\",\n            \"confidence\": 0.5\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polygon"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\n# NDJSON\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Segmentation mask"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "### Raster Segmentation (Byte string array)\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\nresponse = requests.get(url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nmask_prediction = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_prediction_ndjson = {\n    \"name\": \"mask\",\n    \"classifications\": [],\n    \"mask\": {\n        \"instanceURI\": url,\n        \"colorRGB\": (255, 255, 255)\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Segmentation mask with nested classification"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "url_2 = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg_with_subclass.png\"\nresponse_2 = requests.get(url_2)\nmask_data_2 = lb_types.MaskData(im_bytes=response_2.content)\n\n# Python annotation\nmask_with_text_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_with_text_subclass_prediction_ndjson = {\n    \"name\":\n        \"mask_with_text_subclass\",\n    \"mask\": {\n        \"instanceURI\": url_2,\n        \"colorRGB\": (255, 255, 255)\n    },\n    \"classifications\": [{\n        \"name\": \"sub_free_text\",\n        \"answer\": \"free text answer\"\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\n\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s.jpeg\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(name=\"image_prediction_demo\")\ntask = dataset.create_data_rows([test_img_url])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"image_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            mask_prediction,\n            mask_with_text_subclass_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        mask_prediction_ndjson,\n        mask_with_text_subclass_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to a model run\n",
-                "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations. \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Image Prediction Demo\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nmask_annotation = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\nmask_with_text_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    mask_annotation,\n    mask_with_text_subclass_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the annotations from the project and add them to the model\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Image Prediction Import\n",
+        "\n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for image assets. \n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n",
+        "**Supported annotations that can be uploaded through the SDK**\n",
+        "\n",
+        "- Bounding box \n",
+        "- Polygon\n",
+        "- Point\n",
+        "- Polyline \n",
+        "- Raster Segmentation\n",
+        "- Classification free-text\n",
+        "- Classification - radio\n",
+        "- Classification - checklist\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * If you are importing more than 1,000 mask predictions at a time, consider submitting separate jobs, as they can take longer than other prediction types to import.\n",
+        "    * After the execution of this notebook a complete Model Run with predictions will be created in your organization. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotations\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding Box"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with nested classification "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding box with nested classification #######\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\",\n            \"confidence\": 0.5\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polygon"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\n# NDJSON\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Segmentation mask"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "### Raster Segmentation (Byte string array)\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\nresponse = requests.get(url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nmask_prediction = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_prediction_ndjson = {\n    \"name\": \"mask\",\n    \"classifications\": [],\n    \"mask\": {\n        \"instanceURI\": url,\n        \"colorRGB\": (255, 255, 255)\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Segmentation mask with nested classification"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "url_2 = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg_with_subclass.png\"\nresponse_2 = requests.get(url_2)\nmask_data_2 = lb_types.MaskData(im_bytes=response_2.content)\n\n# Python annotation\nmask_with_text_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_with_text_subclass_prediction_ndjson = {\n    \"name\":\n        \"mask_with_text_subclass\",\n    \"mask\": {\n        \"instanceURI\": url_2,\n        \"colorRGB\": (255, 255, 255)\n    },\n    \"classifications\": [{\n        \"name\": \"sub_free_text\",\n        \"answer\": \"free text answer\"\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\n\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s.jpeg\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(name=\"image_prediction_demo\")\ntask = dataset.create_data_rows([test_img_url])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"image_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            mask_prediction,\n            mask_with_text_subclass_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        mask_prediction_ndjson,\n        mask_with_text_subclass_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to a model run\n",
+        "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations. \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Image Prediction Demo\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nmask_annotation = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\nmask_with_text_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    mask_annotation,\n    mask_with_text_subclass_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the annotations from the project and add them to the model\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/pdf_predictions.ipynb b/examples/prediction_upload/pdf_predictions.ipynb
index a10d5ad94..251cece49 100644
--- a/examples/prediction_upload/pdf_predictions.ipynb
+++ b/examples/prediction_upload/pdf_predictions.ipynb
@@ -1,422 +1,420 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# PDF Prediction Import "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "*Annotation types*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box\n",
-                "- Entities\n",
-                "- Relationships (only supported for MAL imports)\n",
-                "\n",
-                "\n",
-                "*NDJson*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box \n",
-                "- Entities \n",
-                "- Relationships (only supported for MAL imports)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entity ##########\n\n# Annotation Types\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"confidence\":\n        0.5,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification #########\n\n# Annotation types\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Bounding Box ###########\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": bbox_dim_1,\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############## Classification Free-form text ##############\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### BBOX with nested classifications #########\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\",\n                                confidence=0.5,\n                            )),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\",\n                    \"confidence\": 0.5,\n                },\n            }],\n        },\n    }],\n    \"bbox\": bbox_dim,\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
-                "\n",
-                "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
-                "- The document must have no more than 15 pages.\n",
-                "- The file size should not exceed 20 MB.\n",
-                "\n",
-                "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
-                "\n",
-                "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
-                "\n",
-                "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"0801.3483.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"PDF_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the predictions payload\n",
-                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting payload should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "To extract the generated text layer url we first need to export the data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n\n# re-write the entity annotation with text selections\nentities_prediction_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", confidence=0.5, textSelections=text_selections)\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_prediction_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    textSelections=text_selections_ner,\n)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_prediction_ndjson}\")\nprint(f\"entities_annotation={entities_prediction}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_prediction_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Python annotation \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_predictions = []\n\nlabel_predictions.append(\n    lb_types.Label(\n        data=lb_types.DocumentData(global_key=global_key),\n        annotations=[\n            entities_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            ner_with_checklist_subclass_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_predictions_ndjson = []\nfor annot in [\n        entities_prediction_ndjson,\n        checklist_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        ner_with_checklist_subclass_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_predictions_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run\n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.1 Create a labelbox project \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Document Prediction Import Demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.2 Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "entities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(name=\"named_entity\",\n                                  textSelections=text_selections),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",)),\n            )\n        ],\n    )),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\",\n                                  text_selections=text_selections_ner),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.4 Create the label object "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.5 Upload annotations to the project using Label import\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.6 Send the annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# PDF Prediction Import "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "*Annotation types*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box\n",
+        "- Entities\n",
+        "- Relationships (only supported for MAL imports)\n",
+        "\n",
+        "\n",
+        "*NDJson*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box \n",
+        "- Entities \n",
+        "- Relationships (only supported for MAL imports)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entity ##########\n\n# Annotation Types\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"confidence\":\n        0.5,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification #########\n\n# Annotation types\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Bounding Box ###########\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": bbox_dim_1,\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############## Classification Free-form text ##############\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### BBOX with nested classifications #########\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\",\n                                confidence=0.5,\n                            )),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\",\n                    \"confidence\": 0.5,\n                },\n            }],\n        },\n    }],\n    \"bbox\": bbox_dim,\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
+        "\n",
+        "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
+        "- The document must have no more than 15 pages.\n",
+        "- The file size should not exceed 20 MB.\n",
+        "\n",
+        "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
+        "\n",
+        "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
+        "\n",
+        "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"0801.3483.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"PDF_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the predictions payload\n",
+        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting payload should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "To extract the generated text layer url we first need to export the data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n\n# re-write the entity annotation with text selections\nentities_prediction_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", confidence=0.5, textSelections=text_selections)\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_prediction_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    textSelections=text_selections_ner,\n)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_prediction_ndjson}\")\nprint(f\"entities_annotation={entities_prediction}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_prediction_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Python annotation \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_predictions = []\n\nlabel_predictions.append(\n    lb_types.Label(\n        data=lb_types.DocumentData(global_key=global_key),\n        annotations=[\n            entities_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            ner_with_checklist_subclass_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_predictions_ndjson = []\nfor annot in [\n        entities_prediction_ndjson,\n        checklist_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        ner_with_checklist_subclass_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_predictions_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run\n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.1 Create a labelbox project \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Document Prediction Import Demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.2 Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "entities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(name=\"named_entity\",\n                                  textSelections=text_selections),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",)),\n            )\n        ],\n    )),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\",\n                                  text_selections=text_selections_ner),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.4 Create the label object "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.5 Upload annotations to the project using Label import\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.6 Send the annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Option deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/text_predictions.ipynb b/examples/prediction_upload/text_predictions.ipynb
index 1616d7341..1f0204b5a 100644
--- a/examples/prediction_upload/text_predictions.ipynb
+++ b/examples/prediction_upload/text_predictions.ipynb
@@ -1,348 +1,346 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Text Prediction Import\n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for text assets. \n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK: \n",
-                "\n",
-                "* Entity\n",
-                "* Classification radio \n",
-                "* Classification checklist \n",
-                "* Classification free-form text \n",
-                "\n",
-                "**Not** supported:\n",
-                "* Segmentation mask\n",
-                "* Polygon\n",
-                "* Bounding box \n",
-                "* Polyline\n",
-                "* Point \n",
-                "\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nentities_prediction = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\",\n                                                confidence=0.5)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\": \"named_entity\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 10,\n        \"end\": 20\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"lorem-ipsum.txt\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"text prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\")\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Text Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Text,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"text_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_predictions = []\nlabel_predictions.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_prediction,\n            nested_radio_prediction,\n            radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson_predictions = []\nfor annot in [\n        entities_prediction_ndjson,\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson_predictions.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Text Prediction Import Demo\",\n                                media_type=lb.MediaType.Text)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "named_entity = lb_types.TextEntity(start=10, end=20)\nentities_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\")\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n# Create a Label for predictions\nlabel = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.TextData(global_key=global_key),\n        annotations=[\n            entities_annotation,\n            nested_radio_annotation,\n            radio_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Text Prediction Import\n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for text assets. \n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "\n",
+        "* Entity\n",
+        "* Classification radio \n",
+        "* Classification checklist \n",
+        "* Classification free-form text \n",
+        "\n",
+        "**Not** supported:\n",
+        "* Segmentation mask\n",
+        "* Polygon\n",
+        "* Bounding box \n",
+        "* Polyline\n",
+        "* Point \n",
+        "\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nentities_prediction = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\",\n                                                confidence=0.5)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\": \"named_entity\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 10,\n        \"end\": 20\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"lorem-ipsum.txt\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"text prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\")\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Text Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Text,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"text_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_predictions = []\nlabel_predictions.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_prediction,\n            nested_radio_prediction,\n            radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson_predictions = []\nfor annot in [\n        entities_prediction_ndjson,\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson_predictions.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Text Prediction Import Demo\",\n                                media_type=lb.MediaType.Text)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "named_entity = lb_types.TextEntity(start=10, end=20)\nentities_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\")\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n# Create a Label for predictions\nlabel = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.TextData(global_key=global_key),\n        annotations=[\n            entities_annotation,\n            nested_radio_annotation,\n            radio_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/video_predictions.ipynb b/examples/prediction_upload/video_predictions.ipynb
index 2315b01cc..7c00690c4 100644
--- a/examples/prediction_upload/video_predictions.ipynb
+++ b/examples/prediction_upload/video_predictions.ipynb
@@ -1,378 +1,376 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Video Prediction Import \n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for video assets.\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n",
-                "**Supported annotations that can be uploaded through the SDK**\n",
-                "- Bounding box\n",
-                "- Point\n",
-                "- Polyline\n",
-                "- Classification - radio\n",
-                "- Classification - checklist\n",
-                "- Classification - free text\n",
-                "- Nested classifications \n",
-                "\n",
-                "**NOT** supported:\n",
-                "- Polygons [not supported in video editor or model]\n",
-                "- Raster segmentation masks [not supported in model]\n",
-                "- Vector segmentation masks [not supported in video editor]\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions\n",
-                "- Confidence scores are currently not supported for segment or frame annotations, which are required for bounding box, point, and line for video assets. For this tutorial, only the radio and checklist annotations will have confidence scores."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding box (frame specific) ###########\n\n# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 15,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Point ########\n# Confidence score is not supported for VideoObjectAnnotation\n# Python Annotation\npoint_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"confidence\":\n        0.5,\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Polyline (frame specific) ########\n# confidence scores are not supported in polyline annotations\n\n# Python Annotation\npolyline_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        12,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 280\n                        },\n                        {\n                            \"x\": 300,\n                            \"y\": 380\n                        },\n                        {\n                            \"x\": 400,\n                            \"y\": 460\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Frame base classifications ########\n\n# Python Annotation\nradio_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n]\n\nchecklist_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_prediction_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n## frame specific\nframe_checklist_classification_prediction_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 39,\n                \"end\": 45\n            }],\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Global Classifications #########\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    )\n]\n\nglobal_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}\n\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\", confidence=0.5)),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\",\n                                confidence=0.5,\n                            )\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classifications under frame base tools ##########\n# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                                  confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\", confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### Free text classification ###########\ntext_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n    )\n]\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"confidence\": 0.5,\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"sample-video-2.mp4\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"Video prediction demo\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors: \", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.\n                    INDEX,  ## defined scope for frame classifications\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Video Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"video_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of [code here](https://docs.labelbox.com/reference/import-video-annotations).\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python Annotation Types"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_predictions = []\nannotations_list = [\n    point_prediction,\n    bbox_prediction,\n    polyline_prediction,\n    checklist_prediction,\n    radio_prediction,\n    nested_radio_prediction,\n    nested_checklist_prediction,\n    frame_bbox_with_checklist_subclass_prediction,\n    global_radio_prediction,\n    global_checklist_prediction,\n    text_prediction,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabel_predictions.append(\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=flatten_list_annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel_prediction_ndjson = []\n\nfor annotation in [\n        point_prediction_ndjson,\n        bbox_prediction_ndjson,\n        polyline_prediction_ndjson,\n        frame_checklist_classification_prediction_ndjson,\n        frame_radio_classification_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        frame_bbox_with_checklist_subclass_prediction_ndjson,\n        global_radio_classification_ndjson,\n        global_checklist_classification_ndjson,\n        text_prediction_ndjson,\n]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"video_prediction_demo\",\n                                media_type=lb.MediaType.Video)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_video_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # A list of data rows, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n]\n\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\nframe_bbox_with_checklist_subclass = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n\nlabels = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabels.append(\n    lb_types.Label(\n        data=lb_types.VideoData(global_key=global_key),\n        annotations=flatten_list_annotations,\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"video_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6. Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Video Prediction Import \n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for video assets.\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n",
+        "**Supported annotations that can be uploaded through the SDK**\n",
+        "- Bounding box\n",
+        "- Point\n",
+        "- Polyline\n",
+        "- Classification - radio\n",
+        "- Classification - checklist\n",
+        "- Classification - free text\n",
+        "- Nested classifications \n",
+        "\n",
+        "**NOT** supported:\n",
+        "- Polygons [not supported in video editor or model]\n",
+        "- Raster segmentation masks [not supported in model]\n",
+        "- Vector segmentation masks [not supported in video editor]\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions\n",
+        "- Confidence scores are currently not supported for segment or frame annotations, which are required for bounding box, point, and line for video assets. For this tutorial, only the radio and checklist annotations will have confidence scores."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding box (frame specific) ###########\n\n# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 15,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Point ########\n# Confidence score is not supported for VideoObjectAnnotation\n# Python Annotation\npoint_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"confidence\":\n        0.5,\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Polyline (frame specific) ########\n# confidence scores are not supported in polyline annotations\n\n# Python Annotation\npolyline_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        12,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 280\n                        },\n                        {\n                            \"x\": 300,\n                            \"y\": 380\n                        },\n                        {\n                            \"x\": 400,\n                            \"y\": 460\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Frame base classifications ########\n\n# Python Annotation\nradio_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n]\n\nchecklist_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_prediction_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n## frame specific\nframe_checklist_classification_prediction_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 39,\n                \"end\": 45\n            }],\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Global Classifications #########\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    )\n]\n\nglobal_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}\n\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\", confidence=0.5)),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\",\n                                confidence=0.5,\n                            )\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classifications under frame base tools ##########\n# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                                  confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\", confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### Free text classification ###########\ntext_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n    )\n]\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"confidence\": 0.5,\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"sample-video-2.mp4\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"Video prediction demo\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors: \", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.\n                    INDEX,  ## defined scope for frame classifications\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Video Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"video_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of [code here](https://docs.labelbox.com/reference/import-video-annotations).\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python Annotation Types"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_predictions = []\nannotations_list = [\n    point_prediction,\n    bbox_prediction,\n    polyline_prediction,\n    checklist_prediction,\n    radio_prediction,\n    nested_radio_prediction,\n    nested_checklist_prediction,\n    frame_bbox_with_checklist_subclass_prediction,\n    global_radio_prediction,\n    global_checklist_prediction,\n    text_prediction,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabel_predictions.append(\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=flatten_list_annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel_prediction_ndjson = []\n\nfor annotation in [\n        point_prediction_ndjson,\n        bbox_prediction_ndjson,\n        polyline_prediction_ndjson,\n        frame_checklist_classification_prediction_ndjson,\n        frame_radio_classification_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        frame_bbox_with_checklist_subclass_prediction_ndjson,\n        global_radio_classification_ndjson,\n        global_checklist_classification_ndjson,\n        text_prediction_ndjson,\n]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"video_prediction_demo\",\n                                media_type=lb.MediaType.Video)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_video_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # A list of data rows, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n]\n\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\nframe_bbox_with_checklist_subclass = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n\nlabels = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabels.append(\n    lb_types.Label(\n        data=lb_types.VideoData(global_key=global_key),\n        annotations=flatten_list_annotations,\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"video_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6. Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index 7b119d8a0..6f49259f0 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -1,178 +1,176 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Project Setup\n",
-                "* This notebok describes how to create and configure a project\n",
-                "* This is the same as creating a new project in the editor and going through all of the steps."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
-                "    * An ontology must be set\n",
-                "    * A Batch must be created"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Dataset\n",
-                "* Create dataset and attach data\n",
-                "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Or Select an Ontology\n",
-                "* Optionally create an ontology or select from an existing one.\n",
-                "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
-                "   "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Project and Setup the Editor\n",
-                "* Setting up a project will add an ontology and will enable labeling to begin\n",
-                "* Creating batches will add all data_rows belonging to the dataset to the queue."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Add data to your projects "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Review"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Project Setup\n",
+        "* This notebok describes how to create and configure a project\n",
+        "* This is the same as creating a new project in the editor and going through all of the steps."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
+        "    * An ontology must be set\n",
+        "    * A Batch must be created"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Dataset\n",
+        "* Create dataset and attach data\n",
+        "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Or Select an Ontology\n",
+        "* Optionally create an ontology or select from an existing one.\n",
+        "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
+        "   "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Project and Setup the Editor\n",
+        "* Setting up a project will add an ontology and will enable labeling to begin\n",
+        "* Creating batches will add all data_rows belonging to the dataset to the queue."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Add data to your projects "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Review"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/queue_management.ipynb b/examples/project_configuration/queue_management.ipynb
index 7d0c7938f..2400032c1 100644
--- a/examples/project_configuration/queue_management.ipynb
+++ b/examples/project_configuration/queue_management.ipynb
@@ -1,208 +1,206 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Queue Management"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* The queue is used to task labelers with specific assets\n",
-                "* We can do any of the following:\n",
-                "    * Set quality settings\n",
-                "    * Set the order of items in the queue\n",
-                "    * Set the percent of assets to review"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q numpy",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nfrom uuid import uuid4\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Set up demo project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox  project\n\nproject = client.create_project(\n    name=\"batch-test-project\",\n    description=\"a description\",\n    quality_mode=QualityMode.\n    Benchmark,  # For Consensus projects use quality_mode = QualityMode.Consensus\n    media_type=lb.MediaType.Image,\n)\n\ndataset = client.create_dataset(name=\"queue_dataset\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create ontology and attach to project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Add data to your dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Example image\nuploads = []\nglobal_keys = []\n# Generate data rows\nfor i in range(1, 5):\n    global_key = str(uuid4())\n    row = {\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_key,\n    }\n    global_keys.append(global_key)\n    uploads.append(row)\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"Errors\", data_rows.errors)\nprint(\"Dataset status: \", data_rows.status)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Attach data to your project and set data row priority"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######## Create batches\n\n# Create the batch\n\nbatch = project.create_batch(\n    \"batch-demo\",  # Each batch in a project must have a unique name\n    global_keys=global_keys[\n        0:2],  # A list of data rows, data row ids or global keys\n    priority=\n    5,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nbatch2 = project.create_batch(\n    \"batch-demo-2\",  # Each batch in a project must have a unique name\n    # Provide a slice of the data since you can't import assets with global keys that already exist in the project.\n    global_keys=global_keys[\n        2:4],  # A list of data rows, data row ids or global keys\n    priority=\n    1,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nprint(\"Batch: \", batch)\nprint(\"Batch2: \", batch2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(\"View the results here:\",\n      f\"https://app.labelbox.com/projects/{project.uid}\")\n# Click `start labeling` to see the images in order",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Queue Order\n",
-                "- Add priority for each data row\n",
-                "- Update priority for each data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_task = project.export()\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Get data rows from project\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(lb.GlobalKey(data_row[\"data_row\"][\"global_key\"])\n                    )  # Convert json data row into data row identifier object\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Get label parameter overrides (LPOs)\nproject_lpos = project.labeling_parameter_overrides()\n\nfor lpo in project_lpos:\n    print(lpo)\n    print(\"Data row:\", lpo.data_row().uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Add LPOs\nlpos = []\npriority = 1\nfor data_row in data_rows:\n    lpos.append((data_row, priority))\n    priority += 1\n\nproject.set_labeling_parameter_overrides(lpos)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Update LPOs\nglobal_keys = []\nfor data_row in data_rows:\n    global_keys.append(data_row.key)\n\nproject.update_data_row_labeling_priority(data_rows=lb.GlobalKeys(global_keys),\n                                          priority=1)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Queue Management"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* The queue is used to task labelers with specific assets\n",
+        "* We can do any of the following:\n",
+        "    * Set quality settings\n",
+        "    * Set the order of items in the queue\n",
+        "    * Set the percent of assets to review"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q numpy",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nfrom uuid import uuid4\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Set up demo project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox  project\n\nproject = client.create_project(\n    name=\"batch-test-project\",\n    description=\"a description\",\n    quality_mode=QualityMode.\n    Benchmark,  # For Consensus projects use quality_mode = QualityMode.Consensus\n    media_type=lb.MediaType.Image,\n)\n\ndataset = client.create_dataset(name=\"queue_dataset\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create ontology and attach to project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Add data to your dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Example image\nuploads = []\nglobal_keys = []\n# Generate data rows\nfor i in range(1, 5):\n    global_key = str(uuid4())\n    row = {\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_key,\n    }\n    global_keys.append(global_key)\n    uploads.append(row)\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"Errors\", data_rows.errors)\nprint(\"Dataset status: \", data_rows.status)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Attach data to your project and set data row priority"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######## Create batches\n\n# Create the batch\n\nbatch = project.create_batch(\n    \"batch-demo\",  # Each batch in a project must have a unique name\n    global_keys=global_keys[\n        0:2],  # A list of data rows, data row ids or global keys\n    priority=\n    5,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nbatch2 = project.create_batch(\n    \"batch-demo-2\",  # Each batch in a project must have a unique name\n    # Provide a slice of the data since you can't import assets with global keys that already exist in the project.\n    global_keys=global_keys[\n        2:4],  # A list of data rows, data row ids or global keys\n    priority=\n    1,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nprint(\"Batch: \", batch)\nprint(\"Batch2: \", batch2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(\"View the results here:\",\n      f\"https://app.labelbox.com/projects/{project.uid}\")\n# Click `start labeling` to see the images in order",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Queue Order\n",
+        "- Add priority for each data row\n",
+        "- Update priority for each data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_task = project.export()\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Get data rows from project\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(lb.GlobalKey(data_row[\"data_row\"][\"global_key\"])\n                    )  # Convert json data row into data row identifier object\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Get label parameter overrides (LPOs)\nproject_lpos = project.labeling_parameter_overrides()\n\nfor lpo in project_lpos:\n    print(lpo)\n    print(\"Data row:\", lpo.data_row().uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Add LPOs\nlpos = []\npriority = 1\nfor data_row in data_rows:\n    lpos.append((data_row, priority))\n    priority += 1\n\nproject.set_labeling_parameter_overrides(lpos)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Update LPOs\nglobal_keys = []\nfor data_row in data_rows:\n    global_keys.append(data_row.key)\n\nproject.update_data_row_labeling_priority(data_rows=lb.GlobalKeys(global_keys),\n                                          priority=1)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index 4cb3da344..6ec331562 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -1,212 +1,210 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Webhook Configuration"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Webhooks are supported for the following events:\n",
-                "* label_created\n",
-                "* label_updated\n",
-                "* label_deleted"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q requests\n%pip install -q hmac\n%pip install -q hashlib\n%pip install -q flask\n%pip install -q Werkzeug",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Configure NGROK (Optional)\n",
-                "* If you do not have a public ip address then follow along\n",
-                "\n",
-                "1. Create an account:\n",
-                "    https://dashboard.ngrok.com/get-started/setup\n",
-                "2. Download ngrok and extract the zip file\n",
-                "3. Add ngrok to your path\n",
-                "4. Add the authtoken `ngrok authtoken <token>`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Configure server to receive requests"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Test server"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(requests.get(\"http://localhost:3001\").text)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Webhook"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "- Set ip address if your ip is publicly accessible.\n",
-                "- Otherwise use the following to get ngrok public_url"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update Webhook"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### List and delete all webhooks"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Webhook Configuration"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Webhooks are supported for the following events:\n",
+        "* label_created\n",
+        "* label_updated\n",
+        "* label_deleted"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q requests\n%pip install -q hmac\n%pip install -q hashlib\n%pip install -q flask\n%pip install -q Werkzeug",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Configure NGROK (Optional)\n",
+        "* If you do not have a public ip address then follow along\n",
+        "\n",
+        "1. Create an account:\n",
+        "    https://dashboard.ngrok.com/get-started/setup\n",
+        "2. Download ngrok and extract the zip file\n",
+        "3. Add ngrok to your path\n",
+        "4. Add the authtoken `ngrok authtoken <token>`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Configure server to receive requests"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Test server"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(requests.get(\"http://localhost:3001\").text)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Webhook"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "- Set ip address if your ip is publicly accessible.\n",
+        "- Otherwise use the following to get ngrok public_url"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update Webhook"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### List and delete all webhooks"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/pyproject.toml b/examples/pyproject.toml
index 5c746f6b2..5f5d381d7 100644
--- a/examples/pyproject.toml
+++ b/examples/pyproject.toml
@@ -27,7 +27,7 @@ dev-dependencies = [
 ]
 
 [tool.rye.scripts]
-clean = { chain = ["clean:databooks", "clean:black", "clean:format"] }
+clean = { chain = ["clean:black", "clean:format", "clean:databooks"] }
 "clean:databooks" = 'databooks meta --rm-outs . --yes'
 "clean:black" = 'black --line-length 80 . --quiet'
 "clean:format" = { call = "scripts.format_notebooks" }
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index 5833cd200..4b2676e5f 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -18,19 +18,18 @@
         "</td>",
     ],
 }
-
 LINK_CELL = {
     "cell_type": "markdown",
     "id": "cb5611d0",
     "metadata": {},
     "source": [
         "<td>\n",
-        '<a href="{colab}" target="_blank"><img',
-        'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>',
+        '<a href="{colab}" target="_blank"><img\n',
+        'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>\n',
         "</td>\n",
         "\n",
         "<td>\n",
-        '<a href="{github}" target="_blank"><img',
+        '<a href="{github}" target="_blank"><img\n',
         'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>\n',
         "</td>",
     ],
diff --git a/examples/template.ipynb b/examples/template.ipynb
index 290c262a4..bc48a2c90 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -1,135 +1,133 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>\n",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>\n",
-                "\n",
-                "<td>\n",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar imports together",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Main section header\n",
-                "\n",
-                "* Include information on what section discusses"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Sub section header\n",
-                "\n",
-                "* Include information on what sub section discusses\n",
-                "* If parameters are needed specify they need included"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Notebook section with steps\n",
-                "\n",
-                "* For long winded processes step headers should be included"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step \"step number\""
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Include information on step"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### \"step number.sub step number\"\n",
-                "\n",
-                "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up\n",
-                "Uncomment and run the cell below to optionally delete Labelbox objects created"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar imports together",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API key and client\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Main section header\n",
+        "\n",
+        "* Include information on what section discusses"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Sub section header\n",
+        "\n",
+        "* Include information on what sub section discusses\n",
+        "* If parameters are needed specify they need included"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Notebook section with steps\n",
+        "\n",
+        "* For long winded processes step headers should be included"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step \"step number\""
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Include information on step"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### \"step number.sub step number\"\n",
+        "\n",
+        "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Clean up\n",
+        "Uncomment and run the cell below to optionally delete Labelbox objects created"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file

From 0d3acd0cfc1ec85d20d20768513d7ef4903013b0 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 10:13:07 -0500
Subject: [PATCH 60/80] modified files again

---
 examples/scripts/format_notebooks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index 4b2676e5f..36972e96a 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -15,7 +15,7 @@
     "source": [
         "<td>",
         '   <a target="_blank" href="https://labelbox.com" ><img src="https://labelbox.com/blog/content/images/2021/02/logo-v4.svg" width=256/></a>',
-        "</td>",
+        "</td>\n",
     ],
 }
 LINK_CELL = {

From 2878c3acafb1bdfa5ee83bb49b14dadc6684aaf6 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 10:15:17 -0500
Subject: [PATCH 61/80] added conditional to workflow to not run on pr merge

---
 .github/workflows/notebooks.yml | 35 ++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 18 deletions(-)

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index 53339848b..e311c5b84 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -13,7 +13,7 @@ on:
 permissions:
   contents: write
   pull-requests: write
-  
+
 jobs:
   format:
     if: github.event.pull_request.merged == false
@@ -29,10 +29,10 @@ jobs:
         run: rye run clean
       - name: Commit changes
         run: |
-            git config --local user.email "github-actions[bot]@users.noreply.github.com"
-            git config --local user.name "github-actions[bot]"
-            git add examples/.
-            git commit -m ":art: Cleaned" || exit 0
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git add examples/.
+          git commit -m ":art: Cleaned" || exit 0
       - name: Push changes
         uses: ad-m/github-push-action@master
         with:
@@ -46,16 +46,15 @@ jobs:
     outputs:
       addedOrModified: ${{ steps.filter.outputs.addedOrModified }}
     steps:
-    - uses: dorny/paths-filter@v3
-      id: filter
-      with:
-        filters: |
-          addedOrModified:
-            - added|deleted: 'examples/**/*.ipynb'
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            addedOrModified:
+              - added|deleted: 'examples/**/*.ipynb'
   create:
     needs: changes
-    if: github.event.pull_request.merged == false
-    if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
+    if: ${{ needs.changes.outputs.addedOrModified == 'true' }} && github.event.pull_request.merged == false
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
@@ -68,12 +67,12 @@ jobs:
         run: rye run create-readme
       - name: Commit changes
         run: |
-            git config --local user.email "github-actions[bot]@users.noreply.github.com"
-            git config --local user.name "github-actions[bot]"
-            git add examples/.
-            git commit -m ":memo: README updated" || exit 0
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git add examples/.
+          git commit -m ":memo: README updated" || exit 0
       - name: Push changes
         uses: ad-m/github-push-action@master
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          branch: ${{ github.head_ref }}
\ No newline at end of file
+          branch: ${{ github.head_ref }}

From c3e739aceb714404f72f248ccf5fbb038345a018 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Wed, 22 May 2024 15:16:30 +0000
Subject: [PATCH 62/80] :art: Cleaned

---
 examples/annotation_import/audio.ipynb                          | 2 +-
 examples/annotation_import/conversational.ipynb                 | 2 +-
 examples/annotation_import/conversational_LLM.ipynb             | 2 +-
 .../annotation_import/conversational_LLM_data_generation.ipynb  | 2 +-
 examples/annotation_import/dicom.ipynb                          | 2 +-
 examples/annotation_import/html.ipynb                           | 2 +-
 examples/annotation_import/image.ipynb                          | 2 +-
 examples/annotation_import/import_labeled_dataset_image.ipynb   | 2 +-
 examples/annotation_import/pdf.ipynb                            | 2 +-
 examples/annotation_import/text.ipynb                           | 2 +-
 examples/annotation_import/tiled.ipynb                          | 2 +-
 examples/annotation_import/video.ipynb                          | 2 +-
 examples/basics/basics.ipynb                                    | 2 +-
 examples/basics/batches.ipynb                                   | 2 +-
 examples/basics/custom_embeddings.ipynb                         | 2 +-
 examples/basics/data_row_metadata.ipynb                         | 2 +-
 examples/basics/data_rows.ipynb                                 | 2 +-
 examples/basics/export_v1_to_v2_migration_support.ipynb         | 2 +-
 examples/basics/ontologies.ipynb                                | 2 +-
 examples/basics/projects.ipynb                                  | 2 +-
 examples/basics/user_management.ipynb                           | 2 +-
 examples/exports/composite_mask_export.ipynb                    | 2 +-
 examples/exports/export_data.ipynb                              | 2 +-
 examples/extras/classification_confusion_matrix.ipynb           | 2 +-
 examples/foundry/object_detection.ipynb                         | 2 +-
 .../huggingface/huggingface_custom_embeddings.ipynb             | 2 +-
 examples/integrations/langchain/langchain.ipynb                 | 2 +-
 examples/integrations/sam/meta_sam.ipynb                        | 2 +-
 examples/integrations/sam/meta_sam_video.ipynb                  | 2 +-
 examples/model_experiments/custom_metrics_basics.ipynb          | 2 +-
 examples/model_experiments/custom_metrics_demo.ipynb            | 2 +-
 examples/model_experiments/model_predictions_to_project.ipynb   | 2 +-
 examples/model_experiments/model_slices.ipynb                   | 2 +-
 examples/prediction_upload/conversational_LLM_predictions.ipynb | 2 +-
 examples/prediction_upload/conversational_predictions.ipynb     | 2 +-
 examples/prediction_upload/geospatial_predictions.ipynb         | 2 +-
 examples/prediction_upload/html_predictions.ipynb               | 2 +-
 examples/prediction_upload/image_predictions.ipynb              | 2 +-
 examples/prediction_upload/pdf_predictions.ipynb                | 2 +-
 examples/prediction_upload/text_predictions.ipynb               | 2 +-
 examples/prediction_upload/video_predictions.ipynb              | 2 +-
 examples/project_configuration/project_setup.ipynb              | 2 +-
 examples/project_configuration/queue_management.ipynb           | 2 +-
 examples/project_configuration/webhooks.ipynb                   | 2 +-
 examples/template.ipynb                                         | 2 +-
 45 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb
index 356554ba1..c798be914 100644
--- a/examples/annotation_import/audio.ipynb
+++ b/examples/annotation_import/audio.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/conversational.ipynb b/examples/annotation_import/conversational.ipynb
index 54bfbc820..d62ae9ee8 100644
--- a/examples/annotation_import/conversational.ipynb
+++ b/examples/annotation_import/conversational.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index 01efccd21..fa1fe02b0 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/conversational_LLM_data_generation.ipynb b/examples/annotation_import/conversational_LLM_data_generation.ipynb
index d5d03d749..8fb71b846 100644
--- a/examples/annotation_import/conversational_LLM_data_generation.ipynb
+++ b/examples/annotation_import/conversational_LLM_data_generation.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/dicom.ipynb b/examples/annotation_import/dicom.ipynb
index 301f68233..cd4c88497 100644
--- a/examples/annotation_import/dicom.ipynb
+++ b/examples/annotation_import/dicom.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/html.ipynb b/examples/annotation_import/html.ipynb
index 20d38368c..7ef731bce 100644
--- a/examples/annotation_import/html.ipynb
+++ b/examples/annotation_import/html.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/image.ipynb b/examples/annotation_import/image.ipynb
index 6d99c0e51..b8c6cd2a6 100644
--- a/examples/annotation_import/image.ipynb
+++ b/examples/annotation_import/image.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/import_labeled_dataset_image.ipynb b/examples/annotation_import/import_labeled_dataset_image.ipynb
index 7b7fee951..9ebd52883 100644
--- a/examples/annotation_import/import_labeled_dataset_image.ipynb
+++ b/examples/annotation_import/import_labeled_dataset_image.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/pdf.ipynb b/examples/annotation_import/pdf.ipynb
index cfc2b82bd..5c0742ba5 100644
--- a/examples/annotation_import/pdf.ipynb
+++ b/examples/annotation_import/pdf.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/text.ipynb b/examples/annotation_import/text.ipynb
index 104189074..1d4bb99d6 100644
--- a/examples/annotation_import/text.ipynb
+++ b/examples/annotation_import/text.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 83e547a5a..19cb28d4e 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/annotation_import/video.ipynb b/examples/annotation_import/video.ipynb
index 01dc1c48b..726e22738 100644
--- a/examples/annotation_import/video.ipynb
+++ b/examples/annotation_import/video.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index 7e9c78a7a..3b7796603 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index 15d898289..2d07682b5 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/custom_embeddings.ipynb b/examples/basics/custom_embeddings.ipynb
index 6448cf41a..45a0b2df0 100644
--- a/examples/basics/custom_embeddings.ipynb
+++ b/examples/basics/custom_embeddings.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/data_row_metadata.ipynb b/examples/basics/data_row_metadata.ipynb
index 3d2c63424..fa5970ce2 100644
--- a/examples/basics/data_row_metadata.ipynb
+++ b/examples/basics/data_row_metadata.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/data_rows.ipynb b/examples/basics/data_rows.ipynb
index 7c16b4eca..46e758ee9 100644
--- a/examples/basics/data_rows.ipynb
+++ b/examples/basics/data_rows.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
index 0a76d7fd1..d44b60a17 100644
--- a/examples/basics/export_v1_to_v2_migration_support.ipynb
+++ b/examples/basics/export_v1_to_v2_migration_support.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/ontologies.ipynb b/examples/basics/ontologies.ipynb
index a576d39f8..0058424fd 100644
--- a/examples/basics/ontologies.ipynb
+++ b/examples/basics/ontologies.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index 74e9551dd..3bc3d8cb9 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index 77845f620..ffe656406 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/exports/composite_mask_export.ipynb b/examples/exports/composite_mask_export.ipynb
index fc21d0c85..ced50a4ea 100644
--- a/examples/exports/composite_mask_export.ipynb
+++ b/examples/exports/composite_mask_export.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/exports/export_data.ipynb b/examples/exports/export_data.ipynb
index 0f31eb823..e0fa8f728 100644
--- a/examples/exports/export_data.ipynb
+++ b/examples/exports/export_data.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
index 1eea6203f..03980ac0d 100644
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ b/examples/extras/classification_confusion_matrix.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/foundry/object_detection.ipynb b/examples/foundry/object_detection.ipynb
index 7c04afaca..5cf092bca 100644
--- a/examples/foundry/object_detection.ipynb
+++ b/examples/foundry/object_detection.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index eb955cef6..e86fe85b9 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/integrations/langchain/langchain.ipynb b/examples/integrations/langchain/langchain.ipynb
index c893efebc..f6653d001 100644
--- a/examples/integrations/langchain/langchain.ipynb
+++ b/examples/integrations/langchain/langchain.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/integrations/sam/meta_sam.ipynb b/examples/integrations/sam/meta_sam.ipynb
index 0bcc92203..4802f7603 100644
--- a/examples/integrations/sam/meta_sam.ipynb
+++ b/examples/integrations/sam/meta_sam.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/integrations/sam/meta_sam_video.ipynb b/examples/integrations/sam/meta_sam_video.ipynb
index ac14a0c0d..76e64105e 100644
--- a/examples/integrations/sam/meta_sam_video.ipynb
+++ b/examples/integrations/sam/meta_sam_video.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/model_experiments/custom_metrics_basics.ipynb b/examples/model_experiments/custom_metrics_basics.ipynb
index a7278447c..0face2b24 100644
--- a/examples/model_experiments/custom_metrics_basics.ipynb
+++ b/examples/model_experiments/custom_metrics_basics.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/model_experiments/custom_metrics_demo.ipynb b/examples/model_experiments/custom_metrics_demo.ipynb
index 311e1bb56..8e0412f40 100644
--- a/examples/model_experiments/custom_metrics_demo.ipynb
+++ b/examples/model_experiments/custom_metrics_demo.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/model_experiments/model_predictions_to_project.ipynb b/examples/model_experiments/model_predictions_to_project.ipynb
index a9476c791..a1560feec 100644
--- a/examples/model_experiments/model_predictions_to_project.ipynb
+++ b/examples/model_experiments/model_predictions_to_project.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/model_experiments/model_slices.ipynb b/examples/model_experiments/model_slices.ipynb
index 3da98ef68..a91709fa0 100644
--- a/examples/model_experiments/model_slices.ipynb
+++ b/examples/model_experiments/model_slices.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index 69e73fc89..be9878401 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/conversational_predictions.ipynb b/examples/prediction_upload/conversational_predictions.ipynb
index 97c65659c..b6a649d56 100644
--- a/examples/prediction_upload/conversational_predictions.ipynb
+++ b/examples/prediction_upload/conversational_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/geospatial_predictions.ipynb b/examples/prediction_upload/geospatial_predictions.ipynb
index df131c510..e3c1212f3 100644
--- a/examples/prediction_upload/geospatial_predictions.ipynb
+++ b/examples/prediction_upload/geospatial_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/html_predictions.ipynb b/examples/prediction_upload/html_predictions.ipynb
index c46ca81da..14e5510a1 100644
--- a/examples/prediction_upload/html_predictions.ipynb
+++ b/examples/prediction_upload/html_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/image_predictions.ipynb b/examples/prediction_upload/image_predictions.ipynb
index d81e9be8b..b28c9f78f 100644
--- a/examples/prediction_upload/image_predictions.ipynb
+++ b/examples/prediction_upload/image_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/pdf_predictions.ipynb b/examples/prediction_upload/pdf_predictions.ipynb
index 251cece49..82d9f05cb 100644
--- a/examples/prediction_upload/pdf_predictions.ipynb
+++ b/examples/prediction_upload/pdf_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/text_predictions.ipynb b/examples/prediction_upload/text_predictions.ipynb
index 1f0204b5a..8943ca17b 100644
--- a/examples/prediction_upload/text_predictions.ipynb
+++ b/examples/prediction_upload/text_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/prediction_upload/video_predictions.ipynb b/examples/prediction_upload/video_predictions.ipynb
index 7c00690c4..b4a7a5cd4 100644
--- a/examples/prediction_upload/video_predictions.ipynb
+++ b/examples/prediction_upload/video_predictions.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index 6f49259f0..2733332b1 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/project_configuration/queue_management.ipynb b/examples/project_configuration/queue_management.ipynb
index 2400032c1..3ee993dd0 100644
--- a/examples/project_configuration/queue_management.ipynb
+++ b/examples/project_configuration/queue_management.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index 6ec331562..36b6f977b 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },
diff --git a/examples/template.ipynb b/examples/template.ipynb
index bc48a2c90..6a052a9b6 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "<td>",
         "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>"
+        "</td>\n"
       ],
       "cell_type": "markdown"
     },

From afb3dbc6dbf13daa9bfda4d19625016cbbbe33fc Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 10:21:15 -0500
Subject: [PATCH 63/80] modified contributing md from feedback

---
 examples/CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index d63c3fced..fd817c502 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -21,7 +21,7 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 5. Imports and installs should come after the main header under a **Setup** section.
 6. Labelbox and other platforms with clients and API keys should be specified under a single section.
 7. Subsections need a second level header **##** and an overview of the section.
-8. The last cell should be a clean up section to delete any labelbox objects created.
+8. The last cell should be a clean up section to delete any labelbox objects created and commented out to prevent accidentally running code blocks.
 
 > [!IMPORTANT]
 > Please make sure to remove any API keys before pushing changes

From 88a65614dcb628ae87515a9fbf4ae1b1a078724f Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 12:52:14 -0500
Subject: [PATCH 64/80] removed python version file and changed email for
 authors

---
 examples/.python-version | 1 -
 examples/pyproject.toml  | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)
 delete mode 100644 examples/.python-version

diff --git a/examples/.python-version b/examples/.python-version
deleted file mode 100644
index 9ad6380c1..000000000
--- a/examples/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.8.18
diff --git a/examples/pyproject.toml b/examples/pyproject.toml
index 5f5d381d7..260271adf 100644
--- a/examples/pyproject.toml
+++ b/examples/pyproject.toml
@@ -2,7 +2,7 @@
 name = "examples"
 version = "0.1.0"
 description = "Labelbox Python Example Notebooks"
-authors = [{ name = "Labelbox", email = "support@labelbox.com" }]
+authors = [{ name = "Labelbox", email = "docs@labelbox.com" }]
 readme = "README.md"
 # Python version matches labelbox SDK
 requires-python = ">=3.8"

From 0224bfe9b416e4c8e8f1c9b74c0376d13237b5b1 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 13:37:19 -0500
Subject: [PATCH 65/80] added comments to workflow to show what they do

---
 .github/workflows/notebooks.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index e311c5b84..3fbbd5c38 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -15,6 +15,7 @@ permissions:
   pull-requests: write
 
 jobs:
+  # Get installs from rye and run rye run clean to format
   format:
     if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
@@ -38,11 +39,11 @@ jobs:
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           branch: ${{ github.head_ref }}
+  # See if notebooks were added or deleted (name change counts as both)
   changes:
     needs: format
     if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
-    # Set job outputs to values from filter step
     outputs:
       addedOrModified: ${{ steps.filter.outputs.addedOrModified }}
     steps:
@@ -52,6 +53,7 @@ jobs:
           filters: |
             addedOrModified:
               - added|deleted: 'examples/**/*.ipynb'
+  # Create readme if the above job shows true using rye run create-readme
   create:
     needs: changes
     if: ${{ needs.changes.outputs.addedOrModified == 'true' }} && github.event.pull_request.merged == false

From 7c259f4b888ab4242087023e6a3e39fe2aae7886 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 13:49:57 -0500
Subject: [PATCH 66/80] added more info on contributing md from feedback

---
 examples/CONTRIBUTING.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index fd817c502..bb7dd5999 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -22,6 +22,7 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 6. Labelbox and other platforms with clients and API keys should be specified under a single section.
 7. Subsections need a second level header **##** and an overview of the section.
 8. The last cell should be a clean up section to delete any labelbox objects created and commented out to prevent accidentally running code blocks.
+9. Notebook github action [workflows](#github-workflows) are required to run successfully before merging.
 
 > [!IMPORTANT]
 > Please make sure to remove any API keys before pushing changes

From 526845f8b17b11a4a4caa02f82760ba05861a654 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 20:39:57 -0500
Subject: [PATCH 67/80] reverted to org workflow to test

---
 examples/.python-version | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 examples/.python-version

diff --git a/examples/.python-version b/examples/.python-version
new file mode 100644
index 000000000..9ad6380c1
--- /dev/null
+++ b/examples/.python-version
@@ -0,0 +1 @@
+3.8.18

From 5bb7a9f56c5864758480a5f384cdfa9a4dbe4ac3 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 06:49:13 -0500
Subject: [PATCH 68/80] testing bug

---
 .github/workflows/notebooks.yml        | 10 ++++++++--
 .github/workflows/rdme-custompages.yml |  5 ++++-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index 3fbbd5c38..0206f4f27 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -24,7 +24,10 @@ jobs:
         with:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
-      - uses: ./.github/actions/notebooks
+      - uses: ./.github/actions/python-package-shared-setup
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.8
       - name: Format
         working-directory: examples
         run: rye run clean
@@ -63,7 +66,10 @@ jobs:
         with:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
-      - uses: ./.github/actions/notebooks
+      - uses: ./.github/actions/python-package-shared-setup
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.8
       - name: Create readme
         working-directory: examples
         run: rye run create-readme
diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index 73a2bed4f..e1bcc9bca 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -14,7 +14,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: ./.github/actions/notebooks
+      - uses: ./.github/actions/python-package-shared-setup
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.8
       - name: Create readme
         working-directory: examples
         run: rye run create-doc-readme

From 404db4217383760b497eb1f827751ab60966491b Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 06:54:12 -0500
Subject: [PATCH 69/80] moved migration guide to somewhere that makes sense

---
 .../{basics => exports}/export_v1_to_v2_migration_support.ipynb   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/{basics => exports}/export_v1_to_v2_migration_support.ipynb (100%)

diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/exports/export_v1_to_v2_migration_support.ipynb
similarity index 100%
rename from examples/basics/export_v1_to_v2_migration_support.ipynb
rename to examples/exports/export_v1_to_v2_migration_support.ipynb

From 8e67bb2656863e3b0a156d841b9d77e051f80304 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Thu, 23 May 2024 11:55:17 +0000
Subject: [PATCH 70/80] :art: Cleaned

---
 examples/exports/export_v1_to_v2_migration_support.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/exports/export_v1_to_v2_migration_support.ipynb b/examples/exports/export_v1_to_v2_migration_support.ipynb
index d44b60a17..2434c7369 100644
--- a/examples/exports/export_v1_to_v2_migration_support.ipynb
+++ b/examples/exports/export_v1_to_v2_migration_support.ipynb
@@ -16,12 +16,12 @@
       "metadata": {},
       "source": [
         "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
         "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
         "</td>\n",
         "\n",
         "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
         "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
         "</td>"
       ],

From 395681d83b582f3eafc38584598b8c6e2c07e56c Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Thu, 23 May 2024 11:56:04 +0000
Subject: [PATCH 71/80] :memo: README updated

---
 examples/README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index 6e4f8e749..2627388c4 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -21,11 +21,6 @@
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>Export V1 to V2 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
     <tr>
       <td>Data Rows</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
@@ -75,6 +70,11 @@
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <td>Export V1 to V2 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>Composite Mask Export</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>

From 5456cfc863065f39452f8edb441eff0bfbda3c67 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 06:58:03 -0500
Subject: [PATCH 72/80] tested bug

---
 .github/workflows/notebooks.yml        | 8 ++++----
 .github/workflows/rdme-custompages.yml | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index 0206f4f27..9a4acc72b 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -26,8 +26,8 @@ jobs:
           fetch-depth: 0
       - uses: ./.github/actions/python-package-shared-setup
         with:
-          rye-version: ${{ vars.RYE_VERSION }}
-          python-version: 3.8
+          rye-version: 0.33.0
+          python-version: 3.12
       - name: Format
         working-directory: examples
         run: rye run clean
@@ -68,8 +68,8 @@ jobs:
           fetch-depth: 0
       - uses: ./.github/actions/python-package-shared-setup
         with:
-          rye-version: ${{ vars.RYE_VERSION }}
-          python-version: 3.8
+          rye-version: 0.33.0
+          python-version: 3.12
       - name: Create readme
         working-directory: examples
         run: rye run create-readme
diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index e1bcc9bca..161c6e452 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -16,7 +16,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: ./.github/actions/python-package-shared-setup
         with:
-          rye-version: ${{ vars.RYE_VERSION }}
+          rye-version: 0.33.0
           python-version: 3.8
       - name: Create readme
         working-directory: examples

From 0eca7a3a874d14d50a5111b9cc73c8c72d671806 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 07:06:45 -0500
Subject: [PATCH 73/80] testing removing pandas fixes bug

---
 examples/pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/pyproject.toml b/examples/pyproject.toml
index 260271adf..05b4ed0af 100644
--- a/examples/pyproject.toml
+++ b/examples/pyproject.toml
@@ -5,7 +5,7 @@ description = "Labelbox Python Example Notebooks"
 authors = [{ name = "Labelbox", email = "docs@labelbox.com" }]
 readme = "README.md"
 # Python version matches labelbox SDK
-requires-python = ">=3.8"
+requires-python = ">=3."
 dependencies = []
 
 [project.urls]
@@ -23,7 +23,7 @@ dev-dependencies = [
     "black[jupyter]>=24.4.2",
     "databooks>=1.3.10",
     # higher versions dont support python 3.8
-    "pandas==1.5.3",
+    "pandas>=1.5.3",
 ]
 
 [tool.rye.scripts]

From 040ab0ca6abb17f54808c66903d01130a7d271f9 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 07:11:18 -0500
Subject: [PATCH 74/80] switched to var rye version bug was fixed

---
 .github/actions/notebooks/action.yml   | 18 ------------------
 .github/workflows/notebooks.yml        |  4 ++--
 .github/workflows/rdme-custompages.yml |  4 ++--
 3 files changed, 4 insertions(+), 22 deletions(-)
 delete mode 100644 .github/actions/notebooks/action.yml

diff --git a/.github/actions/notebooks/action.yml b/.github/actions/notebooks/action.yml
deleted file mode 100644
index 631e61328..000000000
--- a/.github/actions/notebooks/action.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: Labelbox Example Notebook Setup
-    
-runs:  
-  using: "composite"
-  steps:
-    - name: Install the latest version of rye
-      uses: eifinger/setup-rye@v2
-      with:
-        enable-cache: true
-    - name: Rye Setup
-      shell: bash
-      run: |
-        rye config --set-bool behavior.use-uv=true
-    - name: Environment setup
-      working-directory: examples
-      shell: bash
-      run: | 
-        rye sync -f --update-all
\ No newline at end of file
diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index 9a4acc72b..382176478 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -26,7 +26,7 @@ jobs:
           fetch-depth: 0
       - uses: ./.github/actions/python-package-shared-setup
         with:
-          rye-version: 0.33.0
+          rye-version: ${{ vars.RYE_VERSION }}
           python-version: 3.12
       - name: Format
         working-directory: examples
@@ -68,7 +68,7 @@ jobs:
           fetch-depth: 0
       - uses: ./.github/actions/python-package-shared-setup
         with:
-          rye-version: 0.33.0
+          rye-version: ${{ vars.RYE_VERSION }}
           python-version: 3.12
       - name: Create readme
         working-directory: examples
diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index 161c6e452..c39a2d550 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -16,8 +16,8 @@ jobs:
       - uses: actions/checkout@v4
       - uses: ./.github/actions/python-package-shared-setup
         with:
-          rye-version: 0.33.0
-          python-version: 3.8
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.12
       - name: Create readme
         working-directory: examples
         run: rye run create-doc-readme

From 10a8c58868e9637f8614140cc9da128ce93d7826 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 07:42:45 -0500
Subject: [PATCH 75/80] remove python version file in example notebooks not
 needed

---
 examples/.python-version | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 examples/.python-version

diff --git a/examples/.python-version b/examples/.python-version
deleted file mode 100644
index 9ad6380c1..000000000
--- a/examples/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.8.18

From e487a718f7814cc55ab27dac2e133771bb13350c Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 12:46:01 -0500
Subject: [PATCH 76/80] removed broken notebook until reworked

---
 .../classification_confusion_matrix.ipynb     | 107 ------------------
 1 file changed, 107 deletions(-)
 delete mode 100644 examples/extras/classification_confusion_matrix.ipynb

diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
deleted file mode 100644
index 03980ac0d..000000000
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ /dev/null
@@ -1,107 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {},
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "<td>",
-        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-        "</td>\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-        "</td>\n",
-        "\n",
-        "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img\n",
-        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
-        "</td>"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Classification Confusion matrix\n",
-        "\n",
-        "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
-        "\n",
-        "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
-        "\n",
-        "## Installs"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": "%pip install -q \"labelbox[data]\" scikit-learn matplotlib ipywidgets",
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Imports"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": "# Imports and etc\n\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.metrics import classification_report\nimport matplotlib.pyplot as plt\nimport ipywidgets as widgets\nimport labelbox as lb\n\n\ndef get_labelers():\n    labels = project.label_generator()\n    labelers = []\n\n    for l in labels:\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    return labelers\n\n\ndef get_paired_labels(labeler1, labeler2):\n    labels = project.label_generator()\n    l1 = []\n    l2 = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        # Check if benchmark\n        if l.extra[\"Benchmark ID\"]:\n            label_dict[\n                l.data.uid][\"Benchmarks\"] = l.annotations[0].value.answer.name\n\n        # Skip labels not created by either of our labelers\n        if (l.extra[\"Created By\"] != labeler1 and\n                l.extra[\"Created By\"] != labeler2):\n            continue\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n    for uid in label_dict.keys():\n        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n            l1.append(label_dict[uid][labeler1])\n            l2.append(label_dict[uid][labeler2])\n\n    return l1, l2\n\n\ndef get_label_dict():\n    labels = project.label_generator()\n    labelers = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    print(labelers)\n    return label_dict, labelers",
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": "# Labelbox API + project and dataset\n\n# Add your api key\nAPI_KEY = None\nlb = lb.Client(api_key=API_KEY)\n\n# Add your project and dataset uids here\nproject = lb.get_project(\"xxx\")\ndataset = lb.get_dataset(\"xxx\")",
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Labeler Selection"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": "# Selectors\n\nlabelers = get_labelers()\n\nitems = [\n    widgets.Dropdown(\n        options=[\"Benchmarks\"] + labelers,\n        description=\"Compare this:\",\n        disabled=False,\n    ),\n    widgets.Dropdown(\n        options=labelers,\n        description=\"Against this:\",\n        disabled=False,\n    ),\n]\n\nwidgets.VBox(items)",
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Confusion Matrix"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": "# Plot Matrix\n\nl1, l2 = get_paired_labels(items[0].value, items[1].value)\n\nfig, ax = plt.subplots(figsize=(15, 15))\nc = ConfusionMatrixDisplay.from_predictions(\n    l1,\n    l2,\n    labels=list(set(l1)),\n    sample_weight=None,\n    normalize=None,\n    display_labels=None,\n    include_values=True,\n    xticks_rotation=\"vertical\",\n    values_format=None,\n    cmap=\"Blues\",\n    ax=ax,\n    colorbar=False,\n)\n\n_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n\ncr = classification_report(l1,\n                           l2,\n                           output_dict=False,\n                           target_names=list(set(l1)),\n                           zero_division=1)\n\nprint(cr)",
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "metadata": {},
-      "source": "",
-      "cell_type": "code",
-      "outputs": [],
-      "execution_count": null
-    }
-  ]
-}
\ No newline at end of file

From 4e451b68b723dbc242c75565fae59c18734716c0 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 17:39:05 -0500
Subject: [PATCH 77/80] fixed merge conflict

---
 requirements-dev.lock | 34 ++++++++++++++++++++++++++++------
 1 file changed, 28 insertions(+), 6 deletions(-)

diff --git a/requirements-dev.lock b/requirements-dev.lock
index 6832f7280..a90e24e44 100644
--- a/requirements-dev.lock
+++ b/requirements-dev.lock
@@ -6,7 +6,6 @@
 #   features: []
 #   all-features: false
 #   with-sources: false
-#   generate-hashes: false
 
 -e file:libs/labelbox
 alabaster==0.7.13
@@ -38,6 +37,8 @@ charset-normalizer==3.3.2
 click==8.1.7
     # via black
     # via typer
+commonmark==0.9.1
+    # via rich
 coverage==7.5.1
     # via pytest-cov
 databooks==1.3.10
@@ -60,6 +61,10 @@ fastjsonschema==2.19.1
     # via nbformat
 geojson==3.1.0
     # via labelbox
+gitdb==4.0.11
+    # via gitpython
+gitpython==3.1.43
+    # via databooks
 google-api-core==2.19.0
     # via labelbox
 google-auth==2.29.0
@@ -80,6 +85,10 @@ importlib-resources==6.4.0
     # via jsonschema-specifications
 iniconfig==2.0.0
     # via pytest
+ipython==8.12.3
+    # via black
+jedi==0.19.1
+    # via ipython
 jinja2==3.1.4
     # via nbconvert
     # via sphinx
@@ -99,13 +108,11 @@ jupyterlab-pygments==0.3.0
 makefun==1.15.2
     # via decopatch
     # via pytest-cases
-markdown-it-py==3.0.0
-    # via rich
 markupsafe==2.1.5
     # via jinja2
     # via nbconvert
-mdurl==0.1.2
-    # via markdown-it-py
+matplotlib-inline==0.1.7
+    # via ipython
 mistune==3.0.2
     # via nbconvert
 mypy==1.10.0
@@ -141,26 +148,35 @@ pickleshare==0.7.5
 pkgutil-resolve-name==1.3.10
     # via jsonschema
 platformdirs==4.2.2
+    # via black
     # via jupyter-core
     # via yapf
 pluggy==1.5.0
     # via pytest
+prompt-toolkit==3.0.43
+    # via ipython
 proto-plus==1.23.0
     # via google-api-core
 protobuf==4.25.3
     # via google-api-core
     # via googleapis-common-protos
     # via proto-plus
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
 pyasn1==0.6.0
     # via pyasn1-modules
     # via rsa
 pyasn1-modules==0.4.0
     # via google-auth
 pydantic==2.7.1
+    # via databooks
     # via labelbox
 pydantic-core==2.18.2
     # via pydantic
 pygments==2.18.0
+    # via ipython
     # via nbconvert
     # via rich
     # via sphinx
@@ -180,6 +196,7 @@ python-dateutil==2.8.2
     # via pandas
 pytz==2024.1
     # via babel
+    # via pandas
 pyzmq==26.0.3
     # via jupyter-client
 referencing==0.35.1
@@ -191,7 +208,8 @@ requests==2.32.2
     # via google-api-core
     # via labelbox
     # via sphinx
-rich==13.7.1
+rich==12.6.0
+    # via databooks
     # via typer
 rpds-py==0.18.1
     # via jsonschema
@@ -235,6 +253,8 @@ strenum==0.4.15
     # via labelbox
 tinycss2==1.3.0
     # via nbconvert
+tokenize-rt==5.2.0
+    # via black
 toml-cli==0.6.1
 tomli==2.0.1
     # via black
@@ -250,6 +270,7 @@ tornado==6.4
 tqdm==4.66.4
     # via labelbox
 traitlets==5.14.3
+    # via ipython
     # via jupyter-client
     # via jupyter-core
     # via matplotlib-inline
@@ -257,6 +278,7 @@ traitlets==5.14.3
     # via nbconvert
     # via nbformat
 typer==0.12.3
+    # via databooks
     # via toml-cli
 types-pillow==10.2.0.20240520
 types-python-dateutil==2.9.0.20240316

From 914d820dbcc4682a47c2f21fa7ead1460eaf1323 Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Wed, 22 May 2024 10:02:36 -0500
Subject: [PATCH 78/80] squashed custom

fixed merge conflict
---
 .github/actions/notebooks/action.yml          |  18 -
 .github/workflows/notebooks.yml               |  48 +-
 .github/workflows/python-package-develop.yml  |  13 +-
 .github/workflows/rdme-custompages.yml        |   5 +-
 .python-version                               |   2 +-
 docs/conf.py                                  |   2 +-
 docs/labelbox/project-overview.rst            |   6 +
 examples/.python-version                      |   1 -
 examples/CONTRIBUTING.md                      |   3 +-
 examples/README.md                            |  10 +-
 examples/annotation_import/audio.ipynb        | 596 ++++++-----
 .../annotation_import/conversational.ipynb    | 628 ++++++------
 .../conversational_LLM.ipynb                  | 614 ++++++-----
 .../conversational_LLM_data_generation.ipynb  | 536 +++++-----
 examples/annotation_import/dicom.ipynb        | 530 +++++-----
 examples/annotation_import/html.ipynb         | 610 ++++++-----
 examples/annotation_import/image.ipynb        | 868 ++++++++--------
 .../import_labeled_dataset_image.ipynb        | 368 ++++---
 examples/annotation_import/pdf.ipynb          | 768 +++++++-------
 examples/annotation_import/text.ipynb         | 630 ++++++------
 examples/annotation_import/tiled.ipynb        | 688 +++++++------
 examples/annotation_import/video.ipynb        | 812 ++++++++-------
 examples/basics/basics.ipynb                  | 368 ++++---
 examples/basics/batches.ipynb                 | 596 ++++++-----
 examples/basics/custom_embeddings.ipynb       | 570 ++++++-----
 examples/basics/data_row_metadata.ipynb       | 598 ++++++-----
 examples/basics/data_rows.ipynb               | 628 ++++++------
 .../export_v1_to_v2_migration_support.ipynb   | 593 -----------
 examples/basics/ontologies.ipynb              | 602 ++++++-----
 examples/basics/projects.ipynb                | 680 +++++++------
 examples/basics/user_management.ipynb         | 490 +++++----
 examples/exports/composite_mask_export.ipynb  | 508 +++++-----
 examples/exports/export_data.ipynb            | 952 +++++++++---------
 .../export_v1_to_v2_migration_support.ipynb   | 591 +++++++++++
 .../classification_confusion_matrix.ipynb     | 109 --
 examples/foundry/object_detection.ipynb       | 514 +++++-----
 .../huggingface_custom_embeddings.ipynb       | 298 +++---
 .../integrations/langchain/langchain.ipynb    | 350 ++++---
 examples/integrations/sam/meta_sam.ipynb      | 496 +++++----
 .../integrations/sam/meta_sam_video.ipynb     | 464 +++++----
 .../custom_metrics_basics.ipynb               | 508 +++++-----
 .../custom_metrics_demo.ipynb                 | 846 ++++++++--------
 .../model_predictions_to_project.ipynb        | 538 +++++-----
 examples/model_experiments/model_slices.ipynb | 488 +++++----
 .../conversational_LLM_predictions.ipynb      | 770 +++++++-------
 .../conversational_predictions.ipynb          | 712 +++++++------
 .../geospatial_predictions.ipynb              | 756 +++++++-------
 .../prediction_upload/html_predictions.ipynb  | 672 ++++++-------
 .../prediction_upload/image_predictions.ipynb | 940 +++++++++--------
 .../prediction_upload/pdf_predictions.ipynb   | 838 ++++++++-------
 .../prediction_upload/text_predictions.ipynb  | 690 +++++++------
 .../prediction_upload/video_predictions.ipynb | 750 +++++++-------
 .../project_configuration/project_setup.ipynb | 350 ++++---
 .../queue_management.ipynb                    | 410 ++++----
 examples/project_configuration/webhooks.ipynb | 418 ++++----
 examples/pyproject.toml                       |   8 +-
 examples/scripts/format_notebooks.py          |  17 +-
 examples/template.ipynb                       | 264 +++--
 libs/labelbox/CHANGELOG.md                    |  27 +
 libs/labelbox/Dockerfile                      |   6 +-
 libs/labelbox/pyproject.toml                  |   2 +-
 libs/labelbox/src/labelbox/__init__.py        |   2 +-
 libs/labelbox/src/labelbox/schema/project.py  |  64 ++
 .../src/labelbox/schema/project_overview.py   |  38 +
 .../integration/test_project_model_config.py  |   5 +
 .../tests/integration/test_task_queue.py      |  14 +-
 requirements-dev.lock                         |  64 +-
 requirements.lock                             |  34 +-
 68 files changed, 13671 insertions(+), 13723 deletions(-)
 delete mode 100644 .github/actions/notebooks/action.yml
 create mode 100644 docs/labelbox/project-overview.rst
 delete mode 100644 examples/.python-version
 delete mode 100644 examples/basics/export_v1_to_v2_migration_support.ipynb
 create mode 100644 examples/exports/export_v1_to_v2_migration_support.ipynb
 delete mode 100644 examples/extras/classification_confusion_matrix.ipynb
 create mode 100644 libs/labelbox/src/labelbox/schema/project_overview.py

diff --git a/.github/actions/notebooks/action.yml b/.github/actions/notebooks/action.yml
deleted file mode 100644
index 631e61328..000000000
--- a/.github/actions/notebooks/action.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: Labelbox Example Notebook Setup
-    
-runs:  
-  using: "composite"
-  steps:
-    - name: Install the latest version of rye
-      uses: eifinger/setup-rye@v2
-      with:
-        enable-cache: true
-    - name: Rye Setup
-      shell: bash
-      run: |
-        rye config --set-bool behavior.use-uv=true
-    - name: Environment setup
-      working-directory: examples
-      shell: bash
-      run: | 
-        rye sync -f --update-all
\ No newline at end of file
diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index 950f4bea0..382176478 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -13,8 +13,9 @@ on:
 permissions:
   contents: write
   pull-requests: write
-  
+
 jobs:
+  # Get installs from rye and run rye run clean to format
   format:
     if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
@@ -23,56 +24,63 @@ jobs:
         with:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
-      - uses: ./.github/actions/notebooks
+      - uses: ./.github/actions/python-package-shared-setup
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.12
       - name: Format
         working-directory: examples
         run: rye run clean
       - name: Commit changes
         run: |
-            git config --local user.email "github-actions[bot]@users.noreply.github.com"
-            git config --local user.name "github-actions[bot]"
-            git add examples/.
-            git commit -m ":art: Cleaned" || exit 0
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git add examples/.
+          git commit -m ":art: Cleaned" || exit 0
       - name: Push changes
         uses: ad-m/github-push-action@master
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           branch: ${{ github.head_ref }}
+  # See if notebooks were added or deleted (name change counts as both)
   changes:
     needs: format
     if: github.event.pull_request.merged == false
     runs-on: ubuntu-latest
-    # Set job outputs to values from filter step
     outputs:
       addedOrModified: ${{ steps.filter.outputs.addedOrModified }}
     steps:
-    - uses: dorny/paths-filter@v3
-      id: filter
-      with:
-        filters: |
-          addedOrModified:
-            - added|deleted: 'examples/**/*.ipynb'
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            addedOrModified:
+              - added|deleted: 'examples/**/*.ipynb'
+  # Create readme if the above job shows true using rye run create-readme
   create:
     needs: changes
-    if: ${{ needs.changes.outputs.addedOrModified == 'true' }}
+    if: ${{ needs.changes.outputs.addedOrModified == 'true' }} && github.event.pull_request.merged == false
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.head_ref }}
           fetch-depth: 0
-      - uses: ./.github/actions/notebooks
+      - uses: ./.github/actions/python-package-shared-setup
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.12
       - name: Create readme
         working-directory: examples
         run: rye run create-readme
       - name: Commit changes
         run: |
-            git config --local user.email "github-actions[bot]@users.noreply.github.com"
-            git config --local user.name "github-actions[bot]"
-            git add examples/.
-            git commit -m ":memo: README updated" || exit 0
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git add examples/.
+          git commit -m ":memo: README updated" || exit 0
       - name: Push changes
         uses: ad-m/github-push-action@master
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          branch: ${{ github.head_ref }}
\ No newline at end of file
+          branch: ${{ github.head_ref }}
diff --git a/.github/workflows/python-package-develop.yml b/.github/workflows/python-package-develop.yml
index f2dd35da4..cd5110033 100644
--- a/.github/workflows/python-package-develop.yml
+++ b/.github/workflows/python-package-develop.yml
@@ -56,21 +56,16 @@ jobs:
         with:
           token: ${{ secrets.ACTIONS_ACCESS_TOKEN  }}
           ref: ${{ github.head_ref }}
-      - name: Install the latest version of rye
-        uses: eifinger/setup-rye@v2
+      - uses: ./.github/actions/python-package-shared-setup
         with:
-          version: '0.32.0'
-          enable-cache: true
-      - name: Rye Setup
-        run: |
-          rye config --set-bool behavior.use-uv=true
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: '3.8'
       - name: Create build
         id: create-build
         working-directory: libs/labelbox
         run: |
           VERSION=$(date +"%Y.%m.%d.%H.%M")
           echo "pip install --index-url https://test.pypi.org/simple/ --extra-index-url=https://pypi.org/simple/ labelbox-test@$VERSION" >> "$GITHUB_STEP_SUMMARY"
-          rye sync
           rye version "$VERSION"
           rye run toml set --toml-path pyproject.toml project.name labelbox-test
           rye build
@@ -138,4 +133,4 @@ jobs:
             linux/arm64
 
           tags: |
-            ${{ env.CONTAINER_IMAGE }}:${{ github.sha }}
\ No newline at end of file
+            ${{ env.CONTAINER_IMAGE }}:${{ github.sha }}
diff --git a/.github/workflows/rdme-custompages.yml b/.github/workflows/rdme-custompages.yml
index 73a2bed4f..c39a2d550 100644
--- a/.github/workflows/rdme-custompages.yml
+++ b/.github/workflows/rdme-custompages.yml
@@ -14,7 +14,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: ./.github/actions/notebooks
+      - uses: ./.github/actions/python-package-shared-setup
+        with:
+          rye-version: ${{ vars.RYE_VERSION }}
+          python-version: 3.12
       - name: Create readme
         working-directory: examples
         run: rye run create-doc-readme
diff --git a/.python-version b/.python-version
index 9ad6380c1..143c2f5d0 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.8.18
+3.8.19
diff --git a/docs/conf.py b/docs/conf.py
index d46790a01..b1d68de7f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -16,7 +16,7 @@
 project = 'Python SDK reference'
 copyright = '2024, Labelbox'
 author = 'Labelbox'
-release = '3.69.1'
+release = '3.70.0'
 
 # -- General configuration ---------------------------------------------------
 
diff --git a/docs/labelbox/project-overview.rst b/docs/labelbox/project-overview.rst
new file mode 100644
index 000000000..3157eef13
--- /dev/null
+++ b/docs/labelbox/project-overview.rst
@@ -0,0 +1,6 @@
+ProjectOverview
+===============================================================================================
+
+.. automodule:: labelbox.schema.project-overview
+   :members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/examples/.python-version b/examples/.python-version
deleted file mode 100644
index 9ad6380c1..000000000
--- a/examples/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.8.18
diff --git a/examples/CONTRIBUTING.md b/examples/CONTRIBUTING.md
index d63c3fced..bb7dd5999 100644
--- a/examples/CONTRIBUTING.md
+++ b/examples/CONTRIBUTING.md
@@ -21,7 +21,8 @@ Review our [template notebook](template.ipynbs) for general overview on how note
 5. Imports and installs should come after the main header under a **Setup** section.
 6. Labelbox and other platforms with clients and API keys should be specified under a single section.
 7. Subsections need a second level header **##** and an overview of the section.
-8. The last cell should be a clean up section to delete any labelbox objects created.
+8. The last cell should be a clean up section to delete any labelbox objects created and commented out to prevent accidentally running code blocks.
+9. Notebook github action [workflows](#github-workflows) are required to run successfully before merging.
 
 > [!IMPORTANT]
 > Please make sure to remove any API keys before pushing changes
diff --git a/examples/README.md b/examples/README.md
index 6e4f8e749..2627388c4 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -21,11 +21,6 @@
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
       <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
     </tr>
-    <tr>
-      <td>Export V1 to V2 Migration Support</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
     <tr>
       <td>Data Rows</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
@@ -75,6 +70,11 @@
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <td>Export V1 to V2 Migration Support</td>
+      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
+      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_v1_to_v2_migration_support.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
+    </tr>
     <tr>
       <td>Composite Mask Export</td>
       <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
diff --git a/examples/annotation_import/audio.ipynb b/examples/annotation_import/audio.ipynb
index babc5b9ce..c798be914 100644
--- a/examples/annotation_import/audio.ipynb
+++ b/examples/annotation_import/audio.ipynb
@@ -1,301 +1,299 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Audio Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n",
-                "\n",
-                "Suported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Bouding box\n",
-                "* NER\n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask\n",
-                "\n",
-                "MAL and Label Import:\n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for Audio"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_audio\",\n    value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_audio\",\n    \"answer\": \"free text audio annotation\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_audio\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_audio\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_audio\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_audio\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT,\n                      name=\"text_audio\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_audio\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_audio\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology Audio Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Audio,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n                                media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-audio-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[text_annotation, checklist_annotation, radio_annotation],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations \n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/audio.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Audio Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for audio assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Suported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Bouding box\n",
+        "* NER\n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask\n",
+        "\n",
+        "MAL and Label Import:\n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for Audio"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_audio\",\n    value=lb_types.Text(answer=\"free text audio annotation\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_audio\",\n    \"answer\": \"free text audio annotation\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_audio\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_audio\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_audio\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_audio\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample-audio-1.mp3\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"audio_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the text annotation, we provided the `name` as `text_audio`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_audio`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT,\n                      name=\"text_audio\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_audio\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_audio\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology Audio Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Audio,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(name=\"audio_project\",\n                                media_type=lb.MediaType.Audio)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-audio-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[text_annotation, checklist_annotation, radio_annotation],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations \n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational.ipynb b/examples/annotation_import/conversational.ipynb
index 139b6e494..d62ae9ee8 100644
--- a/examples/annotation_import/conversational.ipynb
+++ b/examples/annotation_import/conversational.ipynb
@@ -1,317 +1,315 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Conversational Text Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for conversational text assets, and also  cover MAL and Label Import methods:\n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "* NER\n",
-                "\n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Relationships\n",
-                "* Bouding box \n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask \n",
-                "\n",
-                "MAL and Label Import:\n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# message based classifications\nner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n    message_id=\"0\",\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions right here\",\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"2\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the text annotation, we provided the `name` as `text_convo`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_convo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. However,for conversational texts NDJSON is the only supported format. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations \n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Conversational Text Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for conversational text assets, and also  cover MAL and Label Import methods:\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "* NER\n",
+        "\n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Relationships\n",
+        "* Bouding box \n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask \n",
+        "\n",
+        "MAL and Label Import:\n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for conversational text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# message based classifications\nner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n    message_id=\"0\",\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions right here\",\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"2\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the text annotation, we provided the `name` as `text_convo`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_convo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. However,for conversational texts NDJSON is the only supported format. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations \n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational_LLM.ipynb b/examples/annotation_import/conversational_LLM.ipynb
index 9f0339fad..fa1fe02b0 100644
--- a/examples/annotation_import/conversational_LLM.ipynb
+++ b/examples/annotation_import/conversational_LLM.ipynb
@@ -1,310 +1,308 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
-                "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Set up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Entity "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
-                "\n",
-                "```\n",
-                "\"modelOutputs\" : [\n",
-                "  {\n",
-                "      \"title\": \"Name of the response option\",\n",
-                "      \"content\": \"Content of the response\",\n",
-                "      \"modelConfigName\": \"Name of model configuration\"\n",
-                "  }\n",
-                "]\n",
-                "```\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Example of row_data with model outputs"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Python annotation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "NDJSON annotation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload annotations to a project as pre-labels or complete labels "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Model Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LLM pairwise comparison with Conversational text using MAL and Ground truth\n",
+        "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Set up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Supported annotations for conversational text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Entity "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_annotation_ndjson = {\n    \"name\": \"ner\",\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "text_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_annotation_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Message based\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows with \"modelOutputs\" into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
+        "\n",
+        "```\n",
+        "\"modelOutputs\" : [\n",
+        "  {\n",
+        "      \"title\": \"Name of the response option\",\n",
+        "      \"content\": \"Content of the response\",\n",
+        "      \"modelConfigName\": \"Name of model configuration\"\n",
+        "  }\n",
+        "]\n",
+        "```\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Example of row_data with model outputs"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"pairwise_shooping_asset\"\n\n# Upload data rows\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_annotation_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(\n    name=\"Conversational Text Annotation Import Demo (Pairwise comparison)\",\n    media_type=lb.MediaType.Conversational,\n)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a batch to send to your project\nbatch = project.create_batch(\n    \"first-batch-convo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Python annotation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "NDJSON annotation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        ner_annotation_ndjson,\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload annotations to a project as pre-labels or complete labels "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Model Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/conversational_LLM_data_generation.ipynb b/examples/annotation_import/conversational_LLM_data_generation.ipynb
index f620a3a8d..8fb71b846 100644
--- a/examples/annotation_import/conversational_LLM_data_generation.ipynb
+++ b/examples/annotation_import/conversational_LLM_data_generation.ipynb
@@ -1,271 +1,269 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LLM Data Generation with MAL and Ground Truth\n",
-                "This demo is meant to showcase how to generate prompts and responses to fine-tune large language models (LLMs) using MAL and Ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Set up "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for LLM data generation\n",
-                "Currently, we only support NDJson format for prompt and responses"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Prompt:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "prompt_annotation_ndjson = {\n    \"name\": \"Follow the prompt and select answers\",\n    \"answer\": \"This is an example of a prompt\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Responses:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "response_radio_annotation_ndjson = {\n    \"name\": \"response_radio\",\n    \"answer\": {\n        \"name\": \"response_a\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Only NDJson is currently supported\nresponse_text_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is an example of a response text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "response_checklist_annotation_ndjson = {\n    \"name\": \"response_checklist\",\n    \"answer\": [{\n        \"name\": \"response_a\"\n    }, {\n        \"name\": \"response_c\"\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Create a project and data rows in Labelbox UI\n",
-                "\n",
-                "Currently we do not support this workflow through the SDK.\n",
-                "#### Workflow:\n",
-                "\n",
-                "1. Navigate to annotate and select ***New project***\n",
-                "\n",
-                "2. Select ***LLM data generation*** and then select ***Humans generate prompts and responses***\n",
-                "\n",
-                "3. Name your project, select ***create a new dataset*** and name your dataset. (data rows will be generated automatically in \n",
-                "this step)\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Enter the project id\nproject_id = \"\"\n\n# Select one of the global keys from the data rows generated\nglobal_key = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2 : Create/select an Ontology in Labelbox UI"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Currently we do not support this workflow through the SDK\n",
-                "#### Workflow: \n",
-                "1. In your project, navigate to ***Settings*** and ***Label editor***\n",
-                "\n",
-                "2. Click on ***Edit***\n",
-                "\n",
-                "3. Create a new ontology and add the features used in this demo\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### For this demo the following ontology was generated in the UI: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_json = \"\"\"\n{\n \"tools\": [],\n \"relationships\": [],\n \"classifications\": [\n  {\n   \"schemaNodeId\": \"clpvq9d0002yt07zy0khq42rp\",\n   \"featureSchemaId\": \"clpvq9d0002ys07zyf2eo9p14\",\n   \"type\": \"prompt\",\n   \"name\": \"Follow the prompt and select answers\",\n   \"archived\": false,\n   \"required\": true,\n   \"options\": [],\n   \"instructions\": \"Follow the prompt and select answers\",\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002yz07zy0fjg28z7\",\n   \"featureSchemaId\": \"clpvq9d0002yu07zy28ik5w3i\",\n   \"type\": \"response-radio\",\n   \"name\": \"response_radio\",\n   \"instructions\": \"response_radio\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0002yw07zyci2q5adq\",\n     \"featureSchemaId\": \"clpvq9d0002yv07zyevmz1yoj\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0002yy07zy8pe48zdj\",\n     \"featureSchemaId\": \"clpvq9d0002yx07zy0jvmdxk8\",\n     \"value\": \"response_b\",\n     \"label\": \"response_b\",\n     \"position\": 1,\n     \"options\": []\n    }\n   ]\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002z107zygf8l62ys\",\n   \"featureSchemaId\": \"clpvq9d0002z007zyg26115f9\",\n   \"type\": \"response-text\",\n   \"name\": \"provide_a_reason_for_your_choice\",\n   \"instructions\": \"Provide a reason for your choice\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [],\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0102z907zy8b10hjcj\",\n   \"featureSchemaId\": \"clpvq9d0002z207zy6xla7f82\",\n   \"type\": \"response-checklist\",\n   \"name\": \"response_checklist\",\n   \"instructions\": \"response_checklist\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0102z407zy0adq0rfr\",\n     \"featureSchemaId\": \"clpvq9d0002z307zy6dqb8xsw\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z607zych8b2z5d\",\n     \"featureSchemaId\": \"clpvq9d0102z507zyfwfgacrn\",\n     \"value\": \"response_c\",\n     \"label\": \"response_c\",\n     \"position\": 1,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z807zy03y7gysp\",\n     \"featureSchemaId\": \"clpvq9d0102z707zyh61y5o3u\",\n     \"value\": \"response_d\",\n     \"label\": \"response_d\",\n     \"position\": 2,\n     \"options\": []\n    }\n   ]\n  }\n ],\n \"realTime\": false\n}\n\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        prompt_annotation_ndjson,\n        response_radio_annotation_ndjson,\n        response_text_annotation_ndjson,\n        response_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.get_project(project_id=project_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/conversational_LLM_data_generation.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LLM Data Generation with MAL and Ground Truth\n",
+        "This demo is meant to showcase how to generate prompts and responses to fine-tune large language models (LLMs) using MAL and Ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Set up "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for LLM data generation\n",
+        "Currently, we only support NDJson format for prompt and responses"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Prompt:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "prompt_annotation_ndjson = {\n    \"name\": \"Follow the prompt and select answers\",\n    \"answer\": \"This is an example of a prompt\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Responses:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "response_radio_annotation_ndjson = {\n    \"name\": \"response_radio\",\n    \"answer\": {\n        \"name\": \"response_a\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Only NDJson is currently supported\nresponse_text_annotation_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is an example of a response text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "response_checklist_annotation_ndjson = {\n    \"name\": \"response_checklist\",\n    \"answer\": [{\n        \"name\": \"response_a\"\n    }, {\n        \"name\": \"response_c\"\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Create a project and data rows in Labelbox UI\n",
+        "\n",
+        "Currently we do not support this workflow through the SDK.\n",
+        "#### Workflow:\n",
+        "\n",
+        "1. Navigate to annotate and select ***New project***\n",
+        "\n",
+        "2. Select ***LLM data generation*** and then select ***Humans generate prompts and responses***\n",
+        "\n",
+        "3. Name your project, select ***create a new dataset*** and name your dataset. (data rows will be generated automatically in \n",
+        "this step)\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Enter the project id\nproject_id = \"\"\n\n# Select one of the global keys from the data rows generated\nglobal_key = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2 : Create/select an Ontology in Labelbox UI"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Currently we do not support this workflow through the SDK\n",
+        "#### Workflow: \n",
+        "1. In your project, navigate to ***Settings*** and ***Label editor***\n",
+        "\n",
+        "2. Click on ***Edit***\n",
+        "\n",
+        "3. Create a new ontology and add the features used in this demo\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### For this demo the following ontology was generated in the UI: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_json = \"\"\"\n{\n \"tools\": [],\n \"relationships\": [],\n \"classifications\": [\n  {\n   \"schemaNodeId\": \"clpvq9d0002yt07zy0khq42rp\",\n   \"featureSchemaId\": \"clpvq9d0002ys07zyf2eo9p14\",\n   \"type\": \"prompt\",\n   \"name\": \"Follow the prompt and select answers\",\n   \"archived\": false,\n   \"required\": true,\n   \"options\": [],\n   \"instructions\": \"Follow the prompt and select answers\",\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002yz07zy0fjg28z7\",\n   \"featureSchemaId\": \"clpvq9d0002yu07zy28ik5w3i\",\n   \"type\": \"response-radio\",\n   \"name\": \"response_radio\",\n   \"instructions\": \"response_radio\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0002yw07zyci2q5adq\",\n     \"featureSchemaId\": \"clpvq9d0002yv07zyevmz1yoj\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0002yy07zy8pe48zdj\",\n     \"featureSchemaId\": \"clpvq9d0002yx07zy0jvmdxk8\",\n     \"value\": \"response_b\",\n     \"label\": \"response_b\",\n     \"position\": 1,\n     \"options\": []\n    }\n   ]\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0002z107zygf8l62ys\",\n   \"featureSchemaId\": \"clpvq9d0002z007zyg26115f9\",\n   \"type\": \"response-text\",\n   \"name\": \"provide_a_reason_for_your_choice\",\n   \"instructions\": \"Provide a reason for your choice\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [],\n   \"minCharacters\": 5,\n   \"maxCharacters\": 100\n  },\n  {\n   \"schemaNodeId\": \"clpvq9d0102z907zy8b10hjcj\",\n   \"featureSchemaId\": \"clpvq9d0002z207zy6xla7f82\",\n   \"type\": \"response-checklist\",\n   \"name\": \"response_checklist\",\n   \"instructions\": \"response_checklist\",\n   \"scope\": \"global\",\n   \"required\": true,\n   \"archived\": false,\n   \"options\": [\n    {\n     \"schemaNodeId\": \"clpvq9d0102z407zy0adq0rfr\",\n     \"featureSchemaId\": \"clpvq9d0002z307zy6dqb8xsw\",\n     \"value\": \"response_a\",\n     \"label\": \"response_a\",\n     \"position\": 0,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z607zych8b2z5d\",\n     \"featureSchemaId\": \"clpvq9d0102z507zyfwfgacrn\",\n     \"value\": \"response_c\",\n     \"label\": \"response_c\",\n     \"position\": 1,\n     \"options\": []\n    },\n    {\n     \"schemaNodeId\": \"clpvq9d0102z807zy03y7gysp\",\n     \"featureSchemaId\": \"clpvq9d0102z707zyh61y5o3u\",\n     \"value\": \"response_d\",\n     \"label\": \"response_d\",\n     \"position\": 2,\n     \"options\": []\n    }\n   ]\n  }\n ],\n \"realTime\": false\n}\n\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        prompt_annotation_ndjson,\n        response_radio_annotation_ndjson,\n        response_text_annotation_ndjson,\n        response_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.get_project(project_id=project_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_ndjson,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/dicom.ipynb b/examples/annotation_import/dicom.ipynb
index 2b9640955..cd4c88497 100644
--- a/examples/annotation_import/dicom.ipynb
+++ b/examples/annotation_import/dicom.ipynb
@@ -1,268 +1,266 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# DICOM Annotation Import\n",
-                "\n",
-                "* Annotations must be created and uploaded using NDJSON\n",
-                "* Supported annotations that can be uploaded through the SDK:\n",
-                "    * Polyline\n",
-                "    * Segmentation masks     \n",
-                "* **NOT** supported:\n",
-                "    * Bounding box\n",
-                "    * Point\n",
-                "    * Polygons\n",
-                "    * Free form text classifications\n",
-                "    * Radio classifications \n",
-                "    * Checklist classifications"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for DICOM\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######## Polyline ########\npolyline_annotation = [\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=1,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=20),\n            lb_types.Point(x=250, y=250),\n        ]),\n        segment_index=0,\n        keyframe=True,\n    ),\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=20,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=10),\n            lb_types.Point(x=300, y=300),\n        ]),\n        segment_index=1,\n        keyframe=True,\n    ),\n]\n\npolyline_annotation_ndjson = {\n    \"name\":\n        \"line_dicom\",\n    \"groupKey\":\n        \"axial\",  # should be 'axial', 'sagittal', or 'coronal'\n    \"segments\": [\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    1,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 20\n                    },\n                    {\n                        \"x\": 250,\n                        \"y\": 250\n                    },\n                ],\n            }]\n        },\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    20,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 300,\n                        \"y\": 300\n                    },\n                ],\n            }]\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Segmentation Masks ########\n\nmask_annotation = [\n    lb_types.DICOMMaskAnnotation(\n        group_key=\"axial\",\n        frames=[\n            lb_types.MaskFrame(\n                index=1,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n            lb_types.MaskFrame(\n                index=5,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n        ],\n        instances=[\n            lb_types.MaskInstance(color_rgb=(255, 255, 255),\n                                  name=\"segmentation_mask_dicom\")\n        ],\n    )\n]\n\nmask_annotation_ndjson = {\n    \"groupKey\": \"axial\",\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\":\n                    1,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n            {\n                \"index\":\n                    5,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": (255, 255, 255),\n            \"name\": \"segmentation_mask_dicom\"\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"sample-dicom-1.dcm\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-dicom-1.dcm\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"dicom_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors :\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the line annotation above, we provided the `name` as `line_dicom`. Now, when we setup our ontology, we must ensure that the name of my line tool is also `line_dicom`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-                "\n",
-                "\n",
-                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(\n        tool=lb.Tool.Type.RASTER_SEGMENTATION,\n        name=\"segmentation_mask_dicom\",\n    ),\n    lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_dicom\"),\n])\n\nontology = client.create_ontology(\n    \"Ontology DICOM Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Dicom,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project \n",
-                "Connect the ontology to the labeling project."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"dicom_project_demo\",\n                                media_type=lb.MediaType.Dicom)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create batches\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-dicom-demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # a list of data row objects, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload \n",
-                "Create the annotations payload using the snippets of code above.\n",
-                "\n",
-                "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python Annotation Types"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "annotations_list = polyline_annotation + mask_annotation\nlabels = [\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=annotations_list)\n]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations\n",
-                "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\n\nfor annotation in [polyline_annotation_ndjson, mask_annotation_ndjson]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
-                "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job-\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)\nprint(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/dicom.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# DICOM Annotation Import\n",
+        "\n",
+        "* Annotations must be created and uploaded using NDJSON\n",
+        "* Supported annotations that can be uploaded through the SDK:\n",
+        "    * Polyline\n",
+        "    * Segmentation masks     \n",
+        "* **NOT** supported:\n",
+        "    * Bounding box\n",
+        "    * Point\n",
+        "    * Polygons\n",
+        "    * Free form text classifications\n",
+        "    * Radio classifications \n",
+        "    * Checklist classifications"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for DICOM\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######## Polyline ########\npolyline_annotation = [\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=1,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=20),\n            lb_types.Point(x=250, y=250),\n        ]),\n        segment_index=0,\n        keyframe=True,\n    ),\n    lb_types.DICOMObjectAnnotation(\n        name=\"line_dicom\",\n        group_key=lb_types.GroupKey.AXIAL,\n        frame=20,\n        value=lb_types.Line(points=[\n            lb_types.Point(x=10, y=10),\n            lb_types.Point(x=200, y=10),\n            lb_types.Point(x=300, y=300),\n        ]),\n        segment_index=1,\n        keyframe=True,\n    ),\n]\n\npolyline_annotation_ndjson = {\n    \"name\":\n        \"line_dicom\",\n    \"groupKey\":\n        \"axial\",  # should be 'axial', 'sagittal', or 'coronal'\n    \"segments\": [\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    1,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 20\n                    },\n                    {\n                        \"x\": 250,\n                        \"y\": 250\n                    },\n                ],\n            }]\n        },\n        {\n            \"keyframes\": [{\n                \"frame\":\n                    20,\n                \"line\": [\n                    {\n                        \"x\": 10,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 200,\n                        \"y\": 10\n                    },\n                    {\n                        \"x\": 300,\n                        \"y\": 300\n                    },\n                ],\n            }]\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Segmentation Masks ########\n\nmask_annotation = [\n    lb_types.DICOMMaskAnnotation(\n        group_key=\"axial\",\n        frames=[\n            lb_types.MaskFrame(\n                index=1,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n            lb_types.MaskFrame(\n                index=5,\n                instance_uri=\n                \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            ),\n        ],\n        instances=[\n            lb_types.MaskInstance(color_rgb=(255, 255, 255),\n                                  name=\"segmentation_mask_dicom\")\n        ],\n    )\n]\n\nmask_annotation_ndjson = {\n    \"groupKey\": \"axial\",\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\":\n                    1,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n            {\n                \"index\":\n                    5,\n                \"instanceURI\":\n                    \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-mask-1.png\",\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": (255, 255, 255),\n            \"name\": \"segmentation_mask_dicom\"\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"sample-dicom-1.dcm\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/dicom-sample-data/sample-dicom-1.dcm\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"dicom_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors :\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the line annotation above, we provided the `name` as `line_dicom`. Now, when we setup our ontology, we must ensure that the name of my line tool is also `line_dicom`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+        "\n",
+        "\n",
+        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(\n        tool=lb.Tool.Type.RASTER_SEGMENTATION,\n        name=\"segmentation_mask_dicom\",\n    ),\n    lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_dicom\"),\n])\n\nontology = client.create_ontology(\n    \"Ontology DICOM Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Dicom,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project \n",
+        "Connect the ontology to the labeling project."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"dicom_project_demo\",\n                                media_type=lb.MediaType.Dicom)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create batches\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-dicom-demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # a list of data row objects, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above.\n",
+        "\n",
+        "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python Annotation Types"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "annotations_list = polyline_annotation + mask_annotation\nlabels = [\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=annotations_list)\n]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations\n",
+        "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\n\nfor annotation in [polyline_annotation_ndjson, mask_annotation_ndjson]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
+        "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job-\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)\nprint(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/html.ipynb b/examples/annotation_import/html.ipynb
index d4bcc1b9b..7ef731bce 100644
--- a/examples/annotation_import/html.ipynb
+++ b/examples/annotation_import/html.ipynb
@@ -1,308 +1,306 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# HTML Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for HTML assets, and also  cover MAL and Label Import methods:\n",
-                "\n",
-                "Suported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Bouding box\n",
-                "* NER\n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask\n",
-                "\n",
-                "MAL and Label Import:\n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for HTML"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_html\",\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_html\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_html\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_html\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_html\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_html\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample_html_1.html\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_1.html\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"html_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the text annotation, we provided the `name` as `text_html`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_html`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_html\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_html\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_html\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"nested_checklist_question\",\n        options=[\n            lb.Option(\n                \"first_checklist_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.CHECKLIST,\n                        name=\"sub_checklist_question\",\n                        options=[lb.Option(\"first_sub_checklist_answer\")],\n                    )\n                ],\n            )\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"nested_radio_question\",\n        options=[\n            lb.Option(\n                value=\"first_radio_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.RADIO,\n                        name=\"sub_radio_question\",\n                        options=[lb.Option(value=\"first_sub_radio_answer\")],\n                    ),\n                ],\n            )\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology HTML Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox project\nproject = client.create_project(name=\"HTML Import Annotation Demo\",\n                                media_type=lb.MediaType.Html)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-html-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations \n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/html.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# HTML Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for HTML assets, and also  cover MAL and Label Import methods:\n",
+        "\n",
+        "Suported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Bouding box\n",
+        "* NER\n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask\n",
+        "\n",
+        "MAL and Label Import:\n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for HTML"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_html\",\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"text_html\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_html\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_html\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_html\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nradio_annotation_ndjson = {\n    \"name\": \"radio_html\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"sample_html_1.html\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_1.html\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"html_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the text annotation, we provided the `name` as `text_html`. Now, when we setup our ontology, we must ensure that the name of the tool is also `text_html`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(classifications=[\n    lb.Classification(class_type=lb.Classification.Type.TEXT, name=\"text_html\"),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"checklist_html\",\n        options=[\n            lb.Option(value=\"first_checklist_answer\"),\n            lb.Option(value=\"second_checklist_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"radio_html\",\n        options=[\n            lb.Option(value=\"first_radio_answer\"),\n            lb.Option(value=\"second_radio_answer\"),\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"nested_checklist_question\",\n        options=[\n            lb.Option(\n                \"first_checklist_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.CHECKLIST,\n                        name=\"sub_checklist_question\",\n                        options=[lb.Option(\"first_sub_checklist_answer\")],\n                    )\n                ],\n            )\n        ],\n    ),\n    lb.Classification(\n        class_type=lb.Classification.Type.RADIO,\n        name=\"nested_radio_question\",\n        options=[\n            lb.Option(\n                value=\"first_radio_answer\",\n                options=[\n                    lb.Classification(\n                        class_type=lb.Classification.Type.RADIO,\n                        name=\"sub_radio_question\",\n                        options=[lb.Option(value=\"first_sub_radio_answer\")],\n                    ),\n                ],\n            )\n        ],\n    ),\n])\n\nontology = client.create_ontology(\n    \"Ontology HTML Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox project\nproject = client.create_project(name=\"HTML Import Annotation Demo\",\n                                media_type=lb.MediaType.Html)\n\n# Setup your ontology\nproject.setup_editor(\n    ontology)  # Connect your ontology and editor to your project",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-html-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations \n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1rFv-VvHUBbzFYamz6nSMRJz1mEg6Ukqq#scrollTo=3umnTd-MfI0o&line=1&uniqifier=1)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        text_annotation_ndjson,\n        checklist_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload our label using Model-Assisted Labeling\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"mal_job-{str(uuid.uuid4())}\",\n    predictions=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/image.ipynb b/examples/annotation_import/image.ipynb
index aaccd029d..b8c6cd2a6 100644
--- a/examples/annotation_import/image.ipynb
+++ b/examples/annotation_import/image.ipynb
@@ -1,437 +1,435 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Image annotation import\n",
-                "This notebook will provide examples of each supported annotation type for image assets.\n",
-                "\n",
-                "### [Model-assisted labeling (MAL)](https://docs.labelbox.com/docs/model-assisted-labeling)\n",
-                "\n",
-                "* This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.\n",
-                "\n",
-                "The imported annotations will be pre-populated in the labeling editor. However, in order to convert the pre-labels to real annotations, a human labeler will still need to open the Data Row in the Editor and submit it. This functionality is designed to speed up human labeling.\n",
-                "\n",
-                "### [Import ground truth](https://docs.labelbox.com/docs/import-ground-truth)\n",
-                "\n",
-                "* This  workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.\n",
-                "\n",
-                "### Python annotation types vs NDJSON\n",
-                "**Python annotation type (recommended)**\n",
-                "- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.\n",
-                "\n",
-                "- Allows you to build annotations locally with local file paths, numpy arrays, or URLs\n",
-                "\n",
-                "- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox\n",
-                "\n",
-                "- It supports one-level nested classification (free text / radio / checklist) under the object or classification annotation.\n",
-                "\n",
-                "**NDJSON**\n",
-                "- Skip formatting annotation payload in the Python Annotation Types format just to convert back to NDJSON\n",
-                "\n",
-                "- Ability to create the payload in the NDJSON import format directly\n",
-                "\n",
-                "- It supports any levels of nested classification (free text / radio / checklist) under the object or classification annotation."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key\n",
-                "\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for image\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification : Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Relationship with bounding box\n",
-                "> **NOTE:**  \n",
-                "> Only supported for MAL imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2096, y=1264),\n        end=lb_types.Point(x=2240, y=1689),\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2272, y=1346),\n        end=lb_types.Point(x=2416, y=1704),\n    ),\n)\n\nrelationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"uuid\": uuid_source,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1264.0,\n        \"left\": 2096.0,\n        \"height\": 425.0,\n        \"width\": 144.0\n    },\n}\n\nbbox_target_ndjson = {\n    \"uuid\": uuid_target,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1346.0,\n        \"left\": 2272.0,\n        \"height\": 358.0,\n        \"width\": 144.0\n    },\n}\n\nrelationship_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with nested classification"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\")),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\"\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polygon"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",  # must match your ontology feature\"s name\n    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n        points=[\n            lb_types.Point(x=1489.581, y=183.934),\n            lb_types.Point(x=2278.306, y=256.885),\n            lb_types.Point(x=2428.197, y=200.437),\n            lb_types.Point(x=2560.0, y=335.419),\n            lb_types.Point(x=2557.386, y=503.165),\n            lb_types.Point(x=2320.596, y=503.103),\n            lb_types.Point(x=2156.083, y=628.943),\n            lb_types.Point(x=2161.111, y=785.519),\n            lb_types.Point(x=2002.115, y=894.647),\n            lb_types.Point(x=1838.456, y=877.874),\n            lb_types.Point(x=1436.53, y=874.636),\n            lb_types.Point(x=1411.403, y=758.579),\n            lb_types.Point(x=1353.853, y=751.74),\n            lb_types.Point(x=1345.264, y=453.461),\n            lb_types.Point(x=1426.011, y=421.129),\n        ]),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Composite mask upload using different mask tools from the project's ontology\n",
-                "This example shows how to assigned different annotations (mask instances) from a composite mask using different mask tools"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First we need to extract all the unique colors from the composite mask\ndef extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "cp_mask_url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/composite_mask.png\"\ncolors = extract_rgb_colors_from_url(cp_mask_url)\nresponse = requests.get(cp_mask_url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nrgb_colors_for_mask_with_text_subclass_tool = [\n    (73, 39, 85),\n    (111, 87, 176),\n    (23, 169, 254),\n]\n\ncp_mask = []\nfor color in colors:\n    # We are assigning the color related to the mask_with_text_subclass tool by identifying the unique RGB colors\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask.append(\n            lb_types.ObjectAnnotation(\n                name=\n                \"mask_with_text_subclass\",  # must match your ontology feature\"s name\n                value=lb_types.Mask(mask=mask_data, color=color),\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_free_text\",\n                        value=lb_types.Text(answer=\"free text answer sample\"),\n                    )\n                ],\n            ))\n    else:\n        # Create ObjectAnnotation for other masks\n        cp_mask.append(\n            lb_types.ObjectAnnotation(name=\"mask\",\n                                      value=lb_types.Mask(mask=mask_data,\n                                                          color=color)))\n\n# NDJSON using bytes array\ncp_mask_ndjson = []\n\n# Using bytes array.\nresponse = requests.get(cp_mask_url)\nim_bytes = base64.b64encode(response.content).decode(\"utf-8\")\nfor color in colors:\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask_ndjson.append({\n            \"name\":\n                \"mask_with_text_subclass\",\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n            \"classifications\": [{\n                \"name\": \"sub_free_text\",\n                \"answer\": \"free text answer\"\n            }],\n        })\n    else:\n        cp_mask_ndjson.append({\n            \"name\": \"mask\",\n            \"classifications\": [],\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n        })",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",  # must match your ontology feature\"s name\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point\",\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",  # must match your ontology feature\"s name\n    value=lb_types.Line(  # Coordinates for the keypoints in your polyline\n        points=[\n            lb_types.Point(x=2534.353, y=249.471),\n            lb_types.Point(x=2429.492, y=182.092),\n            lb_types.Point(x=2294.322, y=221.962),\n            lb_types.Point(x=2224.491, y=180.463),\n            lb_types.Point(x=2136.123, y=204.716),\n            lb_types.Point(x=1712.247, y=173.949),\n            lb_types.Point(x=1703.838, y=84.438),\n            lb_types.Point(x=1579.772, y=82.61),\n            lb_types.Point(x=1583.442, y=167.552),\n            lb_types.Point(x=1478.869, y=164.903),\n            lb_types.Point(x=1418.941, y=318.149),\n            lb_types.Point(x=1243.128, y=400.815),\n            lb_types.Point(x=1022.067, y=319.007),\n            lb_types.Point(x=892.367, y=379.216),\n            lb_types.Point(x=670.273, y=364.408),\n            lb_types.Point(x=613.114, y=288.16),\n            lb_types.Point(x=377.559, y=238.251),\n            lb_types.Point(x=368.087, y=185.064),\n            lb_types.Point(x=246.557, y=167.286),\n            lb_types.Point(x=236.648, y=285.61),\n            lb_types.Point(x=90.929, y=326.412),\n        ]),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# End-to-end example: Import pre-labels or ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into catalog\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"image-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(dataset)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we created the bounding box annotation above, we provided the `name` as `bounding_box`. Now, when we setup our ontology, we must ensure that the name of the bounding box tool is also `bounding_box`. The same alignment must hold true for the other tools and classifications we create in our ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"Image Annotation Import Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send a batch of data rows to the project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    \"image-demo-batch\",  # each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the annotations payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Python annotations\n",
-                "\n",
-                "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n    bbox_source,\n    bbox_target,\n    relationship,\n] + cp_mask\n\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations\n",
-                "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nannotations = [\n    radio_annotation_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    checklist_annotation_ndjson,\n    text_annotation_ndjson,\n    bbox_annotation_ndjson,\n    bbox_with_radio_subclass_ndjson,\n    polygon_annotation_ndjson,\n    point_annotation_ndjson,\n    polyline_annotation_ndjson,\n    bbox_source_ndjson,\n    bbox_target_ndjson,\n    relationship_ndjson,  ## Only supported for MAL imports\n] + cp_mask_ndjson\n\nfor annotation in annotations:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload annotations to a project as pre-labels or ground truth\n",
-                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option A: Upload to a labeling project as pre-labels (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# upload MAL labels for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=label,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option B: Upload to a labeling project using ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Relationships are not supported with LabelImport\n# For this demo either run MAL or Ground Truth, not both\n\n# Upload label for this data row in project\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=label)\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Image annotation import\n",
+        "This notebook will provide examples of each supported annotation type for image assets.\n",
+        "\n",
+        "### [Model-assisted labeling (MAL)](https://docs.labelbox.com/docs/model-assisted-labeling)\n",
+        "\n",
+        "* This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.\n",
+        "\n",
+        "The imported annotations will be pre-populated in the labeling editor. However, in order to convert the pre-labels to real annotations, a human labeler will still need to open the Data Row in the Editor and submit it. This functionality is designed to speed up human labeling.\n",
+        "\n",
+        "### [Import ground truth](https://docs.labelbox.com/docs/import-ground-truth)\n",
+        "\n",
+        "* This  workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.\n",
+        "\n",
+        "### Python annotation types vs NDJSON\n",
+        "**Python annotation type (recommended)**\n",
+        "- Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.\n",
+        "\n",
+        "- Allows you to build annotations locally with local file paths, numpy arrays, or URLs\n",
+        "\n",
+        "- Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox\n",
+        "\n",
+        "- It supports one-level nested classification (free text / radio / checklist) under the object or classification annotation.\n",
+        "\n",
+        "**NDJSON**\n",
+        "- Skip formatting annotation payload in the Python Annotation Types format just to convert back to NDJSON\n",
+        "\n",
+        "- Ability to create the payload in the NDJSON import format directly\n",
+        "\n",
+        "- It supports any levels of nested classification (free text / radio / checklist) under the object or classification annotation."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key\n",
+        "\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for image\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification : Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Relationship with bounding box\n",
+        "> **NOTE:**  \n",
+        "> Only supported for MAL imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2096, y=1264),\n        end=lb_types.Point(x=2240, y=1689),\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=2272, y=1346),\n        end=lb_types.Point(x=2416, y=1704),\n    ),\n)\n\nrelationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"uuid\": uuid_source,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1264.0,\n        \"left\": 2096.0,\n        \"height\": 425.0,\n        \"width\": 144.0\n    },\n}\n\nbbox_target_ndjson = {\n    \"uuid\": uuid_target,\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 1346.0,\n        \"left\": 2272.0,\n        \"height\": 358.0,\n        \"width\": 144.0\n    },\n}\n\nrelationship_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with nested classification"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\")),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\"\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polygon"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",  # must match your ontology feature\"s name\n    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n        points=[\n            lb_types.Point(x=1489.581, y=183.934),\n            lb_types.Point(x=2278.306, y=256.885),\n            lb_types.Point(x=2428.197, y=200.437),\n            lb_types.Point(x=2560.0, y=335.419),\n            lb_types.Point(x=2557.386, y=503.165),\n            lb_types.Point(x=2320.596, y=503.103),\n            lb_types.Point(x=2156.083, y=628.943),\n            lb_types.Point(x=2161.111, y=785.519),\n            lb_types.Point(x=2002.115, y=894.647),\n            lb_types.Point(x=1838.456, y=877.874),\n            lb_types.Point(x=1436.53, y=874.636),\n            lb_types.Point(x=1411.403, y=758.579),\n            lb_types.Point(x=1353.853, y=751.74),\n            lb_types.Point(x=1345.264, y=453.461),\n            lb_types.Point(x=1426.011, y=421.129),\n        ]),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Composite mask upload using different mask tools from the project's ontology\n",
+        "This example shows how to assigned different annotations (mask instances) from a composite mask using different mask tools"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First we need to extract all the unique colors from the composite mask\ndef extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "cp_mask_url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/composite_mask.png\"\ncolors = extract_rgb_colors_from_url(cp_mask_url)\nresponse = requests.get(cp_mask_url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nrgb_colors_for_mask_with_text_subclass_tool = [\n    (73, 39, 85),\n    (111, 87, 176),\n    (23, 169, 254),\n]\n\ncp_mask = []\nfor color in colors:\n    # We are assigning the color related to the mask_with_text_subclass tool by identifying the unique RGB colors\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask.append(\n            lb_types.ObjectAnnotation(\n                name=\n                \"mask_with_text_subclass\",  # must match your ontology feature\"s name\n                value=lb_types.Mask(mask=mask_data, color=color),\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_free_text\",\n                        value=lb_types.Text(answer=\"free text answer sample\"),\n                    )\n                ],\n            ))\n    else:\n        # Create ObjectAnnotation for other masks\n        cp_mask.append(\n            lb_types.ObjectAnnotation(name=\"mask\",\n                                      value=lb_types.Mask(mask=mask_data,\n                                                          color=color)))\n\n# NDJSON using bytes array\ncp_mask_ndjson = []\n\n# Using bytes array.\nresponse = requests.get(cp_mask_url)\nim_bytes = base64.b64encode(response.content).decode(\"utf-8\")\nfor color in colors:\n    if color in rgb_colors_for_mask_with_text_subclass_tool:\n        cp_mask_ndjson.append({\n            \"name\":\n                \"mask_with_text_subclass\",\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n            \"classifications\": [{\n                \"name\": \"sub_free_text\",\n                \"answer\": \"free text answer\"\n            }],\n        })\n    else:\n        cp_mask_ndjson.append({\n            \"name\": \"mask\",\n            \"classifications\": [],\n            \"mask\": {\n                \"imBytes\": im_bytes,\n                \"colorRGB\": color\n            },\n        })",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",  # must match your ontology feature\"s name\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point\",\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",  # must match your ontology feature\"s name\n    value=lb_types.Line(  # Coordinates for the keypoints in your polyline\n        points=[\n            lb_types.Point(x=2534.353, y=249.471),\n            lb_types.Point(x=2429.492, y=182.092),\n            lb_types.Point(x=2294.322, y=221.962),\n            lb_types.Point(x=2224.491, y=180.463),\n            lb_types.Point(x=2136.123, y=204.716),\n            lb_types.Point(x=1712.247, y=173.949),\n            lb_types.Point(x=1703.838, y=84.438),\n            lb_types.Point(x=1579.772, y=82.61),\n            lb_types.Point(x=1583.442, y=167.552),\n            lb_types.Point(x=1478.869, y=164.903),\n            lb_types.Point(x=1418.941, y=318.149),\n            lb_types.Point(x=1243.128, y=400.815),\n            lb_types.Point(x=1022.067, y=319.007),\n            lb_types.Point(x=892.367, y=379.216),\n            lb_types.Point(x=670.273, y=364.408),\n            lb_types.Point(x=613.114, y=288.16),\n            lb_types.Point(x=377.559, y=238.251),\n            lb_types.Point(x=368.087, y=185.064),\n            lb_types.Point(x=246.557, y=167.286),\n            lb_types.Point(x=236.648, y=285.61),\n            lb_types.Point(x=90.929, y=326.412),\n        ]),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# End-to-end example: Import pre-labels or ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into catalog\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"image-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(dataset)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we created the bounding box annotation above, we provided the `name` as `bounding_box`. Now, when we setup our ontology, we must ensure that the name of the bounding box tool is also `bounding_box`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"Image Annotation Import Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send a batch of data rows to the project\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch = project.create_batch(\n    \"image-demo-batch\",  # each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the annotations payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Python annotations\n",
+        "\n",
+        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n    bbox_source,\n    bbox_target,\n    relationship,\n] + cp_mask\n\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations\n",
+        "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nannotations = [\n    radio_annotation_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    checklist_annotation_ndjson,\n    text_annotation_ndjson,\n    bbox_annotation_ndjson,\n    bbox_with_radio_subclass_ndjson,\n    polygon_annotation_ndjson,\n    point_annotation_ndjson,\n    polyline_annotation_ndjson,\n    bbox_source_ndjson,\n    bbox_target_ndjson,\n    relationship_ndjson,  ## Only supported for MAL imports\n] + cp_mask_ndjson\n\nfor annotation in annotations:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload annotations to a project as pre-labels or ground truth\n",
+        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option A: Upload to a labeling project as pre-labels (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# upload MAL labels for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=label,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option B: Upload to a labeling project using ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Relationships are not supported with LabelImport\n# For this demo either run MAL or Ground Truth, not both\n\n# Upload label for this data row in project\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=label)\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/import_labeled_dataset_image.ipynb b/examples/annotation_import/import_labeled_dataset_image.ipynb
index e049f2da4..9ebd52883 100644
--- a/examples/annotation_import/import_labeled_dataset_image.ipynb
+++ b/examples/annotation_import/import_labeled_dataset_image.ipynb
@@ -1,187 +1,185 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.data_row_metadata import (\n    DataRowMetadataField,\n    DataRowMetadataKind,\n)\nimport datetime\nimport random\nimport os\nimport json\nfrom PIL import Image\nfrom labelbox.schema.ontology import OntologyBuilder, Tool\nimport requests\nfrom tqdm.notebook import tqdm\nimport uuid\nfrom labelbox.data.annotation_types import (\n    Label,\n    ImageData,\n    ObjectAnnotation,\n    Rectangle,\n    Point,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup Labelbox client"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Initialize the Labelbox client\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Download a public dataset\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Function to download files\ndef download_files(filemap):\n    path, uri = filemap\n    if not os.path.exists(path):\n        response = requests.get(uri, stream=True)\n        with open(path, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n    return path",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Download data rows and annotations\nDATA_ROWS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json\"\nANNOTATIONS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json\"\ndownload_files((\"data_rows.json\", DATA_ROWS_URL))\ndownload_files((\"annotations.json\", ANNOTATIONS_URL))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Load data rows and annotations\nwith open(\"data_rows.json\") as fp:\n    data_rows = json.load(fp)\nwith open(\"annotations.json\") as fp:\n    annotations = json.load(fp)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Create a dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new dataset\ndataset = client.create_dataset(name=\"Geospatial vessel detection\")\nprint(f\"Created dataset with ID: {dataset.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Import Data Rows with Metadata"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Here is an example of adding two metadata fields to your Data Rows: a \"captureDateTime\" field with datetime value, and a \"tag\" field with string value\nmetadata_ontology = client.get_data_row_metadata_ontology()\ndatetime_schema_id = metadata_ontology.reserved_by_name[\"captureDateTime\"].uid\ntag_schema_id = metadata_ontology.reserved_by_name[\"tag\"].uid\ntag_items = [\"WorldView-1\", \"WorldView-2\", \"WorldView-3\", \"WorldView-4\"]\n\nfor datarow in tqdm(data_rows):\n    dt = datetime.datetime.utcnow() + datetime.timedelta(\n        days=random.random() * 30)  # this is random datetime value\n    tag_item = random.choice(tag_items)  # this is a random tag value\n\n    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.\n    metadata_fields = [\n        DataRowMetadataField(schema_id=datetime_schema_id, value=dt),\n        DataRowMetadataField(schema_id=tag_schema_id, value=tag_item),\n    ]\n\n    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.\n    # metadata_fields = [\n    #                    {\"schema_id\": datetime_schema_id, \"value\": dt},\n    #                    {\"schema_id\": tag_schema_id, \"value\": tag_item}\n    #                    ]\n\n    datarow[\"metadata_fields\"] = metadata_fields",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "task = dataset.create_data_rows(data_rows)\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Examine a Data Row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "datarow = next(dataset.data_rows())\nprint(datarow)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup a labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Initialize the OntologyBuilder\nontology_builder = OntologyBuilder()\n\n# Assuming 'annotations' is defined and contains the necessary data\nfor category in annotations[\"categories\"]:\n    print(category[\"name\"])\n    # Add tools to the ontology builder\n    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category[\"name\"]))\n\n# Create the ontology in Labelbox\nontology = client.create_ontology(\n    \"Vessel Detection Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\nprint(f\"Created ontology with ID: {ontology.uid}\")\n\n# Create a project and set up the ontology\nproject = client.create_project(name=\"Vessel Detection\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology=ontology)\nprint(f\"Created project with ID: {project.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\n# Minimal ExportV2 parameters focused solely on data row IDs\nexport_params = {\"data_row_details\": True}  # Only export data row details\n\n# Initiate the streamable export task from catalog\ndataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID\nexport_task = dataset.export(params=export_params)\nexport_task.wait_till_done()\nprint(export_task)\n\ndata_rows = []\n\n\n# Callback used for JSON Converter to correctly collect data row IDs\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    # Parse the JSON string to access the data\n    data = json.loads(output.json_str)\n\n    # Correctly extract and append DataRow ID\n    if \"data_row\" in data and \"id\" in data[\"data_row\"]:\n        data_rows.append(data[\"data_row\"][\"id\"])\n\n\n# Process the stream if there are results\nif export_task.has_result():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.RESULT).start(\n                               stream_handler=json_stream_handler)\n\n# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)\nsampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))\n\n# Create a new batch in the project and add the sampled data rows\nbatch = project.create_batch(\n    \"Initial batch\",  # name of the batch\n    sampled_data_rows,  # list of Data Rows\n    1,  # priority between 1-5\n)\nprint(f\"Created batch with ID: {batch.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Create annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set export parameters focused on data row details\nexport_params = {\n    \"data_row_details\": True,  # Only export data row details\n    \"batch_ids\": [batch.uid\n                 ],  # Optional: Include batch ids to filter by specific batches\n}\n\n# Initialize the streamable export task from project\nexport_task = project.export(params=export_params)\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nlabels = []\nfor datarow in data_rows:\n    annotations_list = []\n    # Access the 'data_row' dictionary first\n    data_row_dict = datarow[\"data_row\"]\n    folder = data_row_dict[\"external_id\"].split(\"/\")[0]\n    id = data_row_dict[\"external_id\"].split(\"/\")[1]\n\n    if folder == \"positive_image_set\":\n        for image in annotations[\"images\"]:\n            if image[\"file_name\"] == id:\n                for annotation in annotations[\"annotations\"]:\n                    if annotation[\"image_id\"] == image[\"id\"]:\n                        bbox = annotation[\"bbox\"]\n                        category_id = annotation[\"category_id\"] - 1\n                        class_name = None\n                        ontology = (ontology_builder.asdict()\n                                   )  # Get the ontology dictionary\n                        for category in ontology[\"tools\"]:\n                            if (category[\"name\"] == annotations[\"categories\"]\n                                [category_id][\"name\"]):\n                                class_name = category[\"name\"]\n                                break\n                        if class_name:\n                            annotations_list.append(\n                                ObjectAnnotation(\n                                    name=class_name,\n                                    value=Rectangle(\n                                        start=Point(x=bbox[0], y=bbox[1]),\n                                        end=Point(\n                                            x=bbox[2] + bbox[0],\n                                            y=bbox[3] + bbox[1],\n                                        ),\n                                    ),\n                                ))\n    image_data = {\"uid\": data_row_dict[\"id\"]}\n    labels.append(Label(data=image_data, annotations=annotations_list))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"label_import_job_{str(uuid.uuid4())}\",\n    labels=labels,\n)\n\n# Wait for the upload to finish and print the results\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/import_labeled_dataset_image.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.data_row_metadata import (\n    DataRowMetadataField,\n    DataRowMetadataKind,\n)\nimport datetime\nimport random\nimport os\nimport json\nfrom PIL import Image\nfrom labelbox.schema.ontology import OntologyBuilder, Tool\nimport requests\nfrom tqdm.notebook import tqdm\nimport uuid\nfrom labelbox.data.annotation_types import (\n    Label,\n    ImageData,\n    ObjectAnnotation,\n    Rectangle,\n    Point,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup Labelbox client"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Initialize the Labelbox client\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Download a public dataset\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Function to download files\ndef download_files(filemap):\n    path, uri = filemap\n    if not os.path.exists(path):\n        response = requests.get(uri, stream=True)\n        with open(path, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n    return path",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Download data rows and annotations\nDATA_ROWS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_datarows.json\"\nANNOTATIONS_URL = \"https://storage.googleapis.com/labelbox-datasets/VHR_geospatial/geospatial_annotations.json\"\ndownload_files((\"data_rows.json\", DATA_ROWS_URL))\ndownload_files((\"annotations.json\", ANNOTATIONS_URL))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Load data rows and annotations\nwith open(\"data_rows.json\") as fp:\n    data_rows = json.load(fp)\nwith open(\"annotations.json\") as fp:\n    annotations = json.load(fp)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create a dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new dataset\ndataset = client.create_dataset(name=\"Geospatial vessel detection\")\nprint(f\"Created dataset with ID: {dataset.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Import Data Rows with Metadata"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Here is an example of adding two metadata fields to your Data Rows: a \"captureDateTime\" field with datetime value, and a \"tag\" field with string value\nmetadata_ontology = client.get_data_row_metadata_ontology()\ndatetime_schema_id = metadata_ontology.reserved_by_name[\"captureDateTime\"].uid\ntag_schema_id = metadata_ontology.reserved_by_name[\"tag\"].uid\ntag_items = [\"WorldView-1\", \"WorldView-2\", \"WorldView-3\", \"WorldView-4\"]\n\nfor datarow in tqdm(data_rows):\n    dt = datetime.datetime.utcnow() + datetime.timedelta(\n        days=random.random() * 30)  # this is random datetime value\n    tag_item = random.choice(tag_items)  # this is a random tag value\n\n    # Option 1: Specify metadata with a list of DataRowMetadataField. This is the recommended option since it comes with validation for metadata fields.\n    metadata_fields = [\n        DataRowMetadataField(schema_id=datetime_schema_id, value=dt),\n        DataRowMetadataField(schema_id=tag_schema_id, value=tag_item),\n    ]\n\n    # Option 2: Uncomment to try. Alternatively, you can specify the metadata fields with dictionary format without declaring the DataRowMetadataField objects. It is equivalent to Option 1.\n    # metadata_fields = [\n    #                    {\"schema_id\": datetime_schema_id, \"value\": dt},\n    #                    {\"schema_id\": tag_schema_id, \"value\": tag_item}\n    #                    ]\n\n    datarow[\"metadata_fields\"] = metadata_fields",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "task = dataset.create_data_rows(data_rows)\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Examine a Data Row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "datarow = next(dataset.data_rows())\nprint(datarow)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup a labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Initialize the OntologyBuilder\nontology_builder = OntologyBuilder()\n\n# Assuming 'annotations' is defined and contains the necessary data\nfor category in annotations[\"categories\"]:\n    print(category[\"name\"])\n    # Add tools to the ontology builder\n    ontology_builder.add_tool(Tool(tool=Tool.Type.BBOX, name=category[\"name\"]))\n\n# Create the ontology in Labelbox\nontology = client.create_ontology(\n    \"Vessel Detection Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\nprint(f\"Created ontology with ID: {ontology.uid}\")\n\n# Create a project and set up the ontology\nproject = client.create_project(name=\"Vessel Detection\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology=ontology)\nprint(f\"Created project with ID: {project.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\n# Minimal ExportV2 parameters focused solely on data row IDs\nexport_params = {\"data_row_details\": True}  # Only export data row details\n\n# Initiate the streamable export task from catalog\ndataset = client.get_dataset(dataset.uid)  # Update with the actual dataset ID\nexport_task = dataset.export(params=export_params)\nexport_task.wait_till_done()\nprint(export_task)\n\ndata_rows = []\n\n\n# Callback used for JSON Converter to correctly collect data row IDs\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    # Parse the JSON string to access the data\n    data = json.loads(output.json_str)\n\n    # Correctly extract and append DataRow ID\n    if \"data_row\" in data and \"id\" in data[\"data_row\"]:\n        data_rows.append(data[\"data_row\"][\"id\"])\n\n\n# Process the stream if there are results\nif export_task.has_result():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.RESULT).start(\n                               stream_handler=json_stream_handler)\n\n# Randomly select 200 Data Rows (or fewer if the dataset has less than 200 data rows)\nsampled_data_rows = random.sample(data_rows, min(len(data_rows), 200))\n\n# Create a new batch in the project and add the sampled data rows\nbatch = project.create_batch(\n    \"Initial batch\",  # name of the batch\n    sampled_data_rows,  # list of Data Rows\n    1,  # priority between 1-5\n)\nprint(f\"Created batch with ID: {batch.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set export parameters focused on data row details\nexport_params = {\n    \"data_row_details\": True,  # Only export data row details\n    \"batch_ids\": [batch.uid\n                 ],  # Optional: Include batch ids to filter by specific batches\n}\n\n# Initialize the streamable export task from project\nexport_task = project.export(params=export_params)\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nlabels = []\nfor datarow in data_rows:\n    annotations_list = []\n    # Access the 'data_row' dictionary first\n    data_row_dict = datarow[\"data_row\"]\n    folder = data_row_dict[\"external_id\"].split(\"/\")[0]\n    id = data_row_dict[\"external_id\"].split(\"/\")[1]\n\n    if folder == \"positive_image_set\":\n        for image in annotations[\"images\"]:\n            if image[\"file_name\"] == id:\n                for annotation in annotations[\"annotations\"]:\n                    if annotation[\"image_id\"] == image[\"id\"]:\n                        bbox = annotation[\"bbox\"]\n                        category_id = annotation[\"category_id\"] - 1\n                        class_name = None\n                        ontology = (ontology_builder.asdict()\n                                   )  # Get the ontology dictionary\n                        for category in ontology[\"tools\"]:\n                            if (category[\"name\"] == annotations[\"categories\"]\n                                [category_id][\"name\"]):\n                                class_name = category[\"name\"]\n                                break\n                        if class_name:\n                            annotations_list.append(\n                                ObjectAnnotation(\n                                    name=class_name,\n                                    value=Rectangle(\n                                        start=Point(x=bbox[0], y=bbox[1]),\n                                        end=Point(\n                                            x=bbox[2] + bbox[0],\n                                            y=bbox[3] + bbox[1],\n                                        ),\n                                    ),\n                                ))\n    image_data = {\"uid\": data_row_dict[\"id\"]}\n    labels.append(Label(data=image_data, annotations=annotations_list))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=f\"label_import_job_{str(uuid.uuid4())}\",\n    labels=labels,\n)\n\n# Wait for the upload to finish and print the results\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/pdf.ipynb b/examples/annotation_import/pdf.ipynb
index efd00aff2..5c0742ba5 100644
--- a/examples/annotation_import/pdf.ipynb
+++ b/examples/annotation_import/pdf.ipynb
@@ -1,387 +1,385 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 1,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# PDF Annotation Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "\n",
-                "Supported annotations for PDF assets \n",
-                "\n",
-                "*Annotation types*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box\n",
-                "- Entities\n",
-                "- Relationships (only supported for MAL imports)\n",
-                "\n",
-                "\n",
-                "*NDJson*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box \n",
-                "- Entities \n",
-                "- Relationships (only supported for MAL imports)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key\n",
-                "Guides on https://docs.labelbox.com/docs/create-an-api-key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Supported Annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entity ##########\n\n# Annotation Types\nentities_annotations = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_annotations_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification #########\n\n# Annotation types\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Bounding Box ###########\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=102.771, y=135.3),  # x = left, y = top\n        end=lb_types.Point(x=518.571,\n                           y=245.143),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 135.3,\n        \"left\": 102.771,\n        \"height\": 109.843,\n        \"width\": 415.8\n    },\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############## Classification Free-form text ##############\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\"name\": \"free_text\", \"answer\": \"sample text\"}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### BBOX with nested classifications #########\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=317.271, y=226.757),  # x = left, y = top\n        end=lb_types.Point(x=566.657,\n                           y=420.986),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_annotation_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\"\n                },\n            }],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 226.757,\n        \"left\": 317.271,\n        \"height\": 194.229,\n        \"width\": 249.386,\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\"\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### Relationships ##########\nentity_source = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_target = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nentity_source_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_source,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\n\nentity_target_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_target,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\nner_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### BBOX with relationships #############\n# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=188.257, y=68.875),  # x = left, y = top\n        end=lb_types.Point(x=270.907,\n                           y=149.556),  # x = left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=96.424, y=66.251),\n        end=lb_types.Point(x=179.074, y=146.932),\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source_2 = str(uuid.uuid4())\nuuid_target_2 = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_source_2,\n    \"bbox\": {\n        \"top\": 68.875,\n        \"left\": 188.257,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_target_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_target_2,\n    \"bbox\": {\n        \"top\": 66.251,\n        \"left\": 96.424,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source_2,\n        \"target\": uuid_target_2,\n        \"type\": \"unidirectional\",\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
-                "\n",
-                "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
-                "- The document must have no more than 15 pages.\n",
-                "- The file size should not exceed 20 MB.\n",
-                "\n",
-                "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
-                "\n",
-                "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
-                "\n",
-                "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"0801.3483_doc.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an Ontology for your project\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Creating a labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"PDF_annotation_demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"PDF_annotation_batch\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5. Create the annotation payload\n",
-                "Create the annotations payload using the snippets of code in Supported predictions section.\n",
-                "\n",
-                "Labelbox support NDJSON only for this data type.\n",
-                "\n",
-                "The resulting label should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Step 5.1: First, we need to populate the text selections for Entity annotations\n",
-                "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "To extract the generated text layer url we first need to export the data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n    \"Organic charge transfer salts based on the donor\",\n    \"the experimental investigations on this issue have not\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\ntext_selections_source = []\ntext_selections_target = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_annotations_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_annotation_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[2]:\n            relationship_source = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_source = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=relationship_source, page=1)\n            text_selections_source.append(text_selection_entity_source)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entity_source_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_source,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[3]:\n            relationship_target = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_target = lb_types.DocumentTextSelection(\n                group_id=group[\"id\"], tokenIds=relationship_target, page=1)\n            text_selections_target.append(text_selection_entity_target)\n            # build text selections forthe NDJson annotations\n            update_text_selections(\n                annotation=entity_target_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_target,  # ids representing individual words from the group\n                page=1,\n            )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Re-write the python annotations to include text selections (only required for python annotation types)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# re-write the entity annotation with text selections\nentities_annotation_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", textSelections=text_selections)\nentities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_annotation_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\", textSelections=text_selections_ner)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# re-write the entity source and target annotations withe text selectios\nentity_source_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_source)\nentity_source = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_source_doc)\n\nentity_target_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_target)\nentity_target = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_target_doc)\n\n# re-write the entity relationship with the re-created entities\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\nprint(f\"entities_annotation={entities_annotation}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_annotation_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")\nprint(f\"entity_source_ndjson={entity_source_ndjson}\")\nprint(f\"entity_target_ndjson={entity_target_ndjson}\")\nprint(f\"entity_source={entity_source}\")\nprint(f\"entity_target={entity_target}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotation\n",
-                "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n            entity_source,\n            entity_target,\n            entity_relationship,  # Only supported for MAL imports\n            bbox_source,\n            bbox_target,\n            bbox_relationship,  # Only supported for MAL imports\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJson annotations\n",
-                "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annot in [\n        entities_annotations_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        bbox_annotation_ndjson,\n        bbox_with_radio_subclass_annotation_ndjson,\n        ner_with_checklist_subclass_annotation_ndjson,\n        entity_source_ndjson,\n        entity_target_ndjson,\n        ner_relationship_annotation_ndjson,  # Only supported for MAL imports\n        bbox_source_ndjson,\n        bbox_target_ndjson,\n        bbox_relationship_annotation_ndjson,  # Only supported for MAL imports\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Import the annotation payload\n",
-                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option A: Upload to a labeling project as pre-labels (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option B: Upload to a labeling project using ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Uncomment this code when excluding relationships from label import\n## Relationships are not currently supported for label import\n\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=labels) ## Remove unsupported relationships from the labels list\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 1,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/pdf.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# PDF Annotation Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "\n",
+        "Supported annotations for PDF assets \n",
+        "\n",
+        "*Annotation types*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box\n",
+        "- Entities\n",
+        "- Relationships (only supported for MAL imports)\n",
+        "\n",
+        "\n",
+        "*NDJson*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box \n",
+        "- Entities \n",
+        "- Relationships (only supported for MAL imports)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key\n",
+        "Guides on https://docs.labelbox.com/docs/create-an-api-key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Supported Annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entity ##########\n\n# Annotation Types\nentities_annotations = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_annotations_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification #########\n\n# Annotation types\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Bounding Box ###########\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=102.771, y=135.3),  # x = left, y = top\n        end=lb_types.Point(x=518.571,\n                           y=245.143),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_annotation_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": {\n        \"top\": 135.3,\n        \"left\": 102.771,\n        \"height\": 109.843,\n        \"width\": 415.8\n    },\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############## Classification Free-form text ##############\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\"),\n)\n\ntext_annotation_ndjson = {\"name\": \"free_text\", \"answer\": \"sample text\"}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### BBOX with nested classifications #########\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=317.271, y=226.757),  # x = left, y = top\n        end=lb_types.Point(x=566.657,\n                           y=420.986),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_annotation_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\"\n                },\n            }],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 226.757,\n        \"left\": 317.271,\n        \"height\": 194.229,\n        \"width\": 249.386,\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\"\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### Relationships ##########\nentity_source = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_target = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source = str(uuid.uuid4())\nuuid_target = str(uuid.uuid4())\n\nentity_source_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_source,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\n\nentity_target_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"uuid\":\n        uuid_target,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}\nner_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source,\n        \"target\": uuid_target,\n        \"type\": \"unidirectional\",\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### BBOX with relationships #############\n# Python Annotation\nbbox_source = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=188.257, y=68.875),  # x = left, y = top\n        end=lb_types.Point(x=270.907,\n                           y=149.556),  # x = left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_target = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=96.424, y=66.251),\n        end=lb_types.Point(x=179.074, y=146.932),\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n)\n\nbbox_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=bbox_source,\n        target=bbox_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)\n\n## Only supported for MAL imports\nuuid_source_2 = str(uuid.uuid4())\nuuid_target_2 = str(uuid.uuid4())\n\nbbox_source_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_source_2,\n    \"bbox\": {\n        \"top\": 68.875,\n        \"left\": 188.257,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_target_ndjson = {\n    \"name\": \"bounding_box\",\n    \"uuid\": uuid_target_2,\n    \"bbox\": {\n        \"top\": 66.251,\n        \"left\": 96.424,\n        \"height\": 80.681,\n        \"width\": 82.65\n    },\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}\n\nbbox_relationship_annotation_ndjson = {\n    \"name\": \"relationship\",\n    \"relationship\": {\n        \"source\": uuid_source_2,\n        \"target\": uuid_target_2,\n        \"type\": \"unidirectional\",\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
+        "\n",
+        "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
+        "- The document must have no more than 15 pages.\n",
+        "- The file size should not exceed 20 MB.\n",
+        "\n",
+        "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
+        "\n",
+        "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
+        "\n",
+        "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"0801.3483_doc.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an Ontology for your project\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Creating a labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"PDF_annotation_demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"PDF_annotation_batch\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5. Create the annotation payload\n",
+        "Create the annotations payload using the snippets of code in Supported predictions section.\n",
+        "\n",
+        "Labelbox support NDJSON only for this data type.\n",
+        "\n",
+        "The resulting label should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Step 5.1: First, we need to populate the text selections for Entity annotations\n",
+        "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "To extract the generated text layer url we first need to export the data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n    \"Organic charge transfer salts based on the donor\",\n    \"the experimental investigations on this issue have not\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\ntext_selections_source = []\ntext_selections_target = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_annotations_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_annotation_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[2]:\n            relationship_source = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_source = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=relationship_source, page=1)\n            text_selections_source.append(text_selection_entity_source)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entity_source_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_source,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[3]:\n            relationship_target = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            text_selection_entity_target = lb_types.DocumentTextSelection(\n                group_id=group[\"id\"], tokenIds=relationship_target, page=1)\n            text_selections_target.append(text_selection_entity_target)\n            # build text selections forthe NDJson annotations\n            update_text_selections(\n                annotation=entity_target_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                relationship_target,  # ids representing individual words from the group\n                page=1,\n            )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Re-write the python annotations to include text selections (only required for python annotation types)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# re-write the entity annotation with text selections\nentities_annotation_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", textSelections=text_selections)\nentities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_annotation_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\", textSelections=text_selections_ner)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# re-write the entity source and target annotations withe text selectios\nentity_source_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_source)\nentity_source = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_source_doc)\n\nentity_target_doc = lb_types.DocumentEntity(\n    name=\"named_entity\", text_selections=text_selections_target)\nentity_target = lb_types.ObjectAnnotation(name=\"named_entity\",\n                                          value=entity_target_doc)\n\n# re-write the entity relationship with the re-created entities\nentity_relationship = lb_types.RelationshipAnnotation(\n    name=\"relationship\",\n    value=lb_types.Relationship(\n        source=entity_source,\n        target=entity_target,\n        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n    ),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\nprint(f\"entities_annotation={entities_annotation}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_annotation_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")\nprint(f\"entity_source_ndjson={entity_source_ndjson}\")\nprint(f\"entity_target_ndjson={entity_target_ndjson}\")\nprint(f\"entity_source={entity_source}\")\nprint(f\"entity_target={entity_target}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotation\n",
+        "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n            entity_source,\n            entity_target,\n            entity_relationship,  # Only supported for MAL imports\n            bbox_source,\n            bbox_target,\n            bbox_relationship,  # Only supported for MAL imports\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJson annotations\n",
+        "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annot in [\n        entities_annotations_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        radio_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        bbox_annotation_ndjson,\n        bbox_with_radio_subclass_annotation_ndjson,\n        ner_with_checklist_subclass_annotation_ndjson,\n        entity_source_ndjson,\n        entity_target_ndjson,\n        ner_relationship_annotation_ndjson,  # Only supported for MAL imports\n        bbox_source_ndjson,\n        bbox_target_ndjson,\n        bbox_relationship_annotation_ndjson,  # Only supported for MAL imports\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Import the annotation payload\n",
+        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python annotation types)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option A: Upload to a labeling project as pre-labels (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option B: Upload to a labeling project using ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Uncomment this code when excluding relationships from label import\n## Relationships are not currently supported for label import\n\n# upload_job = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name=\"label_import_job\"+str(uuid.uuid4()),\n#     labels=labels) ## Remove unsupported relationships from the labels list\n\n# print(\"Errors:\", upload_job.errors)\n# print(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/text.ipynb b/examples/annotation_import/text.ipynb
index 74691ad78..1d4bb99d6 100644
--- a/examples/annotation_import/text.ipynb
+++ b/examples/annotation_import/text.ipynb
@@ -1,318 +1,316 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Text Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for text assets, and also cover MAL and Label Import methods.\n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK: \n",
-                "\n",
-                "* Entity\n",
-                "* Classification radio \n",
-                "* Classification checklist \n",
-                "* Classification free-form text \n",
-                "\n",
-                "\n",
-                "**Not** supported:\n",
-                "* Relationships\n",
-                "* Segmentation mask\n",
-                "* Polygon\n",
-                "* Bounding box \n",
-                "* Polyline\n",
-                "* Point \n",
-                "\n",
-                "MAL and Label Import: \n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n",
-                "For information on what types of annotations are supported per data type, refer to the Import text annotations [documentation](https://docs.labelbox.com/reference/import-text-annotations)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Notes:\n",
-                "  * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
-                "  * You may need to refresh your browser in order to see the results of the import job."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Supported Python annotation types and NDJSON"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nnamed_entitity_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                      name=\"named_entity\")\n\n# NDJSON\nentities_ndjson = {\n    \"name\": \"named_entity\",\n    \"location\": {\n        \"start\": 67,\n        \"end\": 128\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Checklist (Multi-choice) ##########\n\n# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\n#  NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annoations - putting it all together "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# You can now include ohter fields like attachments, media type and metadata in the data row creation step: https://docs.labelbox.com/reference/text-file\nglobal_key = \"lorem-ipsum.txt\"\ntext_asset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"TEXT\",\n    \"attachments\": [{\n        \"type\":\n            \"TEXT_URL\",\n        \"value\":\n            \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n    }],\n}\n\ndataset = client.create_dataset(\n    name=\"text_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([text_asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2:  Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool and classification `name` should match the `name` field in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-                "\n",
-                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(  # Text classification given the name \"text\"\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\"),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Text Annotations\",\n                                  ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project \n",
-                "Connect the ontology to the labeling project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Text Annotation Import Demo\",\n                                media_type=lb.MediaType.Text)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-text-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label\nlabels = []\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            named_entitity_annotation,\n            radio_annotation,\n            checklist_annotation,\n            text_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        entities_ndjson,\n        radio_annotation_ndjson,\n        checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or ground truth\n",
-                "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python Annotation types). \n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/text.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Text Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for text assets, and also cover MAL and Label Import methods.\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "\n",
+        "* Entity\n",
+        "* Classification radio \n",
+        "* Classification checklist \n",
+        "* Classification free-form text \n",
+        "\n",
+        "\n",
+        "**Not** supported:\n",
+        "* Relationships\n",
+        "* Segmentation mask\n",
+        "* Polygon\n",
+        "* Bounding box \n",
+        "* Polyline\n",
+        "* Point \n",
+        "\n",
+        "MAL and Label Import: \n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n",
+        "For information on what types of annotations are supported per data type, refer to the Import text annotations [documentation](https://docs.labelbox.com/reference/import-text-annotations)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Notes:\n",
+        "  * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+        "  * You may need to refresh your browser in order to see the results of the import job."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Supported Python annotation types and NDJSON"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nnamed_entitity_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                      name=\"named_entity\")\n\n# NDJSON\nentities_ndjson = {\n    \"name\": \"named_entity\",\n    \"location\": {\n        \"start\": 67,\n        \"end\": 128\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Checklist (Multi-choice) ##########\n\n# Python annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\n#  NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annoations - putting it all together "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# You can now include ohter fields like attachments, media type and metadata in the data row creation step: https://docs.labelbox.com/reference/text-file\nglobal_key = \"lorem-ipsum.txt\"\ntext_asset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"TEXT\",\n    \"attachments\": [{\n        \"type\":\n            \"TEXT_URL\",\n        \"value\":\n            \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n    }],\n}\n\ndataset = client.create_dataset(\n    name=\"text_annotation_import_demo_dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([text_asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2:  Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool and classification `name` should match the `name` field in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the checklist annotation above, we provided the `name` as `checklist_question`. Now, when we setup our ontology, we must ensure that the name of my classification tool is also `checklist_question`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+        "\n",
+        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(  # Text classification given the name \"text\"\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\"),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Text Annotations\",\n                                  ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project \n",
+        "Connect the ontology to the labeling project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Text Annotation Import Demo\",\n                                media_type=lb.MediaType.Text)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-text-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. If you are using Python Annotation types, compose your annotations into Labels attached to the data rows."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label\nlabels = []\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            named_entitity_annotation,\n            radio_annotation,\n            checklist_annotation,\n            text_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        entities_ndjson,\n        radio_annotation_ndjson,\n        checklist_annotation_ndjson,\n        text_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or ground truth\n",
+        "For the purpose of this tutorial only import one of the annotations payloads at the time (NDJSON or Python Annotation types). \n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job_label_import = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_label_import.wait_until_done()\nprint(\"Errors:\", upload_job_label_import.errors)\nprint(\"Status of uploads: \", upload_job_label_import.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 5aa68ef0c..19cb28d4e 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -1,347 +1,345 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Tiled Imagery Annotation Import\n",
-                "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK: \n",
-                "  * Point \n",
-                "  * Polygon\n",
-                "  * Bounding Box \n",
-                "  * Classification radio \n",
-                "  * Classification checklist \n",
-                "  * Classification free-form text\n",
-                "\n",
-                "**Not** supported:\n",
-                "  * Segmentation mask\n",
-                "\n",
-                "\n",
-                "MAL and Label Import: \n",
-                "\n",
-                "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-                "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
-                "\n",
-                "For information on what types of annotations are supported per data type, refer to this documentation:\n",
-                "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Notes:\n",
-                " * This notebook uses the Slippy Maps format\n",
-                " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
-                " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
-                " * You may need to refresh your browser in order to see the results of the import job."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport numpy as np\nimport cv2\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for tiled imagery"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Supported Python annotation types and NDJSON "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point_geo\",\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"line\": line_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"polygon\": polygon_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\"\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Upload Annotations - putting it all together\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project\n",
-                "Connect the ontology to the labeling project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Geospatial Project Demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-geo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload \n",
-                "Create the annotations payload using the snippets of code above\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python annotations\n",
-                "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=20,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_annotation_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            polygon_annotation_two,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### NDJSON annotations\n",
-                "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\n\nfor annotations in [\n        point_annotation_ndjson,\n        polyline_annotation_ndjson,\n        polygon_annotation_ndjson,\n        bbox_annotation_ndjson,\n        radio_annotation_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)\n",
-                "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_geo_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Tiled Imagery Annotation Import\n",
+        "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "  * Point \n",
+        "  * Polygon\n",
+        "  * Bounding Box \n",
+        "  * Classification radio \n",
+        "  * Classification checklist \n",
+        "  * Classification free-form text\n",
+        "\n",
+        "**Not** supported:\n",
+        "  * Segmentation mask\n",
+        "\n",
+        "\n",
+        "MAL and Label Import: \n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n",
+        "For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Notes:\n",
+        " * This notebook uses the Slippy Maps format\n",
+        " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
+        " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+        " * You may need to refresh your browser in order to see the results of the import job."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport numpy as np\nimport cv2\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for tiled imagery"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Supported Python annotation types and NDJSON "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\": \"point_geo\",\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_annotation_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"line\": line_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_annotation_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"polygon\": polygon_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# NDJSON\nradio_annotation_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\"\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\n# NDJSON\nchecklist_annotation_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n        {\n            \"name\": \"third_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Upload Annotations - putting it all together\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\n\nproject = client.create_project(name=\"Geospatial Project Demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Batches and Ontology\n\n# Create a batch to send to your MAL project\nbatch = project.create_batch(\n    \"first-batch-geo-demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python annotations\n",
+        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=20,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_annotation_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            polygon_annotation_two,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### NDJSON annotations\n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\n\nfor annotations in [\n        point_annotation_ndjson,\n        polyline_annotation_ndjson,\n        polygon_annotation_ndjson,\n        bbox_annotation_ndjson,\n        radio_annotation_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        checklist_annotation_ndjson,\n        nested_checklist_annotation_ndjson,\n        nested_radio_annotation_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload label for this data row in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_geo_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\nprint(\"Errors:\", upload_job.errors)\nprint(\"Status of uploads: \", upload_job.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/annotation_import/video.ipynb b/examples/annotation_import/video.ipynb
index 266197bdf..726e22738 100644
--- a/examples/annotation_import/video.ipynb
+++ b/examples/annotation_import/video.ipynb
@@ -1,409 +1,407 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Video Annotation Import\n",
-                "\n",
-                "* Annotations must be created and uploaded using NDJSON\n",
-                "* Supported annotations that can be uploaded through the SDK:\n",
-                "    * Bounding box\n",
-                "    * Point\n",
-                "    * Polyline \n",
-                "    * Radio classifications \n",
-                "    * Checklist classifications \n",
-                "    *  Segmentation masks\n",
-                "* **NOT** supported:\n",
-                "    * Polygons \n",
-                "\n",
-                "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO\nimport pprint\n\npp = pprint.PrettyPrinter(indent=4)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Replace with your API key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported annotations for video\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box: (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_frame_annotation_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio and checklist (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n# Python annotation\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n]\n\n## NDJSON\nframe_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist and radio (global)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "##### Global Classifications #######\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}\n\n# Python annotation\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist (global)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######### Free text classification ###########\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature\"s name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with sub-classifications (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\"\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\"\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Masks (frame-based)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "def extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "### Raster Segmentation (Byte string array)\n##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation\n\n# Single mask\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\nresponse = requests.get(url)\nimg_bytes = base64.b64encode(response.content).decode(\"utf-8\")\n\n# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain\n# all frames and instances\nframes_mask_single = [\n    lb_types.MaskFrame(\n        index=20,\n        im_bytes=response.\n        content,  # Instead of bytes you could also pass an instance URI : instance_uri=url\n    )\n]\ninstances_mask_single = [\n    lb_types.MaskInstance(color_rgb=(76, 104, 177), name=\"video_mask\")\n]\n\n## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame\nframes_cp_mask_url = [\n    {\n        \"1\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png\"\n    },\n    {\n        \"24\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\n    },\n    {\n        \"26\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png\"\n    },\n]\n\nrgb_mask_tool = [(227, 135, 126), (169, 248, 152), (83, 152, 103)]\ncp_masks = []\nunique_colors = set()\n\nlb_frames = []\nlb_instances = []\ncounter = 0\n\nfor d in frames_cp_mask_url:\n    for frame_no, v in d.items():\n        response = requests.get(v)\n        colors = extract_rgb_colors_from_url(v)\n        for color in colors:\n            if not color in unique_colors:\n                unique_colors.add(color)\n                name = (\"video_mask\" if color in rgb_mask_tool else\n                        \"mask_with_text_subclass\")\n                lb_instances.append(\n                    lb_types.MaskInstance(color_rgb=color, name=name))\n                counter += 1\n        lb_frames.append(\n            lb_types.MaskFrame(index=frame_no, im_bytes=response.content))\ncp_masks.append(\n    lb_types.VideoMaskAnnotation(\n        frames=lb_frames + frames_mask_single,\n        instances=lb_instances + instances_mask_single,\n    ))\n\npp.pprint(lb_frames)\npp.pprint(cp_masks)\n\n# NDJSON - single tool\nvideo_mask_ndjson_bytes_2 = {\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\": 31,\n                \"imBytes\": img_bytes,\n            },\n            {\n                \"index\": 34,\n                \"imBytes\": img_bytes,\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": [76, 104, 177],\n            \"name\": \"video_mask\"\n        }],\n    }\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Multiple instances of bounding box annotations in the same frame"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Fist instance of bounding box ranging from frame 22 to 27\nbbox_annotation_1 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON example:\nbbox_frame_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}\n\n# Second instance of bounding box ranging from frame 22 to 27\nbbox_annotation_2 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON\nbbox_frame_annotation_ndjson2 = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## End-to-end example: Import pre-labels or ground truth"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"sample-video-jellyfish.mp4\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"VIDEO\",\n}\n\ndataset = client.create_dataset(\n    name=\"video_demo_dataset\",\n    iam_integration=\n    None,  # If this argument is removed, labelbox will use the default integration for your organization.\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 2: Create/select an ontology\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
-                "\n",
-                "For example, when we create the bounding box annotation above, we provided the `name` as `bbox_video`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_video`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
-                "\n",
-                "\n",
-                "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.INDEX,\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## Need to defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Video Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 3: Create a labeling project \n",
-                "Connect the ontology to the labeling project."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Video Annotation Import Demo\",\n                                media_type=lb.MediaType.Video)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 4: Send a batch of data rows to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    \"first-batch-video-demo2\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # A paginated collection of data row objects, a list of data rows or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 5: Create the annotations payload \n",
-                "Create the annotations payload using the snippets of code above.\n",
-                "\n",
-                "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python Annotation Types"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass_annotation,\n    bbox_annotation_1,\n    bbox_annotation_2,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n    cp_masks,\n]\n\nfor annotation in annotations_list:\n    label.append(\n        lb_types.Label(data={\"global_key\": global_key}, annotations=annotation))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations\n",
-                "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "First, let\"s update the bbox with nested classifications with the corresponding featureSchemaId"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\n\nannotations_list_ndjson = [\n    point_annotation_ndjson,\n    bbox_annotation_ndjson,\n    polyline_frame_annotation_ndjson,\n    frame_checklist_classification_ndjson,\n    frame_radio_classification_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    frame_bbox_with_checklist_subclass_annotation_ndjson,\n    global_radio_classification_ndjson,\n    global_checklist_classification_ndjson,\n    text_annotation_ndjson,\n    bbox_frame_annotation_ndjson,\n    bbox_frame_annotation_ndjson2,\n    video_mask_ndjson_bytes_2,\n]\n\nfor annotation in annotations_list_ndjson:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
-                "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model-Assisted Labeling (MAL)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=label,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# For this demo either run MAL or Ground truth import, not both.\n\n# upload_job_label_import = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name = \"label_import_job-\" + str(uuid.uuid4()),\n#     labels=label\n# )\n\n# upload_job_label_import.wait_until_done()\n# print(\"Errors:\", upload_job_label_import.errors)\n# print(\"Status of uploads: \", upload_job_label_import.statuses)\n# print(\"   \")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Optional deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Video Annotation Import\n",
+        "\n",
+        "* Annotations must be created and uploaded using NDJSON\n",
+        "* Supported annotations that can be uploaded through the SDK:\n",
+        "    * Bounding box\n",
+        "    * Point\n",
+        "    * Polyline \n",
+        "    * Radio classifications \n",
+        "    * Checklist classifications \n",
+        "    *  Segmentation masks\n",
+        "* **NOT** supported:\n",
+        "    * Polygons \n",
+        "\n",
+        "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nfrom PIL import Image\nimport requests\nimport base64\nimport labelbox as lb\nimport labelbox.types as lb_types\nfrom io import BytesIO\nimport pprint\n\npp = pprint.PrettyPrinter(indent=4)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Replace with your API key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported annotations for video\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box: (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation class\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_annotation_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_frame_annotation_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio and checklist (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n# Python annotation\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    ),\n]\n\n## NDJSON\nframe_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist and radio (global)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "##### Global Classifications #######\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}\n\n# Python annotation\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\"\n        },\n        {\n            \"name\": \"second_checklist_answer\"\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist (global)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_annotation_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\"\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_annotation_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\"\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######### Free text classification ###########\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature\"s name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with sub-classifications (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_annotation_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\"\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\": \"checklist_class\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\"\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Masks (frame-based)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "def extract_rgb_colors_from_url(image_url):\n    response = requests.get(image_url)\n    img = Image.open(BytesIO(response.content))\n\n    colors = set()\n    for x in range(img.width):\n        for y in range(img.height):\n            pixel = img.getpixel((x, y))\n            if pixel[:3] != (0, 0, 0):\n                colors.add(pixel[:3])  # Get only the RGB values\n\n    return colors",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "### Raster Segmentation (Byte string array)\n##  For this example we are going to to pass all the annotations payload in a single VideoMaskAnnotation\n\n# Single mask\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\nresponse = requests.get(url)\nimg_bytes = base64.b64encode(response.content).decode(\"utf-8\")\n\n# We are generating our frames and instances in this step, and will later add them to the VideoMaskAnnotation that will contain\n# all frames and instances\nframes_mask_single = [\n    lb_types.MaskFrame(\n        index=20,\n        im_bytes=response.\n        content,  # Instead of bytes you could also pass an instance URI : instance_uri=url\n    )\n]\ninstances_mask_single = [\n    lb_types.MaskInstance(color_rgb=(76, 104, 177), name=\"video_mask\")\n]\n\n## Add multiple masks using multiple tools in different frames - Note that only once composite mask can exist per frame\nframes_cp_mask_url = [\n    {\n        \"1\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_1_composite_mask.png\"\n    },\n    {\n        \"24\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_24_composite_mask.png\"\n    },\n    {\n        \"26\":\n            \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/frame_26_composite_mask.png\"\n    },\n]\n\nrgb_mask_tool = [(227, 135, 126), (169, 248, 152), (83, 152, 103)]\ncp_masks = []\nunique_colors = set()\n\nlb_frames = []\nlb_instances = []\ncounter = 0\n\nfor d in frames_cp_mask_url:\n    for frame_no, v in d.items():\n        response = requests.get(v)\n        colors = extract_rgb_colors_from_url(v)\n        for color in colors:\n            if not color in unique_colors:\n                unique_colors.add(color)\n                name = (\"video_mask\" if color in rgb_mask_tool else\n                        \"mask_with_text_subclass\")\n                lb_instances.append(\n                    lb_types.MaskInstance(color_rgb=color, name=name))\n                counter += 1\n        lb_frames.append(\n            lb_types.MaskFrame(index=frame_no, im_bytes=response.content))\ncp_masks.append(\n    lb_types.VideoMaskAnnotation(\n        frames=lb_frames + frames_mask_single,\n        instances=lb_instances + instances_mask_single,\n    ))\n\npp.pprint(lb_frames)\npp.pprint(cp_masks)\n\n# NDJSON - single tool\nvideo_mask_ndjson_bytes_2 = {\n    \"masks\": {\n        \"frames\": [\n            {\n                \"index\": 31,\n                \"imBytes\": img_bytes,\n            },\n            {\n                \"index\": 34,\n                \"imBytes\": img_bytes,\n            },\n        ],\n        \"instances\": [{\n            \"colorRGB\": [76, 104, 177],\n            \"name\": \"video_mask\"\n        }],\n    }\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Multiple instances of bounding box annotations in the same frame"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Fist instance of bounding box ranging from frame 22 to 27\nbbox_annotation_1 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON example:\nbbox_frame_annotation_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}\n\n# Second instance of bounding box ranging from frame 22 to 27\nbbox_annotation_2 = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=22,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=27,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n    ),\n]\n# NDJSON\nbbox_frame_annotation_ndjson2 = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 22,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 27,\n                \"bbox\": bbox_dm2\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## End-to-end example: Import pre-labels or ground truth"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"sample-video-jellyfish.mp4\"\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n    \"media_type\":\n        \"VIDEO\",\n}\n\ndataset = client.create_dataset(\n    name=\"video_demo_dataset\",\n    iam_integration=\n    None,  # If this argument is removed, labelbox will use the default integration for your organization.\n)\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the bounding box annotation above, we provided the `name` as `bbox_video`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_video`. The same alignment must hold true for the other tools and classifications we create in our ontology.\n",
+        "\n",
+        "\n",
+        "[Documentation for reference ](https://docs.labelbox.com/reference/import-text-annotations)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.INDEX,\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## Need to defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Video Annotation Import Demo Ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 3: Create a labeling project \n",
+        "Connect the ontology to the labeling project."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Video Annotation Import Demo\",\n                                media_type=lb.MediaType.Video)\n\n## connect ontology to your project\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 4: Send a batch of data rows to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch = project.create_batch(\n    \"first-batch-video-demo2\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # A paginated collection of data row objects, a list of data rows or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above.\n",
+        "\n",
+        "Labelbox supports two formats for the annotations payload: NDJSON and Python Annotation types."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python Annotation Types"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass_annotation,\n    bbox_annotation_1,\n    bbox_annotation_2,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n    cp_masks,\n]\n\nfor annotation in annotations_list:\n    label.append(\n        lb_types.Label(data={\"global_key\": global_key}, annotations=annotation))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations\n",
+        "Here we create the complete `label_ndjson` payload of annotations. There is one annotation for each *reference to an annotation* that we created above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "First, let\"s update the bbox with nested classifications with the corresponding featureSchemaId"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\n\nannotations_list_ndjson = [\n    point_annotation_ndjson,\n    bbox_annotation_ndjson,\n    polyline_frame_annotation_ndjson,\n    frame_checklist_classification_ndjson,\n    frame_radio_classification_ndjson,\n    nested_radio_annotation_ndjson,\n    nested_checklist_annotation_ndjson,\n    frame_bbox_with_checklist_subclass_annotation_ndjson,\n    global_radio_classification_ndjson,\n    global_checklist_classification_ndjson,\n    text_annotation_ndjson,\n    bbox_frame_annotation_ndjson,\n    bbox_frame_annotation_ndjson2,\n    video_mask_ndjson_bytes_2,\n]\n\nfor annotation in annotations_list_ndjson:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or completed labels\n",
+        "For the purpose of this tutorial only run one of the label imports at once, otherwise the previous import might get overwritten."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model-Assisted Labeling (MAL)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload MAL label for this data row in project\nupload_job_mal = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job-\" + str(uuid.uuid4()),\n    predictions=label,\n)\n\nupload_job_mal.wait_until_done()\nprint(\"Errors:\", upload_job_mal.errors)\nprint(\"Status of uploads: \", upload_job_mal.statuses)\nprint(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# For this demo either run MAL or Ground truth import, not both.\n\n# upload_job_label_import = lb.LabelImport.create_from_objects(\n#     client = client,\n#     project_id = project.uid,\n#     name = \"label_import_job-\" + str(uuid.uuid4()),\n#     labels=label\n# )\n\n# upload_job_label_import.wait_until_done()\n# print(\"Errors:\", upload_job_label_import.errors)\n# print(\"Status of uploads: \", upload_job_label_import.statuses)\n# print(\"   \")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Delete Project\n# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/basics.ipynb b/examples/basics/basics.ipynb
index cb2dd6230..3b7796603 100644
--- a/examples/basics/basics.ipynb
+++ b/examples/basics/basics.ipynb
@@ -1,187 +1,185 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Basic project/datasets overview\n",
-                "\n",
-                "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fields\n",
-                "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
-                "* These fields are attributes of the object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(project.name)\nprint(dataset.name)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.update(description=\"new description field\")\nprint(project.description)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Pagination\n",
-                "* Queries that return a list of database objects are return as a PaginatedCollection\n",
-                "* Limits the data that is being returned for better performance"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Query parameters\n",
-                "* Query with the following conventions:\n",
-                "    * `DbObject.Field`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Querying Limitations\n",
-                "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
-                "* The below query is not valid since get_project returns a project not a dataset\n",
-                ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Relationships between projects and batches/datasets\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Basic project/datasets overview\n",
+        "\n",
+        "This notebook is used to go over the basic of the Python SDK, such as what a db object is, and how to interact with it. \n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API key and client\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# For the purpose of this demo get a single project/dataset id from your organization\n\n# Get a single Project id\n# get_projects returns a PaginatedCollection object, which is iterable.\nproject = next(client.get_projects())\nproject_id = project.uid\nproject_name = project.name\nprint(\"Project ID: \", project_id)\nprint(\"Project Name:\", project_name)\n\nprint(\"-\" * 40)\n\n# Get a single dataset id\n# get_datasets returns a PaginatedCollection object, which is iterable.\ndataset = next(client.get_datasets())\ndataset_id = dataset.uid\ndataset_name = dataset.name\nprint(\"Dataset ID: \", dataset_id)\nprint(\"Dataset Name:\", dataset_name)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Fetch the project and dataset by using the IDs fetched in the previous cell\nproject = client.get_project(project_id)\ndataset = client.get_dataset(dataset_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Project: \", project)\nprint(\"Dataset: \", dataset)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Fields\n",
+        "* All db objects have fields (look at the source code to see them https://github.com/Labelbox/labelbox-python/blob/develop/labelbox/schema/project.py)\n",
+        "* These fields are attributes of the object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(project.name)\nprint(dataset.name)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Fields can be updated. This will be reflected server side (you will see it in labelbox) "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.update(description=\"new description field\")\nprint(project.description)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Pagination\n",
+        "* Queries that return a list of database objects are return as a PaginatedCollection\n",
+        "* Limits the data that is being returned for better performance"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels_paginated_collection = project.labels()\nprint(\"Type of collection: \", type(labels_paginated_collection))\n\n# A paginated collection can be parsed by using list()\n# list(paginated...) should be avoided for queries that could return more than a dozen results\nprint(\"Number of labels :\", len(list(labels_paginated_collection)))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over the paginated collection\ntry:\n    single_label = next(project.labels())\n    print(single_label)\nexcept StopIteration:\n    print(\"Project has no labels !\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Query parameters\n",
+        "* Query with the following conventions:\n",
+        "    * `DbObject.Field`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "datasets = client.get_datasets(where=lb.Dataset.name == dataset_name)\n\nprojects = client.get_projects(\n    where=((lb.Project.name == project_name) &\n           (lb.Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one.",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Querying Limitations\n",
+        "* The DbObject used for the query must be the same as the DbObject returned by the querying function.  \n",
+        "* The below query is not valid since get_project returns a project not a dataset\n",
+        ">  `>>> projects = client.get_projects(where = lb.Dataset.name == \"dataset_name\")`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Relationships between projects and batches/datasets\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "sample_project_batches = project.batches()\n\nlist(sample_project_batches)\n\nfor b in sample_project_batches:\n    print(f\" Name of project : {b.project().name}\")\n    print(f\" Name of batches in project: {b.name}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/batches.ipynb b/examples/basics/batches.ipynb
index dc62d4a7a..2d07682b5 100644
--- a/examples/basics/batches.ipynb
+++ b/examples/basics/batches.ipynb
@@ -1,301 +1,299 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Batches\n",
-                "https://docs.labelbox.com/docs/batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* A batch is collection of data rows.\n",
-                "* A data row cannot be part of more than one batch in a given project.\n",
-                "* Batches work for all data types, but there can only be one data type per project.\n",
-                "* Batches can not be shared between projects.\n",
-                "* Batches may have data rows from multiple datasets.\n",
-                "* Currently, only benchmarks quality settings is supported in batch projects\n",
-                "* You can set the priority for each batch."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create a dataset and data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup batch project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Select all data rows from the dataset\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Select a random sample\n",
-                "This method is useful if you have large datasets and only want to work with a handful of data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "sample = random.sample(global_keys, 4)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a batch\n",
-                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create multiple batches\n",
-                "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
-                "\n",
-                "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
-                "\n",
-                "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
-                "\n",
-                "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create batches from a dataset\n",
-                "\n",
-                "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
-                "\n",
-                "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Manage Batches\n",
-                "Note: You can view your batch data through the **Data Rows** tab."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Export Batches"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create and Attach Ontology to Project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export from Project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Archive a batch"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up \n",
-                "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/batches.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Batches\n",
+        "https://docs.labelbox.com/docs/batches"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* A batch is collection of data rows.\n",
+        "* A data row cannot be part of more than one batch in a given project.\n",
+        "* Batches work for all data types, but there can only be one data type per project.\n",
+        "* Batches can not be shared between projects.\n",
+        "* Batches may have data rows from multiple datasets.\n",
+        "* Currently, only benchmarks quality settings is supported in batch projects\n",
+        "* You can set the priority for each batch."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport random\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API key and client\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create a dataset and data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a dataset\ndataset = client.create_dataset(name=\"Demo-Batches-Colab\")\n\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n    })\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"ERRORS: \", data_rows.errors)\nprint(\"RESULT URL: \", data_rows.result_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup batch project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Demo-Batches-Project\",\n                                media_type=lb.MediaType.Image)\nprint(\"Project Name: \", project.name, \"Project ID: \", project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create batches"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Select all data rows from the dataset\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "global_keys = [data_row[\"data_row\"][\"global_key\"] for data_row in data_rows]\nprint(\"Number of global keys:\", len(global_keys))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Select a random sample\n",
+        "This method is useful if you have large datasets and only want to work with a handful of data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "sample = random.sample(global_keys, 4)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a batch\n",
+        "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch = project.create_batch(\n    name=\"Demo-First-Batch\",  # Each batch in a project must have a unique name\n    global_keys=sample,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)\n# number of data rows in the batch\nprint(\"Number of data rows in batch: \", batch.size)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create multiple batches\n",
+        "The `project.create_batches()` method accepts up to 1 million data rows.  Batches are chunked into groups of 100k if necessary, which is the maximum batch size. This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method.\n",
+        "\n",
+        "This method takes in a list of either data row IDs or `DataRow` objects into a `data_rows` argument or global keys into a `global_keys` argument, but both approaches cannot be used in the same method. Batches will be created with the specified `name_prefix` argument and a unique suffix to ensure unique batch names. The suffix will be a 4-digit number starting at `0000`.\n",
+        "\n",
+        "For example, if the name prefix is `demo-create-batches-` and three batches are created, the names will be `demo-create-batches-0000`, `demo-create-batches-0001`, and `demo-create-batches-0002`. This method will throw an error if a batch with the same name already exists.\n",
+        "\n",
+        "In the code below, only one batch will be created, since we are only using the few data rows we created above. Creating over 100k data rows for this demonstration is not sensible, but this method is the preferred approach for batch creation as it will gracefully handle massive sets of data rows."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First, we must create a second project so that we can re-use the data rows we already created.\nsecond_project = client.create_project(name=\"Second-Demo-Batches-Project\",\n                                       media_type=lb.MediaType.Image)\nprint(\"Project Name: \", second_project.name, \"Project ID: \", second_project.uid)\n\n# Then, use the method that will create multiple batches if necessary.\ntask = second_project.create_batches(name_prefix=\"demo-create-batches-\",\n                                     global_keys=global_keys,\n                                     priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create batches from a dataset\n",
+        "\n",
+        "If you wish to create batches in a project using all the data rows of a dataset, instead of having to gather global keys or ID and using subsets of data rows, you can use the `project.create_batches_from_dataset()` method. This method takes in a dataset ID and creates a batch (or batches if there are more than 100k data rows) comprised of all data rows not already in the project.\n",
+        "\n",
+        "The same logic applies to the `name_prefix` argument and the naming of batches as described in the section immediately above."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First, we must create a third project so that we can re-use the data rows we already created.\nthird_project = client.create_project(name=\"Third-Demo-Batches-Project\",\n                                      media_type=lb.MediaType.Image)\nprint(\"Project Name: \", third_project.name, \"Project ID: \", third_project.uid)\n\n# Then, use the method to create batches from a dataset.\ntask = third_project.create_batches_from_dataset(\n    name_prefix=\"demo-batches-from-dataset-\",\n    dataset_id=dataset.uid,\n    priority=5)\n\nprint(\"Errors: \", task.errors())\nprint(\"Result: \", task.result())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Manage Batches\n",
+        "Note: You can view your batch data through the **Data Rows** tab."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Export Batches"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create and Attach Ontology to Project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export from Project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        batch.uid\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "## Export the data row iDs\ndata_rows = [dr for dr in data_rows]\nprint(\"Data rows in batch: \", data_rows)\n\n## List the batches in your project\nfor batch in project.batches():\n    print(\"Batch name: \", batch.name, \"  Batch ID:\", batch.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Archive a batch"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Archiving a batch removes all queued data rows in the batch from the project\nbatch.remove_queued_data_rows()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Clean up \n",
+        "Uncomment and run the cell below to optionally delete the batch, dataset, and/or project created in this demo."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Delete Batch\n# batch.delete()\n\n# Delete Project\n# project.delete()\n\n# Delete DataSet\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/custom_embeddings.ipynb b/examples/basics/custom_embeddings.ipynb
index 65f4952d6..45a0b2df0 100644
--- a/examples/basics/custom_embeddings.ipynb
+++ b/examples/basics/custom_embeddings.ipynb
@@ -1,288 +1,286 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Custom Embeddings\n",
-                "\n",
-                "You can improve your data exploration and similarity search experience by adding your own custom embeddings. Labelbox allows you to upload up to 10 different custom embeddings per workspace on any kind of data. You can experiment with different embeddings to power your data selection."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Set up "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip3 install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport numpy as np\nimport json\nimport uuid\nimport random",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Select data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "- Get images from a Labelbox dataset\n",
-                "- To improve similarity search, you need to upload custom embeddings to at least 1,000 data rows.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATASET_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.get_dataset(dataset_id=DATASET_ID)\nexport_task = dataset.export()\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_row_dict = [{\"data_row_id\": dr[\"data_row\"][\"id\"]} for dr in data_rows]\ndata_row_dict = data_row_dict[:\n                              1000]  # keep the first 1000 examples for the sake of this demo",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Create custom embedding payload "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Generate random vectors for embeddings (max : 2048 dimensions)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nb_data_rows = len(data_row_dict)\nprint(\"Number of data rows: \", nb_data_rows)\n# Labelbox supports custom embedding vectors of dimension up to 2048\ncustom_embeddings = [list(np.random.random(2048)) for _ in range(nb_data_rows)]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "List all custom embeddings available in your Labelbox workspace"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "embeddings = client.get_embeddings()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Choose an existing embedding type or create a new one"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Name of the custom embedding must be unique\nembedding = client.create_embedding(\"my_custom_embedding_2048_dimensions\", 2048)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Create payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "The payload should encompass the `key` (data row id  or global key) and the new embedding vector data. Note that the `dataset.upsert_data_rows()` operation will only update the values you pass in the payload; all other existing row data will not be modified."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "payload = []\nfor data_row_dict, custom_embedding in zip(data_row_dict, custom_embeddings):\n    payload.append({\n        \"key\":\n            lb.UniqueId(data_row_dict[\"data_row_id\"]),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": custom_embedding\n        }],\n    })\n\nprint(\"payload\", len(payload), payload[:1])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Upload payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Upsert data rows with custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "task = dataset.upsert_data_rows(payload)\ntask.wait_till_done()\nprint(task.errors)\nprint(task.status)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Get the count of imported vectors for a custom embedding"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Count how many data rows have a specific custom embedding (this can take a couple of minutes)\ncount = embedding.get_imported_vector_count()\nprint(count)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Delete custom embedding type"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# embedding.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Upload custom embeddings during data row creation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Create a dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset_new = client.create_dataset(name=\"data_rows_with_embeddings\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch an embedding (2048 dimension)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "embedding = client.get_embedding_by_name(\"my_custom_embedding_2048_dimensions\")\nvector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Upload data rows with embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": vector\n        }],\n    })\n\ntask1 = dataset_new.create_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Custom Embeddings\n",
+        "\n",
+        "You can improve your data exploration and similarity search experience by adding your own custom embeddings. Labelbox allows you to upload up to 10 different custom embeddings per workspace on any kind of data. You can experiment with different embeddings to power your data selection."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Set up "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip3 install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport numpy as np\nimport json\nimport uuid\nimport random",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Select data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "- Get images from a Labelbox dataset\n",
+        "- To improve similarity search, you need to upload custom embeddings to at least 1,000 data rows.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATASET_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.get_dataset(dataset_id=DATASET_ID)\nexport_task = dataset.export()\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_row_dict = [{\"data_row_id\": dr[\"data_row\"][\"id\"]} for dr in data_rows]\ndata_row_dict = data_row_dict[:\n                              1000]  # keep the first 1000 examples for the sake of this demo",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Create custom embedding payload "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Generate random vectors for embeddings (max : 2048 dimensions)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nb_data_rows = len(data_row_dict)\nprint(\"Number of data rows: \", nb_data_rows)\n# Labelbox supports custom embedding vectors of dimension up to 2048\ncustom_embeddings = [list(np.random.random(2048)) for _ in range(nb_data_rows)]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "List all custom embeddings available in your Labelbox workspace"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "embeddings = client.get_embeddings()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Choose an existing embedding type or create a new one"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Name of the custom embedding must be unique\nembedding = client.create_embedding(\"my_custom_embedding_2048_dimensions\", 2048)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "The payload should encompass the `key` (data row id  or global key) and the new embedding vector data. Note that the `dataset.upsert_data_rows()` operation will only update the values you pass in the payload; all other existing row data will not be modified."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "payload = []\nfor data_row_dict, custom_embedding in zip(data_row_dict, custom_embeddings):\n    payload.append({\n        \"key\":\n            lb.UniqueId(data_row_dict[\"data_row_id\"]),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": custom_embedding\n        }],\n    })\n\nprint(\"payload\", len(payload), payload[:1])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Upload payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Upsert data rows with custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "task = dataset.upsert_data_rows(payload)\ntask.wait_till_done()\nprint(task.errors)\nprint(task.status)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Get the count of imported vectors for a custom embedding"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Count how many data rows have a specific custom embedding (this can take a couple of minutes)\ncount = embedding.get_imported_vector_count()\nprint(count)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Delete custom embedding type"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# embedding.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Upload custom embeddings during data row creation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create a dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a dataset\ndataset_new = client.create_dataset(name=\"data_rows_with_embeddings\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch an embedding (2048 dimension)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "embedding = client.get_embedding_by_name(\"my_custom_embedding_2048_dimensions\")\nvector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Upload data rows with embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        \"embeddings\": [{\n            \"embedding_id\": embedding.id,\n            \"vector\": vector\n        }],\n    })\n\ntask1 = dataset_new.create_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/data_row_metadata.ipynb b/examples/basics/data_row_metadata.ipynb
index 9c9f64987..fa5970ce2 100644
--- a/examples/basics/data_row_metadata.ipynb
+++ b/examples/basics/data_row_metadata.ipynb
@@ -1,302 +1,300 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Data Row Metadata\n",
-                "\n",
-                "Metadata is useful to better understand data on the platform to help with labeling review, model diagnostics, and data selection. This **should not be confused with attachments**. Attachments provide additional context for labelers but is not searchable within Catalog."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Metadata ontology\n",
-                "\n",
-                "We use a similar system for managing metadata as we do feature schemas. Metadata schemas are strongly typed to ensure we can provide the best experience in the App. Each metadata field can be uniquely accessed by id. Names are unique within the kind of metadata, reserved or custom. A DataRow can have a maximum of 5 metadata fields at a time.\n",
-                "\n",
-                "### Metadata kinds\n",
-                "\n",
-                "* **Enum**: A classification with options, only one option can be selected at a time\n",
-                "* **DateTime**: A utc ISO datetime \n",
-                "* **String**: A string of less than 500 characters\n",
-                "\n",
-                "### Reserved fields\n",
-                "\n",
-                "* **tag**: a free text field\n",
-                "* **split**: enum of train-valid-test\n",
-                "* **captureDateTime**: ISO 8601 datetime field. All times must be in UTC\n",
-                "\n",
-                "### Custom fields\n",
-                "\n",
-                "* **Embedding**: 128 float 32 vector used for similarity. To upload custom embeddings use the following [tutorial](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/basics/custom_embeddings.ipynb)\n",
-                "* Any metadata kind can be customized"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom datetime import datetime\nfrom pprint import pprint\nfrom labelbox.schema.data_row_metadata import DataRowMetadataKind\nfrom uuid import uuid4",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get the current metadata ontology "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "mdo = client.get_data_row_metadata_ontology()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# list all your metadata ontology as a dictionary accessable by id\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Access metadata by name"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "split_field = mdo.reserved_by_name[\"split\"]\nsplit_field",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "tag_field = mdo.reserved_by_name[\"tag\"]\ntag_field",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "train_field = mdo.reserved_by_name[\"split\"][\"train\"]\ntrain_field",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Construct metadata fields for existing metadata schemas\n",
-                "\n",
-                "To construct a metadata field you must provide the name for the metadata field and the value that will be uploaded. You can either construct a DataRowMetadataField object or specify the name and value in a dictionary format.\n",
-                "\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option 1: Specify metadata with a list of `DataRowMetadataField` objects. This is the recommended option since it comes with validation for metadata fields."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Construct a metadata field of string kind\ntag_metadata_field = lb.DataRowMetadataField(\n    name=\"tag\",\n    value=\"tag_string\",\n)\n\n# Construct an metadata field of datetime kind\ncapture_datetime_field = lb.DataRowMetadataField(\n    name=\"captureDateTime\",\n    value=datetime.utcnow(),\n)\n\n# Construct a metadata field of Enums options\nsplit_metadata_field = lb.DataRowMetadataField(\n    name=\"split\",\n    value=\"train\",\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Option 2: You can also specify the metadata fields with dictionary format without declaring the `DataRowMetadataField` objects.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Construct a dictionary of string metadata\ntag_metadata_field_dict = {\n    \"name\": \"tag\",\n    \"value\": \"tag_string\",\n}\n\n# Construct a dictionary of datetime metadata\ncapture_datetime_field_dict = {\n    \"name\": \"captureDateTime\",\n    \"value\": datetime.utcnow(),\n}\n\n# Construct a dictionary of Enums options metadata\nsplit_metadata_field_dict = {\n    \"name\": \"split\",\n    \"value\": \"train\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create a custom metadata schema with their corresponding fields\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Final\ncustom_metadata_fields = []\n\n# Create the schema for the metadata\nnumber_schema = mdo.create_schema(name=\"numberMetadataCustom\",\n                                  kind=DataRowMetadataKind.number)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_number = lb.DataRowMetadataField(\n    name=number_schema.name, value=5.0)\n\ncustom_metadata_fields.append(data_row_metadata_fields_number)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create the schema for an enum metadata\ncustom_metadata_fields = []\n\nenum_schema = mdo.create_schema(\n    name=\"enumMetadata\",\n    kind=DataRowMetadataKind.enum,\n    options=[\"option1\", \"option2\"],\n)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_enum_1 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option1\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_1)\n\ndata_row_metadata_fields_enum_2 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option2\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Inspect the newly created metadata schemas\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create data rows with metadata\n",
-                "\n",
-                "See our [documentation](https://docs.labelbox.com/docs/limits) for information on limits for uploading data rows in a single API operation."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# A simple example of uploading data rows with metadata\ndataset = client.create_dataset(\n    name=\"Simple Data Rows import with metadata example\")\nglobal_key = \"s_basic.jpg\"\ndata_row = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/basic.jpg\",\n    \"global_key\":\n        global_key,\n}\n# This line works with dictionaries as well as schemas and fields created with DataRowMetadataField\ndata_row[\"metadata_fields\"] = custom_metadata_fields + [\n    split_metadata_field,\n    capture_datetime_field_dict,\n    tag_metadata_field,\n]\n\ntask = dataset.create_data_rows([data_row])\ntask.wait_till_done()\nresult_task = task.result\nprint(result_task)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Update data row metadata"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get the data row that was uploaded in the previous cell\nnum_schema = mdo.get_by_name(\"numberMetadataCustom\")\n\n# Update the metadata\nupdated_metadata = lb.DataRowMetadataField(schema_id=num_schema.uid, value=10.2)\n\n# Create data row payload\ndata_row_payload = lb.DataRowMetadata(global_key=global_key,\n                                      fields=[updated_metadata])\n\n# Upsert the fields with the update metadata for number-metadata\nmdo.bulk_upsert([data_row_payload])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Update metadata schema"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# update a name\nnumber_schema = mdo.update_schema(name=\"numberMetadataCustom\",\n                                  new_name=\"numberMetadataCustomNew\")\n\n# update an Enum metadata schema option's name, this only applies to Enum metadata schema.\nenum_schema = mdo.update_enum_option(name=\"enumMetadata\",\n                                     option=\"option1\",\n                                     new_option=\"option3\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Accessing metadata\n",
-                "\n",
-                "You can examine an individual data row, including its metadata."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_row = next(dataset.data_rows())\nfor metadata_field in data_row.metadata_fields:\n    print(metadata_field[\"name\"], \":\", metadata_field[\"value\"])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "You can bulk export metadata using data row IDs."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_rows_metadata = mdo.bulk_export([data_row.uid])\nlen(data_rows_metadata)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Delete custom metadata schema \n",
-                "You can delete custom metadata schema by name. If you wish to delete a metadata schema, uncomment the line below and insert the desired name."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# status = mdo.delete_schema(name=\"<metadata schema name>\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_row_metadata.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Data Row Metadata\n",
+        "\n",
+        "Metadata is useful to better understand data on the platform to help with labeling review, model diagnostics, and data selection. This **should not be confused with attachments**. Attachments provide additional context for labelers but is not searchable within Catalog."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Metadata ontology\n",
+        "\n",
+        "We use a similar system for managing metadata as we do feature schemas. Metadata schemas are strongly typed to ensure we can provide the best experience in the App. Each metadata field can be uniquely accessed by id. Names are unique within the kind of metadata, reserved or custom. A DataRow can have a maximum of 5 metadata fields at a time.\n",
+        "\n",
+        "### Metadata kinds\n",
+        "\n",
+        "* **Enum**: A classification with options, only one option can be selected at a time\n",
+        "* **DateTime**: A utc ISO datetime \n",
+        "* **String**: A string of less than 500 characters\n",
+        "\n",
+        "### Reserved fields\n",
+        "\n",
+        "* **tag**: a free text field\n",
+        "* **split**: enum of train-valid-test\n",
+        "* **captureDateTime**: ISO 8601 datetime field. All times must be in UTC\n",
+        "\n",
+        "### Custom fields\n",
+        "\n",
+        "* **Embedding**: 128 float 32 vector used for similarity. To upload custom embeddings use the following [tutorial](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/master/examples/basics/custom_embeddings.ipynb)\n",
+        "* Any metadata kind can be customized"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom datetime import datetime\nfrom pprint import pprint\nfrom labelbox.schema.data_row_metadata import DataRowMetadataKind\nfrom uuid import uuid4",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get the current metadata ontology "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "mdo = client.get_data_row_metadata_ontology()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# list all your metadata ontology as a dictionary accessable by id\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Access metadata by name"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "split_field = mdo.reserved_by_name[\"split\"]\nsplit_field",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "tag_field = mdo.reserved_by_name[\"tag\"]\ntag_field",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "train_field = mdo.reserved_by_name[\"split\"][\"train\"]\ntrain_field",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Construct metadata fields for existing metadata schemas\n",
+        "\n",
+        "To construct a metadata field you must provide the name for the metadata field and the value that will be uploaded. You can either construct a DataRowMetadataField object or specify the name and value in a dictionary format.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option 1: Specify metadata with a list of `DataRowMetadataField` objects. This is the recommended option since it comes with validation for metadata fields."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Construct a metadata field of string kind\ntag_metadata_field = lb.DataRowMetadataField(\n    name=\"tag\",\n    value=\"tag_string\",\n)\n\n# Construct an metadata field of datetime kind\ncapture_datetime_field = lb.DataRowMetadataField(\n    name=\"captureDateTime\",\n    value=datetime.utcnow(),\n)\n\n# Construct a metadata field of Enums options\nsplit_metadata_field = lb.DataRowMetadataField(\n    name=\"split\",\n    value=\"train\",\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Option 2: You can also specify the metadata fields with dictionary format without declaring the `DataRowMetadataField` objects.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Construct a dictionary of string metadata\ntag_metadata_field_dict = {\n    \"name\": \"tag\",\n    \"value\": \"tag_string\",\n}\n\n# Construct a dictionary of datetime metadata\ncapture_datetime_field_dict = {\n    \"name\": \"captureDateTime\",\n    \"value\": datetime.utcnow(),\n}\n\n# Construct a dictionary of Enums options metadata\nsplit_metadata_field_dict = {\n    \"name\": \"split\",\n    \"value\": \"train\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create a custom metadata schema with their corresponding fields\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Final\ncustom_metadata_fields = []\n\n# Create the schema for the metadata\nnumber_schema = mdo.create_schema(name=\"numberMetadataCustom\",\n                                  kind=DataRowMetadataKind.number)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_number = lb.DataRowMetadataField(\n    name=number_schema.name, value=5.0)\n\ncustom_metadata_fields.append(data_row_metadata_fields_number)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create the schema for an enum metadata\ncustom_metadata_fields = []\n\nenum_schema = mdo.create_schema(\n    name=\"enumMetadata\",\n    kind=DataRowMetadataKind.enum,\n    options=[\"option1\", \"option2\"],\n)\n\n# Add fields to the metadata schema\ndata_row_metadata_fields_enum_1 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option1\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_1)\n\ndata_row_metadata_fields_enum_2 = lb.DataRowMetadataField(name=enum_schema.name,\n                                                          value=\"option2\")\ncustom_metadata_fields.append(data_row_metadata_fields_enum_2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Inspect the newly created metadata schemas\nmetadata_ontologies = mdo.fields_by_id\npprint(metadata_ontologies, indent=2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create data rows with metadata\n",
+        "\n",
+        "See our [documentation](https://docs.labelbox.com/docs/limits) for information on limits for uploading data rows in a single API operation."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# A simple example of uploading data rows with metadata\ndataset = client.create_dataset(\n    name=\"Simple Data Rows import with metadata example\")\nglobal_key = \"s_basic.jpg\"\ndata_row = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/basic.jpg\",\n    \"global_key\":\n        global_key,\n}\n# This line works with dictionaries as well as schemas and fields created with DataRowMetadataField\ndata_row[\"metadata_fields\"] = custom_metadata_fields + [\n    split_metadata_field,\n    capture_datetime_field_dict,\n    tag_metadata_field,\n]\n\ntask = dataset.create_data_rows([data_row])\ntask.wait_till_done()\nresult_task = task.result\nprint(result_task)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Update data row metadata"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get the data row that was uploaded in the previous cell\nnum_schema = mdo.get_by_name(\"numberMetadataCustom\")\n\n# Update the metadata\nupdated_metadata = lb.DataRowMetadataField(schema_id=num_schema.uid, value=10.2)\n\n# Create data row payload\ndata_row_payload = lb.DataRowMetadata(global_key=global_key,\n                                      fields=[updated_metadata])\n\n# Upsert the fields with the update metadata for number-metadata\nmdo.bulk_upsert([data_row_payload])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Update metadata schema"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# update a name\nnumber_schema = mdo.update_schema(name=\"numberMetadataCustom\",\n                                  new_name=\"numberMetadataCustomNew\")\n\n# update an Enum metadata schema option's name, this only applies to Enum metadata schema.\nenum_schema = mdo.update_enum_option(name=\"enumMetadata\",\n                                     option=\"option1\",\n                                     new_option=\"option3\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Accessing metadata\n",
+        "\n",
+        "You can examine an individual data row, including its metadata."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_row = next(dataset.data_rows())\nfor metadata_field in data_row.metadata_fields:\n    print(metadata_field[\"name\"], \":\", metadata_field[\"value\"])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "You can bulk export metadata using data row IDs."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_rows_metadata = mdo.bulk_export([data_row.uid])\nlen(data_rows_metadata)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Delete custom metadata schema \n",
+        "You can delete custom metadata schema by name. If you wish to delete a metadata schema, uncomment the line below and insert the desired name."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# status = mdo.delete_schema(name=\"<metadata schema name>\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/data_rows.ipynb b/examples/basics/data_rows.ipynb
index c240b0d30..46e758ee9 100644
--- a/examples/basics/data_rows.ipynb
+++ b/examples/basics/data_rows.ipynb
@@ -1,317 +1,315 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Data rows are the assets that are being labeled. We currently support the following asset types:\n",
-                "    * Image\n",
-                "    * Text\n",
-                "    * Video\n",
-                "    * Geospatial / Tiled Imagery\n",
-                "    * Audio\n",
-                "    * Documents \n",
-                "    * HTML \n",
-                "    * DICOM \n",
-                "    * Conversational\n",
-                "* A data row cannot exist without belonging to a dataset.\n",
-                "* Data rows are added to labeling tasks by first attaching them to datasets and then creating batches in projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install labelbox -q",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get data rows from projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Pick a project with batches that have data rows with global keys\nPROJECT_ID = \"<PROJECT-ID>\"\nproject = client.get_project(PROJECT_ID)\nbatches = list(project.batches())\nprint(batches)\n# This is the same as\n# -> dataset = client.get_dataset(dataset_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fetch data rows from project's batches\n",
-                "\n",
-                "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\n\nbatch_ids = [batch.uid for batch in batches]\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\":\n        batch_ids,  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Get single data row\ndata_row = data_rows[0]\nprint(data_row)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get labels from the data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Associated label(s)\", data_row[\"projects\"][project.uid][\"labels\"])\nprint(\"Global key\", data_row[\"data_row\"][\"global_key\"])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get data row ids by using global keys"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"<ENTER GLOBAL KEY>\"\ntask = client.get_data_row_ids_for_global_keys([global_key])\nprint(f\"Data row id: {task['results']}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create\n",
-                "We recommend the following methods to create data rows : `dataset.upsert_data_rows()`, and `dataset.create_data_rows()`, "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create data rows via `dataset.upsert_data_rows()`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a dataset\ndataset = client.create_dataset(name=\"data_rows_demo_dataset_6\")\n# You can also upload metadata along with your data row\nmdo = client.get_data_row_metadata_ontology()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 8):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n        \"attachments\": [\n            {\n                \"type\":\n                    \"IMAGE_OVERLAY\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\": \"RAW_TEXT\",\n                \"value\": \"IOWA, Zone 2232, June 2022 [Text string]\",\n            },\n            {\n                \"type\":\n                    \"TEXT_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n            },\n            {\n                \"type\":\n                    \"IMAGE\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\":\n                    \"VIDEO\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4\",\n            },\n            {\n                \"type\":\n                    \"HTML\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/windy.html\",\n            },\n            {\n                \"type\":\n                    \"PDF_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n            },\n        ],\n    })\n\ntask1 = dataset.upsert_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Create data rows from data in your local path "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "from PIL import Image\n\n# Create dummy empty jpeg file\nwidth = 400\nheight = 300\ncolor = (255, 255, 255)  # White color\nimage = Image.new(\"RGB\", (width, height), color)\n\n# Save the image as a JPEG file\nimage.save(\"dummy.jpg\")\n\nlocal_data_path = \"dummy.jpg\"\n\ndata = {\"row_data\": local_data_path, \"global_key\": str(uuid.uuid4())}\n\ntask3 = dataset.upsert_data_rows([data])\ntask3.wait_till_done()\nprint(\"ERRORS: \", task3.errors)\nprint(\"RESULTS:\", task3.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# You can mix local files with urls when creating data rows\ntask4 = dataset.upsert_data_rows([\n    {\n        \"row_data\":\n            \"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_0009.jpeg\",\n        \"global_key\":\n            str(uuid.uuid4()),\n    },\n    {\n        \"row_data\": local_data_path,\n        \"global_key\": str(uuid.uuid4())\n    },\n])\ntask4.wait_till_done()\nprint(\"ERRORS: \", task4.errors)\nprint(\"RESULTS:\", task4.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create data rows via `dataset.create_data_rows()`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset_2 = client.create_dataset(name=\"data_rows_demo_dataset_3\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n    })\n\ntask1_2 = dataset_2.create_data_rows(uploads)\ntask1_2.wait_till_done()\nprint(\"ERRORS: \", task1_2.errors)\nprint(\"RESULTS:\", task1_2.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update\n",
-                "`dataset.upsert_data_rows()` can also be use to update data rows\n",
-                "\n",
-                "To update data rows using this method, you need to pass a `key`, which can reference either a global key or a data row ID. Additionally, include any fields that you wish to update along with their new values.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Update the global key assodicated with the DATAROW_ID or GLOBAL_KEY, and include a additional metadata\ndata = {\n    \"key\":\n        lb.UniqueId(DATA_ROW_ID),\n    \"global_key\":\n        \"NEW-ID-%id\" % uuid.uuid1(),\n    \"metadata_fields\": [\n        # New metadata\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"captureDateTime\"].uid,\n            value=\"2000-01-01 00:00:00\",\n        ),\n        # Include original metadata otherwise it will be removed\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"tag\"].uid,\n            value=\"tag_string\",\n        ),\n    ],\n}\n\ntask5 = dataset_2.upsert_data_rows([data])\ntask5.wait_till_done()\nprint(\"ERRORS: \", task5.errors)\nprint(\"RESULTS:\", task5.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a single attachment on an existing data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# You can only create one attachment at the time.\nDATA_ROW_ID = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATA_ROW_ID)\nattachment = data_row.create_attachment(\n    attachment_type=\"RAW_TEXT\", attachment_value=\"LABELERS WILL SEE THIS\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Update a recently created attachment "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "attachment.update(type=\"RAW_TEXT\", value=\"NEW RAW TEXT\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Delete"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Delete a single data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATAROW_ID_TO_DELETE = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATAROW_ID_TO_DELETE)\ndata_row.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Bulk delete data row objects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Bulk delete a list of data_rows ( limit: 4K data rows per call)\nlb.DataRow.bulk_delete(list(dataset.data_rows()))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/data_rows.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Data rows are the assets that are being labeled. We currently support the following asset types:\n",
+        "    * Image\n",
+        "    * Text\n",
+        "    * Video\n",
+        "    * Geospatial / Tiled Imagery\n",
+        "    * Audio\n",
+        "    * Documents \n",
+        "    * HTML \n",
+        "    * DICOM \n",
+        "    * Conversational\n",
+        "* A data row cannot exist without belonging to a dataset.\n",
+        "* Data rows are added to labeling tasks by first attaching them to datasets and then creating batches in projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install labelbox -q",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get data rows from projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Pick a project with batches that have data rows with global keys\nPROJECT_ID = \"<PROJECT-ID>\"\nproject = client.get_project(PROJECT_ID)\nbatches = list(project.batches())\nprint(batches)\n# This is the same as\n# -> dataset = client.get_dataset(dataset_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Fetch data rows from project's batches\n",
+        "\n",
+        "Batches will need to be exported from your project as a export parameter. Before you can export from a project you will need an ontology attached."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\n\nbatch_ids = [batch.uid for batch in batches]\n\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\":\n        batch_ids,  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Get single data row\ndata_row = data_rows[0]\nprint(data_row)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get labels from the data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Associated label(s)\", data_row[\"projects\"][project.uid][\"labels\"])\nprint(\"Global key\", data_row[\"data_row\"][\"global_key\"])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get data row ids by using global keys"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"<ENTER GLOBAL KEY>\"\ntask = client.get_data_row_ids_for_global_keys([global_key])\nprint(f\"Data row id: {task['results']}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create\n",
+        "We recommend the following methods to create data rows : `dataset.upsert_data_rows()`, and `dataset.create_data_rows()`, "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create data rows via `dataset.upsert_data_rows()`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a dataset\ndataset = client.create_dataset(name=\"data_rows_demo_dataset_6\")\n# You can also upload metadata along with your data row\nmdo = client.get_data_row_metadata_ontology()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "uploads = []\n# Generate data rows\nfor i in range(1, 8):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n        \"attachments\": [\n            {\n                \"type\":\n                    \"IMAGE_OVERLAY\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\": \"RAW_TEXT\",\n                \"value\": \"IOWA, Zone 2232, June 2022 [Text string]\",\n            },\n            {\n                \"type\":\n                    \"TEXT_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt\",\n            },\n            {\n                \"type\":\n                    \"IMAGE\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/disease_attachment.jpeg\",\n            },\n            {\n                \"type\":\n                    \"VIDEO\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4\",\n            },\n            {\n                \"type\":\n                    \"HTML\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-sample-datasets/Docs/windy.html\",\n            },\n            {\n                \"type\":\n                    \"PDF_URL\",\n                \"value\":\n                    \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n            },\n        ],\n    })\n\ntask1 = dataset.upsert_data_rows(uploads)\ntask1.wait_till_done()\nprint(\"ERRORS: \", task1.errors)\nprint(\"RESULTS:\", task1.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Create data rows from data in your local path "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "from PIL import Image\n\n# Create dummy empty jpeg file\nwidth = 400\nheight = 300\ncolor = (255, 255, 255)  # White color\nimage = Image.new(\"RGB\", (width, height), color)\n\n# Save the image as a JPEG file\nimage.save(\"dummy.jpg\")\n\nlocal_data_path = \"dummy.jpg\"\n\ndata = {\"row_data\": local_data_path, \"global_key\": str(uuid.uuid4())}\n\ntask3 = dataset.upsert_data_rows([data])\ntask3.wait_till_done()\nprint(\"ERRORS: \", task3.errors)\nprint(\"RESULTS:\", task3.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# You can mix local files with urls when creating data rows\ntask4 = dataset.upsert_data_rows([\n    {\n        \"row_data\":\n            \"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_0009.jpeg\",\n        \"global_key\":\n            str(uuid.uuid4()),\n    },\n    {\n        \"row_data\": local_data_path,\n        \"global_key\": str(uuid.uuid4())\n    },\n])\ntask4.wait_till_done()\nprint(\"ERRORS: \", task4.errors)\nprint(\"RESULTS:\", task4.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create data rows via `dataset.create_data_rows()`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset_2 = client.create_dataset(name=\"data_rows_demo_dataset_3\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "uploads = []\n# Generate data rows\nfor i in range(1, 9):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            \"TEST-ID-%id\" % uuid.uuid1(),\n        ## add metadata (optional)\n        \"metadata_fields\": [\n            lb.DataRowMetadataField(\n                schema_id=mdo.reserved_by_name[\"tag\"].\n                uid,  # specify the schema id\n                value=\"tag_string\",  # typed inputs\n            ),\n        ],\n    })\n\ntask1_2 = dataset_2.create_data_rows(uploads)\ntask1_2.wait_till_done()\nprint(\"ERRORS: \", task1_2.errors)\nprint(\"RESULTS:\", task1_2.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update\n",
+        "`dataset.upsert_data_rows()` can also be use to update data rows\n",
+        "\n",
+        "To update data rows using this method, you need to pass a `key`, which can reference either a global key or a data row ID. Additionally, include any fields that you wish to update along with their new values.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Update the global key assodicated with the DATAROW_ID or GLOBAL_KEY, and include a additional metadata\ndata = {\n    \"key\":\n        lb.UniqueId(DATA_ROW_ID),\n    \"global_key\":\n        \"NEW-ID-%id\" % uuid.uuid1(),\n    \"metadata_fields\": [\n        # New metadata\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"captureDateTime\"].uid,\n            value=\"2000-01-01 00:00:00\",\n        ),\n        # Include original metadata otherwise it will be removed\n        lb.DataRowMetadataField(\n            schema_id=mdo.reserved_by_name[\"tag\"].uid,\n            value=\"tag_string\",\n        ),\n    ],\n}\n\ntask5 = dataset_2.upsert_data_rows([data])\ntask5.wait_till_done()\nprint(\"ERRORS: \", task5.errors)\nprint(\"RESULTS:\", task5.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a single attachment on an existing data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# You can only create one attachment at the time.\nDATA_ROW_ID = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATA_ROW_ID)\nattachment = data_row.create_attachment(\n    attachment_type=\"RAW_TEXT\", attachment_value=\"LABELERS WILL SEE THIS\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Update a recently created attachment "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "attachment.update(type=\"RAW_TEXT\", value=\"NEW RAW TEXT\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Delete"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Delete a single data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATAROW_ID_TO_DELETE = \"<DATA-ROW-ID>\"\ndata_row = client.get_data_row(DATAROW_ID_TO_DELETE)\ndata_row.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Bulk delete data row objects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Bulk delete a list of data_rows ( limit: 4K data rows per call)\nlb.DataRow.bulk_delete(list(dataset.data_rows()))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/export_v1_to_v2_migration_support.ipynb b/examples/basics/export_v1_to_v2_migration_support.ipynb
deleted file mode 100644
index c56513435..000000000
--- a/examples/basics/export_v1_to_v2_migration_support.ipynb
+++ /dev/null
@@ -1,593 +0,0 @@
-{
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Export V1 to V2 migration "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "**Export V1 will no longer be available in any version of the SDK starting on April 2024**. We recommend users to plan accordingly. \n",
-                "\n",
-                "This notebook is designed to help users identify alternative V2 export methods that can serve as replacements for V1 methods."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Key changes included in export V2 methods ( ``export()`` and ``export_v2()``):\n",
-                "1. Added flexibility to only export the data that is needed. The new methods include parameters and filters to give you more granular control over your exports.\n",
-                "2. Added functionality to stream an **unlimited** number of data rows using ``export()`` (available on SDK >=3.56). Upgrading to `export()` is recommended as it is a more scalable solution.\n",
-                "\n",
-                "For complete details on how to use export V2 methods please see the [Export V2 methods](https://docs.labelbox.com/reference/label-export#export-v2-methods) documentation."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "###  Export V1 deprecated methods: \n",
-                "Project methods : \n",
-                "1. ```project.export_labels()```\n",
-                "2. ```project.label_generator()```\n",
-                "3. ```project.export_queued_data_rows()```\n",
-                "\n",
-                "Dataset methods: \n",
-                "1. ```dataset.export_data_rows()```\n",
-                "\n",
-                "Batch methods: \n",
-                "1. ```batch.export_data_rows()```\n",
-                "\n",
-                "Model methods :\n",
-                "1. ```model_run.export_labels()```\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport pprint\n\npp = pprint.PrettyPrinter(width=30, compact=True)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Exports V1 to V2 guidelines\n",
-                "The follow sections will demonstrate how to use the export V2 methods to fetch data from your projects, datasets, batches and model runs. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export labels from a project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "PROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V1 (deprecated)  \n",
-                "1. ```project.export_labels()``` \n",
-                "    - Parameters:  \n",
-                "        - ```download: bool = False```\n",
-                "        - ```timeout_seconds: int = 1800```\n",
-                "    - Output : (str | List[Dict[Any, Any]] | None)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "2. ```project.label_generator()```\n",
-                "    - Parameters: \n",
-                "        - ```timeout_seconds: int = 600```\n",
-                "    - Output: LabelGenerator"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single entry from the output of project.label_generator() (deprecated)\n# Label objects will not be deprecated.\nsingle_output_from_generator = \"\"\"\n\nLabel(\n    uid='clrf5csho2ihx07ilffgp2fzj',\n    data=ImageData(\n        im_bytes=None,\n        file_path=None,\n        url='https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg',\n        arr=None\n    ),\n    annotations=[\n        ObjectAnnotation(\n            confidence=None,\n            name='bounding_box',\n            feature_schema_id='clrf5ck4a0b9b071paa9ncu15',\n            extra={\n                'instanceURI': 'https://api.labelbox.com/masks/feature/clrf5csvi6ofm07lsf9pygwvi?token=<token>'\n                'color': '#ff0000',\n                'feature_id': 'clrf5csvi6ofm07lsf9pygwvi',\n                'value': 'bounding_box',\n                'page': None,\n                'unit': None\n            },\n            value=Rectangle(\n                extra={},\n                start=Point(extra={}, x=2096.0, y=1264.0),\n                end=Point(extra={}, x=2240.0, y=1689.0)\n            ),\n            classifications=[]\n        ),\n        # Add more annotations as needed\n        # ...\n    ],\n    extra={\n        'Created By': 'aovalle@labelbox.com',\n        'Project Name': 'Image Annotation Import Demo',\n        'Created At': '2024-01-15T16:35:59.000Z',\n        'Updated At': '2024-01-15T16:51:56.000Z',\n        'Seconds to Label': 66.0,\n        'Agreement': -1.0,\n        'Benchmark Agreement': -1.0,\n        'Benchmark ID': None,\n        'Dataset Name': 'image-demo-dataset',\n        'Reviews': [],\n        'View Label': 'https://editor.labelbox.com?project=clrf5ckex09m9070x1te223u5&label=clrf5csho2ihx07ilffgp2fzj',\n        'Has Open Issues': 0.0,\n        'Skipped': False,\n        'media_type': 'image',\n        'Data Split': None,\n        'Global Key': '2560px-Kitano_Street_Kobe01s5s41102.jpeg'\n    }\n)\n\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V2 \n",
-                "\n",
-                "For complete details on the supported filters abd parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Parameters:  \n",
-                "        - ```\"label_details\": True```\n",
-                "        - ```\"attachments\": True```\n",
-                "        - ```\"data_row_details\": True```\n",
-                "        - ```\"project_details\": True```\n",
-                "        - ```\"label_details\": True```\n",
-                "        - ```\"performance_details\": True```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Set the export parameters to only export labels\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n}\n# You also have the option to include additional filtering to narrow down the list of labels\nfilters = {}\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export queued (\"To Label\") data rows from a project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V1 (deprecated):  \n",
-                "1. ``project.export_queued_data_rows()`` :\n",
-                "    - Parameters: \n",
-                "        - ``timeout_seconds: int = 120``\n",
-                "        - ``include_metadata: bool = False``\n",
-                "    - Output: List[Dict[str, str]]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single entry from the output of project.export_queued_data_rows() (deprecated)\nsingle_output_example = \"\"\"\n[\n  {'id': 'clpouak6nap2g0783ajd1d6pf',\n 'createdAt': '2023-12-03T02:04:34.062Z',\n 'updatedAt': '2023-12-03T02:05:33.797Z',\n 'externalId': None,\n 'globalKey': 'b57c9ab2-304f-4c17-ba5f-c536f39a6a46',\n 'metadataFields': [],\n 'customMetadata': [],\n 'rowData': 'https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg',\n 'mediaAttributes': {'assetType': 'image',\n  'contentLength': 305973,\n  'height': 1333,\n  'mimeType': 'image/jpeg',\n  'subType': 'jpeg',\n  'superType': 'image',\n  'width': 2000}}\n]\n\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Parameters (Minimum required parameters):  \n",
-                "      - ```\"data_row_details\": True```\n",
-                "      - ```\"project_details\": True```\n",
-                "    - Required filters: \n",
-                "      - ``` \"workflow_status\": \"ToLabel\"```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n    \"project_details\": True,\n}\nfilters = {\n    \"workflow_status\":\n        \"ToLabel\"  ## Using this filter will only export queued data rows\n}\n\n# An ExportTask is returned, this provides additional information about the status of your task, such as\n# any errors encountered and includes additional methods to stream your data\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a Dataset "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V1 (deprecated): \n",
-                "\n",
-                "1. ```dataset.export_data_rows()```\n",
-                "  - Parameters:  \n",
-                "    - ``timeout_seconds=120``\n",
-                "    - ``include_metadata: bool = True``\n",
-                "  - Output: \n",
-                "    - Data row object generator\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single entry from the output of dataset.export_data_rows() (deprecated)\n# Data row objects will not be deprecated.\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Parameters (minimum required parameters):  \n",
-                "      - ``\"data_row_details\": True``\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a batch"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V1 (deprecated): \n",
-                "1. ```batch.export_data_rows()```\n",
-                "  - Parameters:  \n",
-                "    - ``timeout_seconds=120``\n",
-                "    - ``include_metadata: bool = True``\n",
-                "  - Output: \n",
-                "    - Data row object generator"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single output from batch.export_data_rows() method (deprecated)\n# Data row objects will not be deprecated\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Required parameters:  \n",
-                "      - ```\"data_row_details\": True```,\n",
-                "      - ```\"batch_ids\": [<batch_id>] ```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Find the batch ID by navigating to \"Batches\" -->  \"Manage batches\" --> \"Copy Batch ID\"\nBATCH_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        BATCH_ID\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a Model "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V1 (deprecated): \n",
-                "1. ```model_run.export_labels(downlaod=True)```\n",
-                "    - Parameters:  \n",
-                "        - ```download: bool = False```\n",
-                "        - ```timeout_seconds: int = 1800```\n",
-                "    - Output : (str | List[Dict[Any, Any]] | None)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Single output from model_run.export_labels()\nsingle_output_example = \"\"\"\n[\n   {'ID': '1c48a7a0-3016-48e0-b0e3-47430f974869',\n   'Data Split': 'training',\n   'DataRow ID': 'clpqdyf650xd40712pycshy6a',\n   'External ID': './resume/BANKING/99124477.pdf',\n   'Labeled Data': 'https://storage.labelbox.com/cl5bn8qvq1av907xtb3bp8q60%2F8c6afc38-42a4-b2e1-a2e3-1e3b0c2998fc-99124477.pdf?Expires=1706637969726&KeyName=labelbox-assets-key-3&Signature=2nVt3sJ21CbjGS9I64yFquUELRw',\n   'Media Attributes': {'assetType': 'pdf',\n      'contentLength': 42535,\n      'mimeType': 'application/pdf',\n      'pageCount': 3,\n      'subType': 'pdf',\n      'superType': 'application'},\n   'Label': {'objects': [{'featureId': 'b9f3b584-0f45-050a-88d4-39c2a169c8e1',\n      'schemaId': 'clq1ckwbd08jp07z91q9mch5j',\n      'title': 'Test',\n      'value': 'test',\n      'color': '#1CE6FF',\n      'data': {'location': [{'text-bbox': {'page': 1,\n            'top': 158.44,\n            'left': 58.765,\n            'height': 13.691,\n            'width': 78.261}}],\n         'unit': 'POINTS'}}],\n      'classifications': [],\n      'relationships': []}}\n   ]\n   \"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```model_run.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Required parameters:  \n",
-                "      - ```\"data_row_details\": True```\n",
-                "      - ```\"project_details\": True```\n",
-                "      - ```\"label_details\": True```\n",
-                "    - Required filters: \n",
-                "      - N/A -> Filters not supported\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "MODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n}\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a video project\n",
-                "Video projects include additional fields. Please refer to the example below to extract specific fields from video exports.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V1 (deprecated)  \n",
-                "1. ```project.export_labels()``` \n",
-                "    - Parameters:  \n",
-                "        - ```download: bool = False```\n",
-                "        - ```timeout_seconds: int = 1800```\n",
-                "    - Output : (str | List[Dict[Any, Any]] | None)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "2. ```project.video_label_generator()```\n",
-                "    - Parameters: \n",
-                "        - ```timeout_seconds: int = 600```\n",
-                "    - Output: LabelGenerator"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### Export V2\n",
-                "\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
-                "\n",
-                "1. ```project.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
-                "\n",
-                "    - Required parameters:  \n",
-                "      - ```\"attachments\": True```\n",
-                "      - ```\"data_row_details\": True```\n",
-                "      - ```\"project_details\": True```\n",
-                "      - ```\"label_details\": True```\n",
-                "      - ```\"performance_details\": True```\n",
-                "    - Output: \n",
-                "       - ```ExportTask```\n",
-                "            - `ExportTask.has_result()` return type:  bool \n",
-                "            - `ExportTask.has_errors()` return type: bool\n",
-                "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "VIDEO_PROJECT_ID = \"\"\nproject = client.get_project(VIDEO_PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"label_details\": True,\n    \"interpolated_frames\":\n        True,  # For additional information on interpolated frames please visit our documentation https://docs.labelbox.com/docs/video-annotations#video-editor-components\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch frame specific objects and frame or global classifications"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import json\nimport pprint as pp  # Assuming pp is imported from pprint module\n\nframes_objects_class_list = []\nglobal_class_list = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n        for k, v in frames_data.items():\n            frames_objects_class_list.append({k: v})\n        global_class_list.extend(dr[\"annotations\"][\"classifications\"])\n\n    print(\"------- Frame specific classifications and objects -------\")\n    pp.pprint(frames_objects_class_list)\n\n    print(\"------ Global classifications -------\")\n    pp.pprint(global_class_list)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch key frame feature map "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "keyframe_map = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"key_frame_feature_map\"]\n        for key, value in annotations.items():\n            keyframe_map.append({key: value})\n\nprint(\"----- Keyframe Feature Map -----\")\npp.pprint(keyframe_map)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Fetch segments "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "segments_map = []\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"segments\"]\n        for key, value in annotations.items():\n            segments_map.append({key: value})\n\nprint(\"----- Segments Feature Map -----\")\npp.pprint(segments_map)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
-}
\ No newline at end of file
diff --git a/examples/basics/ontologies.ipynb b/examples/basics/ontologies.ipynb
index 6937bbe9c..0058424fd 100644
--- a/examples/basics/ontologies.ipynb
+++ b/examples/basics/ontologies.ipynb
@@ -1,304 +1,302 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Ontologies\n",
-                "* An ontology is a collection different tools and classifications that can be used within a project's editor. Each tool or classification is called a \"Feature Schema\". \n",
-                "* Feature Schemas contain information about the tool such as the kind, the name, all subclasses, and other information related to a tool. Feature Schemas can be shared between ontologies. \n",
-                "\n",
-                "* Helpful Links:\n",
-                "    * [Ontology documentation](https://docs.labelbox.com/docs/labelbox-ontology)\n",
-                "    * [Project Setup Using Ontologies](https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/project_setup.ipynb)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install labelbox -q",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Ontology From Normalized Data\n",
-                "* Users can create ontologies from a json definition of the ontology.\n",
-                "* See below `OntologyBuilder` section for more details on constructing the normalized ontology.\n",
-                "* Each tool type requires a specific value be passed:\n",
-                "\n",
-                "| Tool    | Value |\n",
-                "| :----------- | :----------- |\n",
-                "| Bounding box      | rectangle    |\n",
-                "| Polygon           | polygon      |\n",
-                "| Polyline          | line         |\n",
-                "| Point             | point        |\n",
-                "| Segmentation mask | raster-segmentation  |\n",
-                "| Entity            | named-entity |\n",
-                "| Relationship      | edge         |"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# This will automatically create new feature schema\nontology_name = \"sdk-ontology\"\nfeature_schema_cat_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"cat\",\n    \"color\": \"black\",\n}\n\nontology_normalized_json = {\n    \"tools\": [feature_schema_cat_normalized],\n    \"classifications\": [],\n}\nontology = client.create_ontology(name=ontology_name,\n                                  normalized=ontology_normalized_json)\nprint(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Ontology From Existing Feature Schemas\n",
-                "* It is often useful to support the same features in multiple ontologies. \n",
-                "* Labelbox supports this workflow by allowing users to create ontologies using existing feature schemas."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First create the feature schema\nfeature_schema_cat = client.create_feature_schema(feature_schema_cat_normalized)\n# When we create the ontology it will not re-create the feature schema\nprint(feature_schema_cat.uid)\nontology = client.create_ontology_from_feature_schemas(ontology_name,\n                                                       [feature_schema_cat.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Ontology From a Mix of New and Existing Feature Schemas\n",
-                "* If we want to create a new ontology that expands upon a previous ontology it is helpful to be able to share a portion of the features.\n",
-                "* To do this we will create the new schema ids that we want. Then we will create an ontology from the new list of ids.\n",
-                "* Note that for additional customization you can also combine the normalized json and use the create_ontology() method (not covered here)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create new dog schema id\nfeature_schema_dog_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"dog\",\n    \"color\": \"black\",\n    \"classifications\": [],\n}\nfeature_schema_dog = client.create_feature_schema(feature_schema_dog_normalized)\n# The cat is shared between this new ontology and the one we created previously\n# (ie. the cat feature schema will not be re-created)\nontology = client.create_ontology_from_feature_schemas(\n    ontology_name, [feature_schema_cat.uid, feature_schema_dog.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Read\n",
-                "* We can directly query by id for ontologies and feature schemas\n",
-                "* We also can search for both by name"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "#### Fetch by ID\nfeature_schema = client.get_feature_schema(feature_schema_cat.uid)\nontology = client.get_ontology(ontology.uid)\nprint(feature_schema)\nprint(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "#### Search by name\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nontology = next(client.get_ontologies(ontology_name))\nprint(feature_schema)\nprint(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update and Delete"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Check if feature is archived\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nclient.is_feature_schema_archived(ontology_id=ontology.uid,\n                                  feature_schema_id=feature_schema.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Update a feature's title\nclient.update_feature_schema_title(feature_schema_id=feature_schema.uid,\n                                   title=\"cat-2\")\nfeature = client.get_feature_schema(feature_schema_id=feature_schema.uid)\nprint(\"Feature: \", feature)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Replace a feature\ntool = lb.Tool(\n    feature_schema_id=feature_schema.uid,\n    name=\"tool-cat-upserted\",\n    tool=lb.Tool.Type.BBOX,\n    color=\"#FF0000\",\n)\nupserted_feature_schema_id = client.upsert_feature_schema(tool.asdict()).uid\nfeature = client.get_feature_schema(\n    feature_schema_id=upserted_feature_schema_id)\nprint(\"Updated feature: \", feature)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Insert a new feature\ntool = lb.Tool(name=\"tool-cat-2\", tool=lb.Tool.Type.RASTER_SEGMENTATION)\nfeature_schema_id_new = client.create_feature_schema(tool.asdict()).uid\nclient.insert_feature_schema_into_ontology(\n    feature_schema_id=feature_schema_id_new,\n    ontology_id=ontology.uid,\n    position=2,\n)\nprint(\"Updated ontology: \", client.get_ontology(ontology_id=ontology.uid))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Delete or Archived a feature:\n",
-                "\n",
-                "If the feature schema is a root level node with associated labels, it will be archived.\n",
-                "If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
-                "If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.delete_feature_schema_from_ontology(\n    ontology_id=ontology.uid, feature_schema_id=feature_schema_id_new)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Only features with annotations will be archived, features without annotations will be deleted.\nfeature_schema_id_with_annotations = \"\"\nontology_id = \"\"\nclient.unarchive_feature_schema_node(\n    ontology_id=ontology_id,\n    root_feature_schema_id=feature_schema_id_with_annotations,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Ontology Builder\n",
-                "* The ontology builder is a tool for creating and modifying normalized json"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create normalized json with a bounding box and segmentation tool\nontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n    lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\"),\n])\n# Creating an ontology from this is easy\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Alternative syntax for defining the ontology via the OntologyBuilder"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create\nontology_builder = lb.OntologyBuilder()\n# Append tools\ntool_dog = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\")\ntool_cat = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\")\nontology_builder.add_tool(tool_dog)\nontology_builder.add_tool(tool_cat)\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Classifications are supported too (Both for top level and as subclassifications)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"cat\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"name\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"image_quality\",\n            options=[lb.Option(value=\"clear\"),\n                     lb.Option(value=\"blurry\")],\n        )\n    ],\n)\nprint(json.dumps(ontology_builder.asdict(), indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Example of how to add sub-classfication within an option"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder()\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"Global classification\",\n    options=[\n        lb.Option(\n            \"1st option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 1st option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n        lb.Option(\n            \"2nd option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 2nd option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n    ],\n)\n\nontology_builder.add_classification(radio_classification)\n\nontology = client.create_ontology(\"example of nested classification\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* All Tool objects are constructed the same way:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "bbox_tool = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog_box\")\npoly_tool = lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"dog_poly\")\nseg_tool = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"dog_seg\")\npoint_tool = lb.Tool(tool=lb.Tool.Type.POINT, name=\"dog_center\")\nline_tool = lb.Tool(tool=lb.Tool.Type.LINE, name=\"dog_orientation\")\nner_tool = lb.Tool(tool=lb.Tool.Type.NER, name=\"dog_reference\")\nrelationship_tool = lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Classifications are all constructed the same way (except text which doesn't require options)\n",
-                "* Classifications can be global or subclasses to a tool (ie dog bounding box, with a breed classification)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                        name=\"dog_name\")\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"dog_breed\",\n    options=[lb.Option(\"poodle\")],\n)\nchecklist_classification = lb.Classification(\n    class_type=lb.Classification.Type.CHECKLIST,\n    name=\"background\",\n    options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")],\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/ontologies.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Ontologies\n",
+        "* An ontology is a collection different tools and classifications that can be used within a project's editor. Each tool or classification is called a \"Feature Schema\". \n",
+        "* Feature Schemas contain information about the tool such as the kind, the name, all subclasses, and other information related to a tool. Feature Schemas can be shared between ontologies. \n",
+        "\n",
+        "* Helpful Links:\n",
+        "    * [Ontology documentation](https://docs.labelbox.com/docs/labelbox-ontology)\n",
+        "    * [Project Setup Using Ontologies](https://github.com/Labelbox/labelbox-python/blob/master/examples/project_configuration/project_setup.ipynb)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install labelbox -q",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Ontology From Normalized Data\n",
+        "* Users can create ontologies from a json definition of the ontology.\n",
+        "* See below `OntologyBuilder` section for more details on constructing the normalized ontology.\n",
+        "* Each tool type requires a specific value be passed:\n",
+        "\n",
+        "| Tool    | Value |\n",
+        "| :----------- | :----------- |\n",
+        "| Bounding box      | rectangle    |\n",
+        "| Polygon           | polygon      |\n",
+        "| Polyline          | line         |\n",
+        "| Point             | point        |\n",
+        "| Segmentation mask | raster-segmentation  |\n",
+        "| Entity            | named-entity |\n",
+        "| Relationship      | edge         |"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# This will automatically create new feature schema\nontology_name = \"sdk-ontology\"\nfeature_schema_cat_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"cat\",\n    \"color\": \"black\",\n}\n\nontology_normalized_json = {\n    \"tools\": [feature_schema_cat_normalized],\n    \"classifications\": [],\n}\nontology = client.create_ontology(name=ontology_name,\n                                  normalized=ontology_normalized_json)\nprint(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Ontology From Existing Feature Schemas\n",
+        "* It is often useful to support the same features in multiple ontologies. \n",
+        "* Labelbox supports this workflow by allowing users to create ontologies using existing feature schemas."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First create the feature schema\nfeature_schema_cat = client.create_feature_schema(feature_schema_cat_normalized)\n# When we create the ontology it will not re-create the feature schema\nprint(feature_schema_cat.uid)\nontology = client.create_ontology_from_feature_schemas(ontology_name,\n                                                       [feature_schema_cat.uid])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Ontology From a Mix of New and Existing Feature Schemas\n",
+        "* If we want to create a new ontology that expands upon a previous ontology it is helpful to be able to share a portion of the features.\n",
+        "* To do this we will create the new schema ids that we want. Then we will create an ontology from the new list of ids.\n",
+        "* Note that for additional customization you can also combine the normalized json and use the create_ontology() method (not covered here)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create new dog schema id\nfeature_schema_dog_normalized = {\n    \"tool\": \"polygon\",\n    \"name\": \"dog\",\n    \"color\": \"black\",\n    \"classifications\": [],\n}\nfeature_schema_dog = client.create_feature_schema(feature_schema_dog_normalized)\n# The cat is shared between this new ontology and the one we created previously\n# (ie. the cat feature schema will not be re-created)\nontology = client.create_ontology_from_feature_schemas(\n    ontology_name, [feature_schema_cat.uid, feature_schema_dog.uid])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Read\n",
+        "* We can directly query by id for ontologies and feature schemas\n",
+        "* We also can search for both by name"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "#### Fetch by ID\nfeature_schema = client.get_feature_schema(feature_schema_cat.uid)\nontology = client.get_ontology(ontology.uid)\nprint(feature_schema)\nprint(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "#### Search by name\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nontology = next(client.get_ontologies(ontology_name))\nprint(feature_schema)\nprint(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update and Delete"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Check if feature is archived\nfeature_schema = next(client.get_feature_schemas(\"cat\"))\nclient.is_feature_schema_archived(ontology_id=ontology.uid,\n                                  feature_schema_id=feature_schema.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Update a feature's title\nclient.update_feature_schema_title(feature_schema_id=feature_schema.uid,\n                                   title=\"cat-2\")\nfeature = client.get_feature_schema(feature_schema_id=feature_schema.uid)\nprint(\"Feature: \", feature)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Replace a feature\ntool = lb.Tool(\n    feature_schema_id=feature_schema.uid,\n    name=\"tool-cat-upserted\",\n    tool=lb.Tool.Type.BBOX,\n    color=\"#FF0000\",\n)\nupserted_feature_schema_id = client.upsert_feature_schema(tool.asdict()).uid\nfeature = client.get_feature_schema(\n    feature_schema_id=upserted_feature_schema_id)\nprint(\"Updated feature: \", feature)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Insert a new feature\ntool = lb.Tool(name=\"tool-cat-2\", tool=lb.Tool.Type.RASTER_SEGMENTATION)\nfeature_schema_id_new = client.create_feature_schema(tool.asdict()).uid\nclient.insert_feature_schema_into_ontology(\n    feature_schema_id=feature_schema_id_new,\n    ontology_id=ontology.uid,\n    position=2,\n)\nprint(\"Updated ontology: \", client.get_ontology(ontology_id=ontology.uid))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Delete or Archived a feature:\n",
+        "\n",
+        "If the feature schema is a root level node with associated labels, it will be archived.\n",
+        "If the feature schema is a nested node in the ontology and does not have associated labels, it will be deleted.\n",
+        "If the feature schema is a nested node in the ontology and has associated labels, it will not be deleted."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.delete_feature_schema_from_ontology(\n    ontology_id=ontology.uid, feature_schema_id=feature_schema_id_new)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Only features with annotations will be archived, features without annotations will be deleted.\nfeature_schema_id_with_annotations = \"\"\nontology_id = \"\"\nclient.unarchive_feature_schema_node(\n    ontology_id=ontology_id,\n    root_feature_schema_id=feature_schema_id_with_annotations,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Ontology Builder\n",
+        "* The ontology builder is a tool for creating and modifying normalized json"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create normalized json with a bounding box and segmentation tool\nontology_builder = lb.OntologyBuilder(tools=[\n    lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n    lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\"),\n])\n# Creating an ontology from this is easy\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Alternative syntax for defining the ontology via the OntologyBuilder"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create\nontology_builder = lb.OntologyBuilder()\n# Append tools\ntool_dog = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\")\ntool_cat = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"cat\")\nontology_builder.add_tool(tool_dog)\nontology_builder.add_tool(tool_cat)\nontology = client.create_ontology(\"ontology-builder-ontology\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Classifications are supported too (Both for top level and as subclassifications)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"cat\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"name\")\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"image_quality\",\n            options=[lb.Option(value=\"clear\"),\n                     lb.Option(value=\"blurry\")],\n        )\n    ],\n)\nprint(json.dumps(ontology_builder.asdict(), indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Example of how to add sub-classfication within an option"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# We will use add_classification to add this classification to a previously built ontology_builder or you can create new ontology_builder = OntologyBuilder()\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"Global classification\",\n    options=[\n        lb.Option(\n            \"1st option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 1st option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n        lb.Option(\n            \"2nd option\",\n            options=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"Inside 2nd option\",\n                    options=[lb.Option(\"Option A\"),\n                             lb.Option(\"Option B\")],\n                )\n            ],\n        ),\n    ],\n)\n\nontology_builder.add_classification(radio_classification)\n\nontology = client.create_ontology(\"example of nested classification\",\n                                  ontology_builder.asdict())\nprint(json.dumps(ontology.normalized, indent=2))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* All Tool objects are constructed the same way:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "bbox_tool = lb.Tool(tool=lb.Tool.Type.BBOX, name=\"dog_box\")\npoly_tool = lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"dog_poly\")\nseg_tool = lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"dog_seg\")\npoint_tool = lb.Tool(tool=lb.Tool.Type.POINT, name=\"dog_center\")\nline_tool = lb.Tool(tool=lb.Tool.Type.LINE, name=\"dog_orientation\")\nner_tool = lb.Tool(tool=lb.Tool.Type.NER, name=\"dog_reference\")\nrelationship_tool = lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Classifications are all constructed the same way (except text which doesn't require options)\n",
+        "* Classifications can be global or subclasses to a tool (ie dog bounding box, with a breed classification)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "text_classification = lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                        name=\"dog_name\")\nradio_classification = lb.Classification(\n    class_type=lb.Classification.Type.RADIO,\n    name=\"dog_breed\",\n    options=[lb.Option(\"poodle\")],\n)\nchecklist_classification = lb.Classification(\n    class_type=lb.Classification.Type.CHECKLIST,\n    name=\"background\",\n    options=[lb.Option(\"at_park\"), lb.Option(\"has_leash\")],\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/projects.ipynb b/examples/basics/projects.ipynb
index 53dbb3bc0..3bc3d8cb9 100644
--- a/examples/basics/projects.ipynb
+++ b/examples/basics/projects.ipynb
@@ -1,343 +1,341 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* A project can be thought of as a specific labeling task on a set of labels\n",
-                "* That set of labels is defined by the data rows attached to the project\n",
-                "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
-                "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
-                "* Also note that deprecated functions are not explained here."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a project\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create a dataset with data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Add data rows to a project \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create tags and assign them to a project\n",
-                "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create a tag"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Assign the tag to a project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tags = project.update_project_resource_tags([tag.uid])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get project tags"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tags = project.get_resource_tags()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Attach ontology and label data rows\n",
-                "\n",
-                "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create your ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Attach ontology to project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create labels and upload them to project as ground truths"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Move data rows in project to different task queues"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Fetch project configuration"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Return number of labeled data rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Number of labels:\", project.get_label_count())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Copy labels and data rows from one project to a different project\n",
-                "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Copy data rows and labels\n",
-                "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
-                "\n",
-                "##### Parameters\n",
-                "\n",
-                "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
-                "\n",
-                "* `source_project_id`\n",
-                "    - The id of the project were our data rows with labels will originate.\n",
-                "* `annotation_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Clean Up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/projects.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* A project can be thought of as a specific labeling task on a set of labels\n",
+        "* That set of labels is defined by the data rows attached to the project\n",
+        "* Each project has an ontology which defines the types of annotations supported during the labeling process\n",
+        "**Note that there is a lot of advanced usage that is not covered in this notebook. See examples/project_configuration/project_setup.ipynb for those functions**\n",
+        "* Also note that deprecated functions are not explained here."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a project\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Creates an empty project\nproject = client.create_project(\n    name=\"my-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create a dataset with data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.create_dataset(name=\"project-demo-dataset\")\nglobal_keys = []\nuploads = []\n# Generate data rows\nfor i in range(1, 9):\n    gb_key = \"TEST-ID-%id\" % uuid.uuid1()\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            gb_key,\n    })\n    global_keys.append(gb_key)\n\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Add data rows to a project \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"project-demo\",  # each batch in a project must have a unique name\n    global_keys=\n    global_keys,  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create tags and assign them to a project\n",
+        "In this section, we are creating a tag in the ontology and associating it with a project. Then we are listing the tags attached to a project.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create a tag"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get the organization\norganization = client.get_organization()\n\ntag = organization.create_resource_tag({\n    \"text\": \"new-tag-name\",\n    \"color\": \"4ed2f9\"\n})",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Assign the tag to a project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tags = project.update_project_resource_tags([tag.uid])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Get project tags"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tags = project.get_resource_tags()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Attach ontology and label data rows\n",
+        "\n",
+        "In this section, we are creating an ontology to attach to a project and creating labels to import as ground truths. We need this setup to demonstrate other methods later in the demo. For more information, please reference our [Ontology](https://docs.labelbox.com/reference/ontology) and [Import Image Annotation](https://docs.labelbox.com/reference/import-image-annotations) development guides."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create your ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create normalized json with a radio classification\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n    ])\n# Creating an ontology\nontology = client.create_ontology(\"test-ontology\", ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Attach ontology to project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create labels and upload them to project as ground truths"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create labels\nlabels = []\nfor global_key in global_keys:\n    labels.append(\n        lb_types.Label(\n            data={\"global_key\": global_key},\n            annotations=[\n                # Create radio classification annotation for labels\n                lb_types.ClassificationAnnotation(\n                    name=\"radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"second_radio_answer\")),\n                )\n            ],\n        ))\n\n# Upload labels for the data rows in project\nupload_job = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Move data rows in project to different task queues"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get list of task queues for project\ntask_queues = project.task_queues()\n\nfor task_queue in task_queues:\n    print(task_queue)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "project.move_data_rows_to_task_queue(\n    data_row_ids=lb.GlobalKeys(global_keys),  # Provide a list of global keys\n    task_queue_id=task_queues[2].\n    uid,  # Passing None moves data rows to \"Done\" task queue\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Fetch project configuration"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Note the project is not fully setup many of the fields will be empty.\nprint(\"Project is not setup yet:\", project.setup_complete is None)\nprint(\"Project name:\", project.name)\nprint(\"Project description:\", project.description)\nprint(\"Media Type:\", project.media_type)\nbatches = [b for b in project.batches()]\nprint(\"Project Batches\", batches)\nprint(\"Ontology:\", project.ontology())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Return number of labeled data rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Number of labels:\", project.get_label_count())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Copy labels and data rows from one project to a different project\n",
+        "In the below steps we will be copying data rows with their corresponding labels from one project to a different project with a similar ontology. First, we must set up a new project with a ontology that matches the tooling of our source project ontology."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create an empty destination project\ndestination_project = client.create_project(\n    name=\"destination-test-project\",\n    description=\"a description\",\n    media_type=lb.MediaType.Image,\n)\n\n# Create ontology and attach to destination project\ndestination_ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"destination_radio_question\",\n            options=[\n                lb.Option(value=\"destination_first_radio_answer\"),\n                lb.Option(value=\"destination_second_radio_answer\"),\n            ],\n        ),\n    ])\n\ndestination_ontology = client.create_ontology(\"dest-test-ontology\",\n                                              ontology_builder.asdict())\n\ndestination_project.setup_editor(destination_ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Copy data rows and labels\n",
+        "To copy our data rows and labels to our project from a source project we will be using the `send_to_annotate_from_catalog` method with our Labelbox client.\n",
+        "\n",
+        "##### Parameters\n",
+        "\n",
+        "When you send data rows with labels to our destination project, you may choose to include or exclude certain parameters, at a minimum a `source_project_id` will need to be provided:\n",
+        "\n",
+        "* `source_project_id`\n",
+        "    - The id of the project were our data rows with labels will originate.\n",
+        "* `annotation_ontology_mapping`\n",
+        "    - A dictionary containing the mapping of the source project's ontology feature schema ids to the destination project's ontology feature schema ids. If left empty only the data rows will be sent to our destination project with no labels.\n",
+        "* `exclude_data_rows_in_project`\n",
+        "    - Excludes data rows that are already in the project. \n",
+        "* `override_existing_annotations_rule` \n",
+        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming labels from the source project. \n",
+        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+        "        * Options include:\n",
+        "            * ConflictResolutionStrategy.KeepExisting\n",
+        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+        "* `param batch_priority`\n",
+        "    - The priority of the batch."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get ontology dictionary to obtain featureSchemaIds\nsource_ontology_normalized = ontology.normalized\ndestination_ontology_normalized = destination_ontology.normalized\n\nANNOTATION_ONTOLOGY_MAPPING = {\n    source_ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    source_ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    source_ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        destination_ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "send_to_annotate_params = {\n    \"source_project_id\":\n        project.uid,\n    \"annotations_ontology_mapping\":\n        ANNOTATION_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Get task id to workflow you want to send data rows. If sent to initial labeling queue, labels will be pre-labels.\nqueue_id = [\n    queue.uid\n    for queue in destination_project.task_queues()\n    if queue.queue_type == \"MANUAL_REVIEW_QUEUE\"\n][0]\n\ntask = client.send_to_annotate_from_catalog(\n    destination_project_id=destination_project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to send data rows with labels to the Done queue.\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        global_keys  # Provide a list of global keys from source project\n    ),\n    params=send_to_annotate_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Clean Up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# destination_project.delete()\n# dataset.delete()\n# client.delete_unused_ontology(destination_ontology.uid)\n# client.delete_unused_ontology(ontology.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/basics/user_management.ipynb b/examples/basics/user_management.ipynb
index 5e028e049..ffe656406 100644
--- a/examples/basics/user_management.ipynb
+++ b/examples/basics/user_management.ipynb
@@ -1,248 +1,246 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# User Management\n",
-                "* This notebook covers the following:\n",
-                "    * create invites\n",
-                "    * query for remaining allowed invites to an organization\n",
-                "    * set and update organization roles\n",
-                "    * assign users to projects\n",
-                "        * set / update / revoke project role\n",
-                "    * delete users from org"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport os",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* You have to specifically enable experimental features to use this functionality. Notice the \n",
-                "`enable_experimental = True`\n",
-                "    * enables users to send invites and checking the number of seats available via the sdk"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Roles\n",
-                "* When inviting a new user to an organization, there are various roles to select from.\n",
-                "* All available roles to your org can be accessed via `client.get_roles()`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Above we printed out all of the roles available to the current org.\n",
-                "* Notice the `NONE`. That is for project level roles"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create\n",
-                "* Users are created by sending an invite\n",
-                "* An email will be sent to them and they will be asked to join your organization"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Organization Level Permissions\n",
-                "* Invite a new labeler with labeling permissions on all projects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Project Level Permissions\n",
-                "* Invite a new labeler with labeling permissions specific to a set of projects\n",
-                "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Read\n",
-                "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
-                "* You are able to query for members once they have joined."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "users = list(organization.users())\nprint(users[0])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update\n",
-                "* There is no update on invites. Instead you must delete and resend them\n",
-                "* You can update User roles"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Delete"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Invites can only be deleted from the ui at this time. \n",
-                "* Deleting invites can be done in the members tab of the web app."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Delete the User\n",
-                "* Make sure you want to remove the user from the org:\n",
-                "* `>>> organization.remove_user(user)`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Cleanup\n",
-                "* We created an extra project. Let's delete it"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/basics/user_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# User Management\n",
+        "* This notebook covers the following:\n",
+        "    * create invites\n",
+        "    * query for remaining allowed invites to an organization\n",
+        "    * set and update organization roles\n",
+        "    * assign users to projects\n",
+        "        * set / update / revoke project role\n",
+        "    * delete users from org"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport os",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* You have to specifically enable experimental features to use this functionality. Notice the \n",
+        "`enable_experimental = True`\n",
+        "    * enables users to send invites and checking the number of seats available via the sdk"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = None\nclient = lb.Client(api_key=API_KEY, enable_experimental=True)\norganization = client.get_organization()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Please provide a dummy email here:\n# Preferrably one you can access. If you have a google account you can do email+1@<domain>.com\nDUMMY_EMAIL = \"SET THIS\"\n# This should be set to an account that you wan't to change the permissions for.\n# You could invite a new user, accept the invite and use that account if you don't want to effect any active users\nDUMMY_USER_ACCOUNT_ID = \"ckneh4n8c9qvq0706uwwg5i16\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Roles\n",
+        "* When inviting a new user to an organization, there are various roles to select from.\n",
+        "* All available roles to your org can be accessed via `client.get_roles()`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "roles = client.get_roles()\nfor name, role in roles.items():\n    print(role.name, \":\", role.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Above we printed out all of the roles available to the current org.\n",
+        "* Notice the `NONE`. That is for project level roles"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create\n",
+        "* Users are created by sending an invite\n",
+        "* An email will be sent to them and they will be asked to join your organization"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Organization Level Permissions\n",
+        "* Invite a new labeler with labeling permissions on all projects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# First make sure that you have enough seats:\norganization.invite_limit()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "invite = organization.invite_user(DUMMY_EMAIL, roles[\"LABELER\"])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(invite.created_at)\nprint(invite.organization_role_name)\nprint(invite.email)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Project Level Permissions\n",
+        "* Invite a new labeler with labeling permissions specific to a set of projects\n",
+        "* Here we set organization level permissions to Roles.NONE to indicate that the user only has project level permissions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"test_user_management\",\n                                media_type=lb.MediaType.Image)\nproject_role = lb.ProjectRole(project=project, role=roles[\"REVIEWER\"])\ninvite = organization.invite_user(DUMMY_EMAIL,\n                                  roles[\"NONE\"],\n                                  project_roles=[project_role])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Read\n",
+        "* Outstanding invites cannot be queried for at this time. This information can be found in the members tab of the web app.\n",
+        "* You are able to query for members once they have joined."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "users = list(organization.users())\nprint(users[0])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update\n",
+        "* There is no update on invites. Instead you must delete and resend them\n",
+        "* You can update User roles"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "user = client._get_single(lb.User, DUMMY_USER_ACCOUNT_ID)\n\n# Give the user organization level permissions\nuser.update_org_role(roles[\"LABELER\"])\nprint(user.org_role())\n# Restore project level permissions\nuser.update_org_role(roles[\"NONE\"])\nprint(user.org_role())\n# Make the user a labeler for the current project\nuser.upsert_project_role(project, roles[\"LABELER\"])\nprint(user.org_role())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Remove the user from a project (Same as setting the project role to `roles.NONE`)\nuser.remove_from_project(project)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Delete"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Invites can only be deleted from the ui at this time. \n",
+        "* Deleting invites can be done in the members tab of the web app."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Delete the User\n",
+        "* Make sure you want to remove the user from the org:\n",
+        "* `>>> organization.remove_user(user)`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Cleanup\n",
+        "* We created an extra project. Let's delete it"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/exports/composite_mask_export.ipynb b/examples/exports/composite_mask_export.ipynb
index 9b37c4305..ced50a4ea 100644
--- a/examples/exports/composite_mask_export.ipynb
+++ b/examples/exports/composite_mask_export.ipynb
@@ -1,257 +1,255 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Export composite masks \n",
-                "##### **Composite masks are only available on raster segmentation projects**\n",
-                "\n",
-                "Composite masks are a combination of mask instances grouped in a single mask URL. \n",
-                "\n",
-                "The purpose of this demo is to demonstrate how to transition from exporting single masks to exporting composite masks. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Key differences between single mask instance and composite mask."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Composite masks\n",
-                "**IMPORTANT :** The URL for the ```composite_mask``` from exports older than 30 days will no longer be accessible. To obtain a functional URL after this period, please generate a new export for the mask.\n",
-                "1. A single mask url contains all mask instances from a single label. For videos a composite mask contains all mask instances for the frame in each label. \n",
-                "2. The export and mask URL adhere to the following convention:\n",
-                "  - ***Image example***\n",
-                "```json \n",
-                "  {\n",
-                "    \"composite_mask\": {\n",
-                "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
-                "      \"color_rgb\": [\n",
-                "          142,\n",
-                "          220,\n",
-                "          196\n",
-                "      ]\n",
-                "    }\n",
-                "  }\n",
-                "```\n",
-                "  - ***Video example*** :\n",
-                "  The export will adhere to the following URL convention by default.\n",
-                "```json\n",
-                "  {\n",
-                "    \"composite_mask\": {\n",
-                "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/{frame_number}\",\n",
-                "      \"color_rgb\": [\n",
-                "        224,\n",
-                "        17,\n",
-                "        103\n",
-                "      ]\n",
-                "    }\n",
-                "  }\n",
-                "```\n",
-                "3. A unique RGB color is assigned to each mask instance. The example below shows a composite mask of a label, and while it contains all mask instances, only the RGB color associated with this particular annotation will be filled in under the ```color_rgb``` field."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Example on how to fetch a composite mask\n# The mask here shows all the mask instances associated with a label\ntask_id = \"\"\ncomposite_mask_id = \"\"\n\nmask_url = f\"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\"\nreq = urllib.request.Request(mask_url, headers=client.headers)\nimage = Image.open(urllib.request.urlopen(req))\nw, h = image.size\nnew_w = w // 4\nnew_h = h // 4\n\nimage.resize((new_w, new_h), Image.BICUBIC)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Here's an example of an entry featuring a composite mask (see image above) containing the mask instance's RGB color uniquely associated with the annotation.\n",
-                "\n",
-                "```json\n",
-                "    {\n",
-                "      \"feature_id\": \"clpk3ow9u006f14vs2w5qa9l3\",\n",
-                "      \"feature_schema_id\": \"clpk3nvrv05bh08ua8fwqavng\",\n",
-                "      \"name\": \"mask\",\n",
-                "      \"value\": \"mask\",\n",
-                "      \"annotation_kind\": \"ImageSegmentationMask\",\n",
-                "      \"classifications\": [],\n",
-                "      \"composite_mask\": {\n",
-                "          \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
-                "          \"color_rgb\": [\n",
-                "              123,\n",
-                "              103,\n",
-                "              152\n",
-                "          ]\n",
-                "      }\n",
-                "    }\n",
-                "```\n",
-                "- rgb(123,103,152) = Purple\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "---"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Single mask instance:\n",
-                "We are planning on removing single mask instances, but for now they will be displayed adjecent to the composite mask.\n",
-                "1. A single mask instance and mask url is generated for each individual annotation for each label.\n",
-                "2. The export and mask URL adhere to the following convention:  \n",
-                "```json\n",
-                "      {\n",
-                "        \"mask\": {\n",
-                "              \"url\": \"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
-                "          }\n",
-                "      }\n",
-                "\n",
-                "```\n",
-                "3. RGB color is not present"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create an export from an Image project with mask annotations\n",
-                "To better showcase composite masks, make sure you have different mask tools and mask annotations in your project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.has_result():\n    print(export_task.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "stream = export_task.get_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json_str)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create an export from a Video project with mask annotations "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "VIDEO_PROJECT_ID = \"\"\nproject_video = client.get_project(VIDEO_PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task_video = project_video.export(params=export_params, filters=filters)\nexport_task_video.wait_till_done()\n\nif export_task_video.has_result():\n    print(export_task_video.result)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool from each frame"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tools_frames_color = {}\nstream = export_task_video.get_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json_str)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/composite_mask_export.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Export composite masks \n",
+        "##### **Composite masks are only available on raster segmentation projects**\n",
+        "\n",
+        "Composite masks are a combination of mask instances grouped in a single mask URL. \n",
+        "\n",
+        "The purpose of this demo is to demonstrate how to transition from exporting single masks to exporting composite masks. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Key differences between single mask instance and composite mask."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Composite masks\n",
+        "**IMPORTANT :** The URL for the ```composite_mask``` from exports older than 30 days will no longer be accessible. To obtain a functional URL after this period, please generate a new export for the mask.\n",
+        "1. A single mask url contains all mask instances from a single label. For videos a composite mask contains all mask instances for the frame in each label. \n",
+        "2. The export and mask URL adhere to the following convention:\n",
+        "  - ***Image example***\n",
+        "```json \n",
+        "  {\n",
+        "    \"composite_mask\": {\n",
+        "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
+        "      \"color_rgb\": [\n",
+        "          142,\n",
+        "          220,\n",
+        "          196\n",
+        "      ]\n",
+        "    }\n",
+        "  }\n",
+        "```\n",
+        "  - ***Video example*** :\n",
+        "  The export will adhere to the following URL convention by default.\n",
+        "```json\n",
+        "  {\n",
+        "    \"composite_mask\": {\n",
+        "      \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/{frame_number}\",\n",
+        "      \"color_rgb\": [\n",
+        "        224,\n",
+        "        17,\n",
+        "        103\n",
+        "      ]\n",
+        "    }\n",
+        "  }\n",
+        "```\n",
+        "3. A unique RGB color is assigned to each mask instance. The example below shows a composite mask of a label, and while it contains all mask instances, only the RGB color associated with this particular annotation will be filled in under the ```color_rgb``` field."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Example on how to fetch a composite mask\n# The mask here shows all the mask instances associated with a label\ntask_id = \"\"\ncomposite_mask_id = \"\"\n\nmask_url = f\"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\"\nreq = urllib.request.Request(mask_url, headers=client.headers)\nimage = Image.open(urllib.request.urlopen(req))\nw, h = image.size\nnew_w = w // 4\nnew_h = h // 4\n\nimage.resize((new_w, new_h), Image.BICUBIC)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Here's an example of an entry featuring a composite mask (see image above) containing the mask instance's RGB color uniquely associated with the annotation.\n",
+        "\n",
+        "```json\n",
+        "    {\n",
+        "      \"feature_id\": \"clpk3ow9u006f14vs2w5qa9l3\",\n",
+        "      \"feature_schema_id\": \"clpk3nvrv05bh08ua8fwqavng\",\n",
+        "      \"name\": \"mask\",\n",
+        "      \"value\": \"mask\",\n",
+        "      \"annotation_kind\": \"ImageSegmentationMask\",\n",
+        "      \"classifications\": [],\n",
+        "      \"composite_mask\": {\n",
+        "          \"url\": \"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1\",\n",
+        "          \"color_rgb\": [\n",
+        "              123,\n",
+        "              103,\n",
+        "              152\n",
+        "          ]\n",
+        "      }\n",
+        "    }\n",
+        "```\n",
+        "- rgb(123,103,152) = Purple\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "---"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Single mask instance:\n",
+        "We are planning on removing single mask instances, but for now they will be displayed adjecent to the composite mask.\n",
+        "1. A single mask instance and mask url is generated for each individual annotation for each label.\n",
+        "2. The export and mask URL adhere to the following convention:  \n",
+        "```json\n",
+        "      {\n",
+        "        \"mask\": {\n",
+        "              \"url\": \"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask\"\n",
+        "          }\n",
+        "      }\n",
+        "\n",
+        "```\n",
+        "3. RGB color is not present"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create an export from an Image project with mask annotations\n",
+        "To better showcase composite masks, make sure you have different mask tools and mask annotations in your project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.has_result():\n    print(export_task.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "stream = export_task.get_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json_str)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create an export from a Video project with mask annotations "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "VIDEO_PROJECT_ID = \"\"\nproject_video = client.get_project(VIDEO_PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n}\n\nfilters = {}\n\n# export() is the streamable option of exports V2, for more information please visit our documentation:\n# https://docs.labelbox.com/reference/label-export#export-v2-methods\n\nexport_task_video = project_video.export(params=export_params, filters=filters)\nexport_task_video.wait_till_done()\n\nif export_task_video.has_result():\n    print(export_task_video.result)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Get all the ```color_rgb``` associated with annotations that are using a specific mask tool from each frame"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tools_frames_color = {}\nstream = export_task_video.get_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json_str)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/exports/export_data.ipynb b/examples/exports/export_data.ipynb
index 0d897709c..e0fa8f728 100644
--- a/examples/exports/export_data.ipynb
+++ b/examples/exports/export_data.ipynb
@@ -1,479 +1,477 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Export data\n",
-                "How to export data for projects, datasets, slices, data rows and models, with examples for each type of v2 export along with details on optional parameters and filters.\n",
-                "\n",
-                "***Beginning with SDK version 3.68, the `export_v2()` method has been enhanced to incorporate streamable functionality.***"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q urllib3",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport time",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a project\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When you export data rows from a project, you may choose to include or exclude certain attributes, including:\n",
-                "- `attachments`\n",
-                "- `metadata_fields`\n",
-                "- `data_row_details`\n",
-                "- `project_details`\n",
-                "- `label_details`\n",
-                "- `performance_details`\n",
-                "- `interpolated_frames`\n",
-                "    - Only applicable for video data rows.\n",
-                "\n",
-                "### Filters\n",
-                "When you export data rows from a project, you can specify the included data rows with the following filters:\n",
-                "- `last_activity_at`\n",
-                "- `label_created_at`\n",
-                "- `data_row_ids`\n",
-                "- `global_keys`\n",
-                "- `batch_ids`\n",
-                "- `workflow_status`\n",
-                "\n",
-                "#### Filter details\n",
-                "You can set the range for `last_activity_at` and `label_created_at` in the following formats: \n",
-                "- `YYYY-MM-DD`\n",
-                "- `YYYY-MM-DD hh:mm:ss`\n",
-                "- `YYYY-MM-DDThh:mm:ss\u00b1hhmm` (ISO 8601)\n",
-                "\n",
-                "The ISO 8061 format allows you to specify the timezone, while the other two formats assume timezone from the user's workspace settings.\n",
-                "\n",
-                "The `last_activity_at` filter captures the creation and modification of labels, metadata, workflow status, comments, and reviews.\n",
-                "\n",
-                "If you wish to specify data rows to export, uncomment the `data_row_ids` or `global_keys` filter and provide a list of applicable IDs. The data rows must be part of a batch attached to the project in question. You can provide up to 2,000 data row IDs.\n",
-                "\n",
-                "The `batch_ids` filter allows you to specify data rows for export based on their associated batch ID. This is particularly useful when `data_row_ids` is not sufficient due to 2,000 data row IDs limit. \n",
-                "\n",
-                "\n",
-                "The `workflow_status` filter allows you to specify data rows in a given work flow step. This filter only accepts one value. For example, `filters = {\"workflow_status\": \"InReview\"}`. The filer accepts the following:\n",
-                "- `ToLabel`\n",
-                "- `InReview`\n",
-                "- `InRework`\n",
-                "- `Done`\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nexport_task = project.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nclient.enable_experimental = True\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Uncomment to get stream results as a written file\n\n# Provide results with file converter\n\n# if export_task.has_errors():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./errors.txt\"),\n#         stream_type=lb.StreamType.ERRORS\n#     ).start()\n\n# if export_task.has_result():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./result.txt\"),\n#         stream_type=lb.StreamType.RESULT\n#     ).start()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a dataset\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When you export data rows from a dataset, you may choose to include or exclude certain attributes, including:\n",
-                "- `attachments`\n",
-                "- `metadata_fields`\n",
-                "- `data_row_details`\n",
-                "- `project_details`\n",
-                "- `label_details`\n",
-                "- `performance_details`\n",
-                "- `interpolated_frames`\n",
-                "    - Only applicable for video data rows.\n",
-                "- `project_ids`\n",
-                "    - Accepts a list of project IDs. If provided, the labels created _in these projects_ on the exported data rows will be included. \n",
-                "- `model_run_ids`\n",
-                "    - Accepts a list of model run IDs. If provided, the labels and predicitions created _in these model runs_ will be included. \n",
-                "\n",
-                "### Filters\n",
-                "When you export data rows from a dataset, you can specify the included data rows with the following filters:\n",
-                "- `last_activity_at`\n",
-                "- `label_created_at`\n",
-                "- `data_row_ids`\n",
-                "- `global_keys`\n",
-                "\n",
-                "See the _Export data rows from a project_ section above for additional details on each filter. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the dataset ID of the dataset from which you wish to export data rows.\nDATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nexport_task = dataset.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nclient.enable_experimental = True\n\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from Catalog\n",
-                "`get_catalog()` method allows you to fetch all your data from catalog\n",
-                "\n",
-                "### Parameters\n",
-                "When exporting from catalog, you can apply the same parameters as exporting from a project.\n",
-                "\n",
-                "### Filters\n",
-                "When exporting from catalog, you can apply the same filters as exporting from a dataset.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "catalog = client.get_catalog()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\nexport_task = catalog.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\n\nclient.enable_experimental = True\nexport_task = catalog.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "def json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a slice\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When exporting from a slice, you can apply the same parameters as exporting from a dataset.\n",
-                "\n",
-                "### Filters\n",
-                "No filters are applicable to exports from a slice. All the data rows of the slice must be exported."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the Catalog slice ID of the slice from which you wish to export data rows.\nCATALOG_SLICE_ID = \"\"\ncatalog_slice = client.get_catalog_slice(CATALOG_SLICE_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nexport_task = catalog_slice.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nclient.enable_experimental = True\n\nexport_task = catalog_slice.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export data rows from a model run\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "- `attachments`\n",
-                "- `metadata_fields`\n",
-                "- `data_row_details`\n",
-                "- `interpolated_frames`\n",
-                "    - Only applicable for video data rows.\n",
-                "- `predictions`\n",
-                "    - If true, all predictions made in the model run will be included for each data row in the export.\n",
-                "\n",
-                "### Filters\n",
-                "No filters are applicable to exports from a model run. All the data rows of the model run must be exported.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the model run ID of the model run from which you wish to export data rows.\nMODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nexport_task = model_run.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Export Data Row\n",
-                "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
-                "\n",
-                "### Parameters\n",
-                "When exporting data rows, you can apply the same parameters as exporting from a project.\n",
-                "\n",
-                "### Filters\n",
-                "No filters are applicable to export data rows. All the data rows specified in the export task are included."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Insert the global key of the data row you wish to export\nDATA_ROW_GLOBAL_KEY = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Export V2 Method"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export_v2(client=client,\n                                   global_keys=[DATA_ROW_GLOBAL_KEY],\n                                   params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Stream Task Export Method\n",
-                "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
-                "This allows streaming of task results and errors."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export(client=client,\n                                global_keys=[DATA_ROW_GLOBAL_KEY],\n                                params=export_params)\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_data.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Export data\n",
+        "How to export data for projects, datasets, slices, data rows and models, with examples for each type of v2 export along with details on optional parameters and filters.\n",
+        "\n",
+        "***Beginning with SDK version 3.68, the `export_v2()` method has been enhanced to incorporate streamable functionality.***"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q urllib3",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport urllib.request\nfrom PIL import Image\nimport time",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a project\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When you export data rows from a project, you may choose to include or exclude certain attributes, including:\n",
+        "- `attachments`\n",
+        "- `metadata_fields`\n",
+        "- `data_row_details`\n",
+        "- `project_details`\n",
+        "- `label_details`\n",
+        "- `performance_details`\n",
+        "- `interpolated_frames`\n",
+        "    - Only applicable for video data rows.\n",
+        "\n",
+        "### Filters\n",
+        "When you export data rows from a project, you can specify the included data rows with the following filters:\n",
+        "- `last_activity_at`\n",
+        "- `label_created_at`\n",
+        "- `data_row_ids`\n",
+        "- `global_keys`\n",
+        "- `batch_ids`\n",
+        "- `workflow_status`\n",
+        "\n",
+        "#### Filter details\n",
+        "You can set the range for `last_activity_at` and `label_created_at` in the following formats: \n",
+        "- `YYYY-MM-DD`\n",
+        "- `YYYY-MM-DD hh:mm:ss`\n",
+        "- `YYYY-MM-DDThh:mm:ss\u00b1hhmm` (ISO 8601)\n",
+        "\n",
+        "The ISO 8061 format allows you to specify the timezone, while the other two formats assume timezone from the user's workspace settings.\n",
+        "\n",
+        "The `last_activity_at` filter captures the creation and modification of labels, metadata, workflow status, comments, and reviews.\n",
+        "\n",
+        "If you wish to specify data rows to export, uncomment the `data_row_ids` or `global_keys` filter and provide a list of applicable IDs. The data rows must be part of a batch attached to the project in question. You can provide up to 2,000 data row IDs.\n",
+        "\n",
+        "The `batch_ids` filter allows you to specify data rows for export based on their associated batch ID. This is particularly useful when `data_row_ids` is not sufficient due to 2,000 data row IDs limit. \n",
+        "\n",
+        "\n",
+        "The `workflow_status` filter allows you to specify data rows in a given work flow step. This filter only accepts one value. For example, `filters = {\"workflow_status\": \"InReview\"}`. The filer accepts the following:\n",
+        "- `ToLabel`\n",
+        "- `InReview`\n",
+        "- `InRework`\n",
+        "- `Done`\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the project ID of the project from which you wish to export data rows.\nPROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nexport_task = project.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n    # \"batch_ids\": [\"<batch_id>\", \"<batch_id>\"],\n    # \"workflow_status\": \"<workflow_status>\"\n}\n\nclient.enable_experimental = True\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Uncomment to get stream results as a written file\n\n# Provide results with file converter\n\n# if export_task.has_errors():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./errors.txt\"),\n#         stream_type=lb.StreamType.ERRORS\n#     ).start()\n\n# if export_task.has_result():\n#     export_task.get_stream(\n#         converter=lb.FileConverter(file_path=\"./result.txt\"),\n#         stream_type=lb.StreamType.RESULT\n#     ).start()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a dataset\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When you export data rows from a dataset, you may choose to include or exclude certain attributes, including:\n",
+        "- `attachments`\n",
+        "- `metadata_fields`\n",
+        "- `data_row_details`\n",
+        "- `project_details`\n",
+        "- `label_details`\n",
+        "- `performance_details`\n",
+        "- `interpolated_frames`\n",
+        "    - Only applicable for video data rows.\n",
+        "- `project_ids`\n",
+        "    - Accepts a list of project IDs. If provided, the labels created _in these projects_ on the exported data rows will be included. \n",
+        "- `model_run_ids`\n",
+        "    - Accepts a list of model run IDs. If provided, the labels and predicitions created _in these model runs_ will be included. \n",
+        "\n",
+        "### Filters\n",
+        "When you export data rows from a dataset, you can specify the included data rows with the following filters:\n",
+        "- `last_activity_at`\n",
+        "- `label_created_at`\n",
+        "- `data_row_ids`\n",
+        "- `global_keys`\n",
+        "\n",
+        "See the _Export data rows from a project_ section above for additional details on each filter. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the dataset ID of the dataset from which you wish to export data rows.\nDATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nexport_task = dataset.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\n# Note: Filters follow AND logic, so typically using one filter is sufficient.\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"],\n}\n\nclient.enable_experimental = True\n\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from Catalog\n",
+        "`get_catalog()` method allows you to fetch all your data from catalog\n",
+        "\n",
+        "### Parameters\n",
+        "When exporting from catalog, you can apply the same parameters as exporting from a project.\n",
+        "\n",
+        "### Filters\n",
+        "When exporting from catalog, you can apply the same filters as exporting from a dataset.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "catalog = client.get_catalog()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\nexport_task = catalog.export_v2(params=export_params, filters=filters)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id_1>\",\"<project_id_2>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nfilters = {\n    \"last_activity_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"label_created_at\": [\"2000-01-01 00:00:00\", \"2050-01-01 00:00:00\"],\n    # \"global_keys\": [\"<global_key>\", \"<global_key>\"],\n    # \"data_row_ids\": [\"<data_row_id>\", \"<data_row_id>\"]\n}\n\nclient.enable_experimental = True\nexport_task = catalog.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "def json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a slice\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When exporting from a slice, you can apply the same parameters as exporting from a dataset.\n",
+        "\n",
+        "### Filters\n",
+        "No filters are applicable to exports from a slice. All the data rows of the slice must be exported."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the Catalog slice ID of the slice from which you wish to export data rows.\nCATALOG_SLICE_ID = \"\"\ncatalog_slice = client.get_catalog_slice(CATALOG_SLICE_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nexport_task = catalog_slice.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n    # \"project_ids\": [\"<project_id>\", \"<project_id>\"],\n    # \"model_run_ids\": [\"<model_run_id>\", \"<model_run_id>\"]\n}\n\nclient.enable_experimental = True\n\nexport_task = catalog_slice.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a model run\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "- `attachments`\n",
+        "- `metadata_fields`\n",
+        "- `data_row_details`\n",
+        "- `interpolated_frames`\n",
+        "    - Only applicable for video data rows.\n",
+        "- `predictions`\n",
+        "    - If true, all predictions made in the model run will be included for each data row in the export.\n",
+        "\n",
+        "### Filters\n",
+        "No filters are applicable to exports from a model run. All the data rows of the model run must be exported.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the model run ID of the model run from which you wish to export data rows.\nMODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nexport_task = model_run.export_v2(params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"interpolated_frames\": True,\n    \"predictions\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export Data Row\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) developer guide.\n",
+        "\n",
+        "### Parameters\n",
+        "When exporting data rows, you can apply the same parameters as exporting from a project.\n",
+        "\n",
+        "### Filters\n",
+        "No filters are applicable to export data rows. All the data rows specified in the export task are included."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Insert the global key of the data row you wish to export\nDATA_ROW_GLOBAL_KEY = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2 Method"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export_v2(client=client,\n                                   global_keys=[DATA_ROW_GLOBAL_KEY],\n                                   params=export_params)\nexport_task.wait_till_done()\n\nif export_task.errors:\n    print(export_task.errors)\n\nexport_json = export_task.result\nprint(\"results: \", export_json)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Stream Task Export Method\n",
+        "The return type of this method is an ExportTask, instead of a Task. This is just a wrapper around Task, and most of its features are also present in ExportTask.\n",
+        "This allows streaming of task results and errors."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Set the export params to include/exclude certain fields.\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n    \"interpolated_frames\": True,\n    \"embeddings\": True,\n}\n\nclient.enable_experimental = True\n\n# Provide a list of data row global keys\nexport_task = lb.DataRow.export(client=client,\n                                global_keys=[DATA_ROW_GLOBAL_KEY],\n                                params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/exports/export_v1_to_v2_migration_support.ipynb b/examples/exports/export_v1_to_v2_migration_support.ipynb
new file mode 100644
index 000000000..2434c7369
--- /dev/null
+++ b/examples/exports/export_v1_to_v2_migration_support.ipynb
@@ -0,0 +1,591 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/exports/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/exports/export_v1_to_v2_migration_support.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Export V1 to V2 migration "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "**Export V1 will no longer be available in any version of the SDK starting on April 2024**. We recommend users to plan accordingly. \n",
+        "\n",
+        "This notebook is designed to help users identify alternative V2 export methods that can serve as replacements for V1 methods."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Key changes included in export V2 methods ( ``export()`` and ``export_v2()``):\n",
+        "1. Added flexibility to only export the data that is needed. The new methods include parameters and filters to give you more granular control over your exports.\n",
+        "2. Added functionality to stream an **unlimited** number of data rows using ``export()`` (available on SDK >=3.56). Upgrading to `export()` is recommended as it is a more scalable solution.\n",
+        "\n",
+        "For complete details on how to use export V2 methods please see the [Export V2 methods](https://docs.labelbox.com/reference/label-export#export-v2-methods) documentation."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "###  Export V1 deprecated methods: \n",
+        "Project methods : \n",
+        "1. ```project.export_labels()```\n",
+        "2. ```project.label_generator()```\n",
+        "3. ```project.export_queued_data_rows()```\n",
+        "\n",
+        "Dataset methods: \n",
+        "1. ```dataset.export_data_rows()```\n",
+        "\n",
+        "Batch methods: \n",
+        "1. ```batch.export_data_rows()```\n",
+        "\n",
+        "Model methods :\n",
+        "1. ```model_run.export_labels()```\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport pprint\n\npp = pprint.PrettyPrinter(width=30, compact=True)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)\nclient.enable_experimental = (\n    True  ## This is required if using the export() streamable method\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Exports V1 to V2 guidelines\n",
+        "The follow sections will demonstrate how to use the export V2 methods to fetch data from your projects, datasets, batches and model runs. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export labels from a project\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "PROJECT_ID = \"\"\nproject = client.get_project(PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V1 (deprecated)  \n",
+        "1. ```project.export_labels()``` \n",
+        "    - Parameters:  \n",
+        "        - ```download: bool = False```\n",
+        "        - ```timeout_seconds: int = 1800```\n",
+        "    - Output : (str | List[Dict[Any, Any]] | None)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "2. ```project.label_generator()```\n",
+        "    - Parameters: \n",
+        "        - ```timeout_seconds: int = 600```\n",
+        "    - Output: LabelGenerator"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single entry from the output of project.label_generator() (deprecated)\n# Label objects will not be deprecated.\nsingle_output_from_generator = \"\"\"\n\nLabel(\n    uid='clrf5csho2ihx07ilffgp2fzj',\n    data=ImageData(\n        im_bytes=None,\n        file_path=None,\n        url='https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg',\n        arr=None\n    ),\n    annotations=[\n        ObjectAnnotation(\n            confidence=None,\n            name='bounding_box',\n            feature_schema_id='clrf5ck4a0b9b071paa9ncu15',\n            extra={\n                'instanceURI': 'https://api.labelbox.com/masks/feature/clrf5csvi6ofm07lsf9pygwvi?token=<token>'\n                'color': '#ff0000',\n                'feature_id': 'clrf5csvi6ofm07lsf9pygwvi',\n                'value': 'bounding_box',\n                'page': None,\n                'unit': None\n            },\n            value=Rectangle(\n                extra={},\n                start=Point(extra={}, x=2096.0, y=1264.0),\n                end=Point(extra={}, x=2240.0, y=1689.0)\n            ),\n            classifications=[]\n        ),\n        # Add more annotations as needed\n        # ...\n    ],\n    extra={\n        'Created By': 'aovalle@labelbox.com',\n        'Project Name': 'Image Annotation Import Demo',\n        'Created At': '2024-01-15T16:35:59.000Z',\n        'Updated At': '2024-01-15T16:51:56.000Z',\n        'Seconds to Label': 66.0,\n        'Agreement': -1.0,\n        'Benchmark Agreement': -1.0,\n        'Benchmark ID': None,\n        'Dataset Name': 'image-demo-dataset',\n        'Reviews': [],\n        'View Label': 'https://editor.labelbox.com?project=clrf5ckex09m9070x1te223u5&label=clrf5csho2ihx07ilffgp2fzj',\n        'Has Open Issues': 0.0,\n        'Skipped': False,\n        'media_type': 'image',\n        'Data Split': None,\n        'Global Key': '2560px-Kitano_Street_Kobe01s5s41102.jpeg'\n    }\n)\n\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V2 \n",
+        "\n",
+        "For complete details on the supported filters abd parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Parameters:  \n",
+        "        - ```\"label_details\": True```\n",
+        "        - ```\"attachments\": True```\n",
+        "        - ```\"data_row_details\": True```\n",
+        "        - ```\"project_details\": True```\n",
+        "        - ```\"label_details\": True```\n",
+        "        - ```\"performance_details\": True```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Set the export parameters to only export labels\nexport_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"label_details\": True,\n    \"performance_details\": True,\n}\n# You also have the option to include additional filtering to narrow down the list of labels\nfilters = {}\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export queued (\"To Label\") data rows from a project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V1 (deprecated):  \n",
+        "1. ``project.export_queued_data_rows()`` :\n",
+        "    - Parameters: \n",
+        "        - ``timeout_seconds: int = 120``\n",
+        "        - ``include_metadata: bool = False``\n",
+        "    - Output: List[Dict[str, str]]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single entry from the output of project.export_queued_data_rows() (deprecated)\nsingle_output_example = \"\"\"\n[\n  {'id': 'clpouak6nap2g0783ajd1d6pf',\n 'createdAt': '2023-12-03T02:04:34.062Z',\n 'updatedAt': '2023-12-03T02:05:33.797Z',\n 'externalId': None,\n 'globalKey': 'b57c9ab2-304f-4c17-ba5f-c536f39a6a46',\n 'metadataFields': [],\n 'customMetadata': [],\n 'rowData': 'https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg',\n 'mediaAttributes': {'assetType': 'image',\n  'contentLength': 305973,\n  'height': 1333,\n  'mimeType': 'image/jpeg',\n  'subType': 'jpeg',\n  'superType': 'image',\n  'width': 2000}}\n]\n\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Parameters (Minimum required parameters):  \n",
+        "      - ```\"data_row_details\": True```\n",
+        "      - ```\"project_details\": True```\n",
+        "    - Required filters: \n",
+        "      - ``` \"workflow_status\": \"ToLabel\"```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n    \"project_details\": True,\n}\nfilters = {\n    \"workflow_status\":\n        \"ToLabel\"  ## Using this filter will only export queued data rows\n}\n\n# An ExportTask is returned, this provides additional information about the status of your task, such as\n# any errors encountered and includes additional methods to stream your data\n\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a Dataset "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATASET_ID = \"\"\ndataset = client.get_dataset(DATASET_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V1 (deprecated): \n",
+        "\n",
+        "1. ```dataset.export_data_rows()```\n",
+        "  - Parameters:  \n",
+        "    - ``timeout_seconds=120``\n",
+        "    - ``include_metadata: bool = True``\n",
+        "  - Output: \n",
+        "    - Data row object generator\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single entry from the output of dataset.export_data_rows() (deprecated)\n# Data row objects will not be deprecated.\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Parameters (minimum required parameters):  \n",
+        "      - ``\"data_row_details\": True``\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,  # Set to true if you want to export attachments\n    \"metadata_fields\": True,  # Set to true if you want to export metadata\n    \"data_row_details\": True,\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = dataset.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a batch"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V1 (deprecated): \n",
+        "1. ```batch.export_data_rows()```\n",
+        "  - Parameters:  \n",
+        "    - ``timeout_seconds=120``\n",
+        "    - ``include_metadata: bool = True``\n",
+        "  - Output: \n",
+        "    - Data row object generator"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single output from batch.export_data_rows() method (deprecated)\n# Data row objects will not be deprecated\n\nsingle_output_from_data_row_generator = \"\"\"\n<DataRow {\n    \"created_at\": \"2023-12-03 02:04:34.062000+00:00\",\n    \"external_id\": null,\n    \"global_key\": \"b57c9ab2-304f-4c17-ba5f-c536f39a6a46\",\n    \"media_attributes\": {\n        \"assetType\": \"image\",\n        \"contentLength\": 305973,\n        \"height\": 1333,\n        \"mimeType\": \"image/jpeg\",\n        \"subType\": \"jpeg\",\n        \"superType\": \"image\",\n        \"width\": 2000\n    },\n    \"metadata\": [],\n    \"metadata_fields\": [],\n    \"row_data\": \"https://storage.googleapis.com/labelbox-developer-testing-assets/image/data_files/santa.jpeg\",\n    \"uid\": \"clpouak6nap2g0783ajd1d6pf\",\n    \"updated_at\": \"2023-12-03 02:05:33.797000+00:00\"\n}>\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()``` : Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Required parameters:  \n",
+        "      - ```\"data_row_details\": True```,\n",
+        "      - ```\"batch_ids\": [<batch_id>] ```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Find the batch ID by navigating to \"Batches\" -->  \"Manage batches\" --> \"Copy Batch ID\"\nBATCH_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"batch_ids\": [\n        BATCH_ID\n    ],  # Include batch ids if you only want to export specific batches, otherwise,\n    # you can export all the data without using this parameter\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a Model "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V1 (deprecated): \n",
+        "1. ```model_run.export_labels(downlaod=True)```\n",
+        "    - Parameters:  \n",
+        "        - ```download: bool = False```\n",
+        "        - ```timeout_seconds: int = 1800```\n",
+        "    - Output : (str | List[Dict[Any, Any]] | None)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Single output from model_run.export_labels()\nsingle_output_example = \"\"\"\n[\n   {'ID': '1c48a7a0-3016-48e0-b0e3-47430f974869',\n   'Data Split': 'training',\n   'DataRow ID': 'clpqdyf650xd40712pycshy6a',\n   'External ID': './resume/BANKING/99124477.pdf',\n   'Labeled Data': 'https://storage.labelbox.com/cl5bn8qvq1av907xtb3bp8q60%2F8c6afc38-42a4-b2e1-a2e3-1e3b0c2998fc-99124477.pdf?Expires=1706637969726&KeyName=labelbox-assets-key-3&Signature=2nVt3sJ21CbjGS9I64yFquUELRw',\n   'Media Attributes': {'assetType': 'pdf',\n      'contentLength': 42535,\n      'mimeType': 'application/pdf',\n      'pageCount': 3,\n      'subType': 'pdf',\n      'superType': 'application'},\n   'Label': {'objects': [{'featureId': 'b9f3b584-0f45-050a-88d4-39c2a169c8e1',\n      'schemaId': 'clq1ckwbd08jp07z91q9mch5j',\n      'title': 'Test',\n      'value': 'test',\n      'color': '#1CE6FF',\n      'data': {'location': [{'text-bbox': {'page': 1,\n            'top': 158.44,\n            'left': 58.765,\n            'height': 13.691,\n            'width': 78.261}}],\n         'unit': 'POINTS'}}],\n      'classifications': [],\n      'relationships': []}}\n   ]\n   \"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Export V2\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```model_run.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Required parameters:  \n",
+        "      - ```\"data_row_details\": True```\n",
+        "      - ```\"project_details\": True```\n",
+        "      - ```\"label_details\": True```\n",
+        "    - Required filters: \n",
+        "      - N/A -> Filters not supported\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "MODEL_RUN_ID = \"\"\nmodel_run = client.get_model_run(MODEL_RUN_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n}\n\nexport_task = model_run.export(params=export_params)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Provide results with JSON converter\n# Returns streamed JSON output strings from export task results/errors, one by one\n\n\n# Callback used for JSON Converter\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    print(output.json_str)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)\n\nprint(\n    \"file size: \",\n    export_task.get_total_file_size(stream_type=lb.StreamType.RESULT),\n)\nprint(\n    \"line count: \",\n    export_task.get_total_lines(stream_type=lb.StreamType.RESULT),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Export data rows from a video project\n",
+        "Video projects include additional fields. Please refer to the example below to extract specific fields from video exports.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V1 (deprecated)  \n",
+        "1. ```project.export_labels()``` \n",
+        "    - Parameters:  \n",
+        "        - ```download: bool = False```\n",
+        "        - ```timeout_seconds: int = 1800```\n",
+        "    - Output : (str | List[Dict[Any, Any]] | None)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "For a comprehensive example of Export V1 ``project.export_labels()`` output, please refer to our documentation: [Export V1 sample export](https://docs.labelbox.com/reference/export-image-annotations#sample-project-export)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "2. ```project.video_label_generator()```\n",
+        "    - Parameters: \n",
+        "        - ```timeout_seconds: int = 600```\n",
+        "    - Output: LabelGenerator"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### Export V2\n",
+        "\n",
+        "For complete details on the supported filters and parameters, including how they are used and what information is included, please see the [Export overview](https://docs.labelbox.com/reference/label-export#optional-parameters-and-filters) documentation.\n",
+        "\n",
+        "1. ```project.export()```: Starting from SDK version 3.56, a streamable method is available, this method allows you to stream unlimited number of data rows. However, if you are using an earlier version, you can still utilize the ```export_v2()``` function with identical parameters. It's important to note that the output task type differs, and streaming data methods are not included in `export_v2()`.\n",
+        "\n",
+        "    - Required parameters:  \n",
+        "      - ```\"attachments\": True```\n",
+        "      - ```\"data_row_details\": True```\n",
+        "      - ```\"project_details\": True```\n",
+        "      - ```\"label_details\": True```\n",
+        "      - ```\"performance_details\": True```\n",
+        "    - Output: \n",
+        "       - ```ExportTask```\n",
+        "            - `ExportTask.has_result()` return type:  bool \n",
+        "            - `ExportTask.has_errors()` return type: bool\n",
+        "            - `ExportTask.get_stream()` return type: Stream[JsonConverterOutput]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "VIDEO_PROJECT_ID = \"\"\nproject = client.get_project(VIDEO_PROJECT_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_params = {\n    \"attachments\": True,\n    \"metadata_fields\": True,\n    \"data_row_details\": True,\n    \"project_details\": True,\n    \"performance_details\": True,\n    \"label_details\": True,\n    \"interpolated_frames\":\n        True,  # For additional information on interpolated frames please visit our documentation https://docs.labelbox.com/docs/video-annotations#video-editor-components\n}\nfilters = {}\n\n# A task is returned, this provides additional information about the status of your task, such as\n# any errors encountered\nexport_task = project.export(params=export_params, filters=filters)\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch frame specific objects and frame or global classifications"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import json\nimport pprint as pp  # Assuming pp is imported from pprint module\n\nframes_objects_class_list = []\nglobal_class_list = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n        for k, v in frames_data.items():\n            frames_objects_class_list.append({k: v})\n        global_class_list.extend(dr[\"annotations\"][\"classifications\"])\n\n    print(\"------- Frame specific classifications and objects -------\")\n    pp.pprint(frames_objects_class_list)\n\n    print(\"------ Global classifications -------\")\n    pp.pprint(global_class_list)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch key frame feature map "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "keyframe_map = []\n\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"key_frame_feature_map\"]\n        for key, value in annotations.items():\n            keyframe_map.append({key: value})\n\nprint(\"----- Keyframe Feature Map -----\")\npp.pprint(keyframe_map)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Fetch segments "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "segments_map = []\nstream = export_task.get_stream()\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    labels = output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]\n    for label in labels:\n        annotations = label[\"annotations\"][\"segments\"]\n        for key, value in annotations.items():\n            segments_map.append({key: value})\n\nprint(\"----- Segments Feature Map -----\")\npp.pprint(segments_map)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}
\ No newline at end of file
diff --git a/examples/extras/classification_confusion_matrix.ipynb b/examples/extras/classification_confusion_matrix.ipynb
deleted file mode 100644
index f21c8436f..000000000
--- a/examples/extras/classification_confusion_matrix.ipynb
+++ /dev/null
@@ -1,109 +0,0 @@
-{
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Classification Confusion matrix\n",
-                "\n",
-                "This notebook plots a confusion matrix for a multi-class, single label classification.\n",
-                "\n",
-                "It assumes that your project has a number of labels from multiple labelers, where the Annotation Type is Radio.\n",
-                "\n",
-                "## Installs"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\" scikit-learn matplotlib ipywidgets",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Imports and etc\n\nfrom sklearn.metrics import ConfusionMatrixDisplay\nfrom sklearn.metrics import classification_report\nimport matplotlib.pyplot as plt\nimport ipywidgets as widgets\nimport labelbox as lb\n\n\ndef get_labelers():\n    labels = project.label_generator()\n    labelers = []\n\n    for l in labels:\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    return labelers\n\n\ndef get_paired_labels(labeler1, labeler2):\n    labels = project.label_generator()\n    l1 = []\n    l2 = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        # Check if benchmark\n        if l.extra[\"Benchmark ID\"]:\n            label_dict[\n                l.data.uid][\"Benchmarks\"] = l.annotations[0].value.answer.name\n\n        # Skip labels not created by either of our labelers\n        if (l.extra[\"Created By\"] != labeler1 and\n                l.extra[\"Created By\"] != labeler2):\n            continue\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n    for uid in label_dict.keys():\n        if labeler1 in label_dict[uid] and labeler2 in label_dict[uid]:\n            l1.append(label_dict[uid][labeler1])\n            l2.append(label_dict[uid][labeler2])\n\n    return l1, l2\n\n\ndef get_label_dict():\n    labels = project.label_generator()\n    labelers = []\n    label_dict = {}\n\n    for l in labels:\n        if not l.data.uid in label_dict:\n            label_dict[l.data.uid] = {}\n\n        label_dict[l.data.uid][\n            l.extra[\"Created By\"]] = l.annotations[0].value.answer.name\n\n        if not l.extra[\"Created By\"] in labelers:\n            labelers.append(l.extra[\"Created By\"])\n\n    print(labelers)\n    return label_dict, labelers",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Labelbox API + project and dataset\n\n# Add your api key\nAPI_KEY = None\nlb = lb.Client(api_key=API_KEY)\n\n# Add your project and dataset uids here\nproject = lb.get_project(\"xxx\")\ndataset = lb.get_dataset(\"xxx\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Labeler Selection"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Selectors\n\nlabelers = get_labelers()\n\nitems = [\n    widgets.Dropdown(\n        options=[\"Benchmarks\"] + labelers,\n        description=\"Compare this:\",\n        disabled=False,\n    ),\n    widgets.Dropdown(\n        options=labelers,\n        description=\"Against this:\",\n        disabled=False,\n    ),\n]\n\nwidgets.VBox(items)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Confusion Matrix"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Plot Matrix\n\nl1, l2 = get_paired_labels(items[0].value, items[1].value)\n\nfig, ax = plt.subplots(figsize=(15, 15))\nc = ConfusionMatrixDisplay.from_predictions(\n    l1,\n    l2,\n    labels=list(set(l1)),\n    sample_weight=None,\n    normalize=None,\n    display_labels=None,\n    include_values=True,\n    xticks_rotation=\"vertical\",\n    values_format=None,\n    cmap=\"Blues\",\n    ax=ax,\n    colorbar=False,\n)\n\n_ = c.ax_.set_xlabel(items[1].value), c.ax_.set_ylabel(items[0].value)\n\ncr = classification_report(l1,\n                           l2,\n                           output_dict=False,\n                           target_names=list(set(l1)),\n                           zero_division=1)\n\nprint(cr)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
-}
\ No newline at end of file
diff --git a/examples/foundry/object_detection.ipynb b/examples/foundry/object_detection.ipynb
index a477d2541..5cf092bca 100644
--- a/examples/foundry/object_detection.ipynb
+++ b/examples/foundry/object_detection.ipynb
@@ -1,260 +1,258 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Foundry overview\n",
-                "\n",
-                "This notebook is used to go over the basic of foundry through the Python SDK"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:\n",
-                "\n",
-                "* Predict (infer) labels from your data\n",
-                "* Compare the performance of different foundational models with your data and ontologies.\n",
-                "* Prototype, diagnose, and refine a machine learning app to solve specific business needs.\n",
-                "\n",
-                "Foundry creates model runs that predict data row annotations based on your input."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q labelbox",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# End-to-end example: Run foundry and send to annotate from catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into catelog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an ontology that matches model\n",
-                "\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your model and data type.\n",
-                "\n",
-                "For example, when using Amazon Rekognition you would need to create a bounding box annotation for your ontology since it only supports object detection. Likewise when using YOLOv8 you would need to create a classification annotation for your ontology since it only supports image classification. \n",
-                "\n",
-                "In this tutorial, we will use Amazon Rekognition to detect objects in an image dataset. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create ontology with two bounding boxes that is included with Amazon Rekognition: Car and Person\nontology_builder = lb.OntologyBuilder(\n    classifications=[],\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Car\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Person\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Bounding Box Annotation Demo Foundry\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a labeling project\n",
-                "\n",
-                "Connect the ontology to the labeling project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Foundry Image Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Create foundry application in UI\n",
-                "\n",
-                "Currently we do not support this workflow through the SDK\n",
-                "#### Workflow:\n",
-                "\n",
-                "1. Navigate to model and select ***Create*** > ***App***\n",
-                "\n",
-                "2. Select ***Amazon Rekognition*** and name your foundry application\n",
-                "\n",
-                "3. Customize your perimeters and then select ***Save & Create***"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Select your foundry application inside the UI and copy the APP ID from the top right corner\nAMAZON_REKOGNITION_APP_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Run foundry app on data rows\n",
-                "\n",
-                "This step is meant to generate annotations that can later be reused as pre-labels in a project. You must provide your app ID from the previous step for this method to run, please see the [Foundry Apps Guide](https://docs.labelbox.com/docs/foundry-apps#run-app-using-sdk) for more information.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "task = client.run_foundry_app(\n    model_run_name=f\"Amazon-{str(uuid.uuid4())}\",\n    data_rows=lb.GlobalKeys([global_key]),  # Provide a list of global keys\n    app_id=AMAZON_REKOGNITION_APP_ID,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\n\n# Obtain model run ID from task\nMODEL_RUN_ID = task.metadata[\"modelRunId\"]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Map ontology through the UI\n",
-                "\n",
-                "Mapping a model's ontology to a project's ontology is currently not supported through the SDK, however, to showcase how to send foundry predictions to a project, we are going to generate the mapping of the foundry app ontology to the project ontology through the UI.\n",
-                "\n",
-                "#### Workflow\n",
-                "\n",
-                "1. Navigate to your dataset you created for your model run\n",
-                "2. Select ***Select all*** in the top right corner\n",
-                "3. Select ***Manage selection*** > ***Send to Annotate***\n",
-                "4. Specify the project we created from the project dropdown menu\n",
-                "5. Selecting a workflow step is not required since we are not sending annotations from the UI to a project using this notebook \n",
-                "6. Mark ***Include model predictions*** then scroll down and select ***Map***\n",
-                "7. Select the incoming ontology and matching ontology feature for both Car and Person\n",
-                "8. Once both features are mapped press the ***Copy ontology mapping as JSON*** in the top right corner\n",
-                "9. Do not save this configuration, since we are not sending predictions to a project using this UI modal. We will be sending predictions in the following steps using the SDK"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Copy map ontology through the UI then paste JSON file here\nPREDICTIONS_ONTOLOGY_MAPPING = {}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send model generated annotations from catalog to annotate\n",
-                "\n",
-                "### Parameters\n",
-                "\n",
-                "When you send predicted data rows to annotate from catalog, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
-                "\n",
-                "* `predictions_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run = client.get_model_run(MODEL_RUN_ID)\n\nsend_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    None,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths or obtain task queue id through project.task_queues().\n    batch_name=\"Foundry Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [],
-            "cell_type": "markdown"
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/foundry/object_detection.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Foundry overview\n",
+        "\n",
+        "This notebook is used to go over the basic of foundry through the Python SDK"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:\n",
+        "\n",
+        "* Predict (infer) labels from your data\n",
+        "* Compare the performance of different foundational models with your data and ontologies.\n",
+        "* Prototype, diagnose, and refine a machine learning app to solve specific business needs.\n",
+        "\n",
+        "Foundry creates model runs that predict data row annotations based on your input."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q labelbox",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# End-to-end example: Run foundry and send to annotate from catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into catelog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an ontology that matches model\n",
+        "\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your model and data type.\n",
+        "\n",
+        "For example, when using Amazon Rekognition you would need to create a bounding box annotation for your ontology since it only supports object detection. Likewise when using YOLOv8 you would need to create a classification annotation for your ontology since it only supports image classification. \n",
+        "\n",
+        "In this tutorial, we will use Amazon Rekognition to detect objects in an image dataset. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create ontology with two bounding boxes that is included with Amazon Rekognition: Car and Person\nontology_builder = lb.OntologyBuilder(\n    classifications=[],\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Car\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"Person\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Bounding Box Annotation Demo Foundry\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a labeling project\n",
+        "\n",
+        "Connect the ontology to the labeling project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Foundry Image Demo\",\n                                media_type=lb.MediaType.Image)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Create foundry application in UI\n",
+        "\n",
+        "Currently we do not support this workflow through the SDK\n",
+        "#### Workflow:\n",
+        "\n",
+        "1. Navigate to model and select ***Create*** > ***App***\n",
+        "\n",
+        "2. Select ***Amazon Rekognition*** and name your foundry application\n",
+        "\n",
+        "3. Customize your perimeters and then select ***Save & Create***"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Select your foundry application inside the UI and copy the APP ID from the top right corner\nAMAZON_REKOGNITION_APP_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Run foundry app on data rows\n",
+        "\n",
+        "This step is meant to generate annotations that can later be reused as pre-labels in a project. You must provide your app ID from the previous step for this method to run, please see the [Foundry Apps Guide](https://docs.labelbox.com/docs/foundry-apps#run-app-using-sdk) for more information.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "task = client.run_foundry_app(\n    model_run_name=f\"Amazon-{str(uuid.uuid4())}\",\n    data_rows=lb.GlobalKeys([global_key]),  # Provide a list of global keys\n    app_id=AMAZON_REKOGNITION_APP_ID,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\n\n# Obtain model run ID from task\nMODEL_RUN_ID = task.metadata[\"modelRunId\"]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Map ontology through the UI\n",
+        "\n",
+        "Mapping a model's ontology to a project's ontology is currently not supported through the SDK, however, to showcase how to send foundry predictions to a project, we are going to generate the mapping of the foundry app ontology to the project ontology through the UI.\n",
+        "\n",
+        "#### Workflow\n",
+        "\n",
+        "1. Navigate to your dataset you created for your model run\n",
+        "2. Select ***Select all*** in the top right corner\n",
+        "3. Select ***Manage selection*** > ***Send to Annotate***\n",
+        "4. Specify the project we created from the project dropdown menu\n",
+        "5. Selecting a workflow step is not required since we are not sending annotations from the UI to a project using this notebook \n",
+        "6. Mark ***Include model predictions*** then scroll down and select ***Map***\n",
+        "7. Select the incoming ontology and matching ontology feature for both Car and Person\n",
+        "8. Once both features are mapped press the ***Copy ontology mapping as JSON*** in the top right corner\n",
+        "9. Do not save this configuration, since we are not sending predictions to a project using this UI modal. We will be sending predictions in the following steps using the SDK"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Copy map ontology through the UI then paste JSON file here\nPREDICTIONS_ONTOLOGY_MAPPING = {}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send model generated annotations from catalog to annotate\n",
+        "\n",
+        "### Parameters\n",
+        "\n",
+        "When you send predicted data rows to annotate from catalog, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
+        "\n",
+        "* `predictions_ontology_mapping`\n",
+        "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
+        "* `exclude_data_rows_in_project`\n",
+        "    - Excludes data rows that are already in the project. \n",
+        "* `override_existing_annotations_rule` \n",
+        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
+        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+        "        * Options include:\n",
+        "            * ConflictResolutionStrategy.KeepExisting\n",
+        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+        "* `param batch_priority`\n",
+        "    - The priority of the batch.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run = client.get_model_run(MODEL_RUN_ID)\n\nsend_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    None,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths or obtain task queue id through project.task_queues().\n    batch_name=\"Foundry Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Clean up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [],
+      "cell_type": "markdown"
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
index 667e1d4a1..e86fe85b9 100644
--- a/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
+++ b/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb
@@ -1,152 +1,150 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Install required libraries"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q transformers",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Imports"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Labelbox Credentials"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Select data rows in Labelbox for custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.get_dataset(DATASET_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Get a HuggingFace Model to generate custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Generate and upload custom embeddings"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/huggingface/huggingface_custom_embeddings.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Install required libraries"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q transformers",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Imports"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport transformers\n\ntransformers.logging.set_verbosity(50)\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport requests\nfrom tqdm import tqdm\nimport numpy as np",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Labelbox Credentials"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Select data rows in Labelbox for custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get images from a Labelbox dataset,\n# Ensure the images are available by obtaining a token from your cloud provider if necessary\nDATASET_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.get_dataset(DATASET_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "export_task = dataset.export_v2()\n\nexport_task.wait_till_done()\nif export_task.errors:\n    print(export_task.errors)\nexport_json = export_task.result\n\ndata_row_urls = [dr_url[\"data_row\"][\"row_data\"] for dr_url in export_json]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Get a HuggingFace Model to generate custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get ResNet-50 from HuggingFace\nimage_processor = transformers.AutoImageProcessor.from_pretrained(\n    \"microsoft/resnet-50\")\nmodel = transformers.ResNetModel.from_pretrained(\"microsoft/resnet-50\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Pick an existing custom embedding in Labelbox, or create a custom embedding"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new embedding in your workspace, use the right dimensions to your use case, here we use 2048 for ResNet-50\nnew_custom_embedding_id = client.create_embedding(\n    name=\"My new awesome embedding\", dims=2048).id\n\n# Or use an existing embedding from your workspace\n# existing_embedding_id = client.get_embedding_by_name(name=\"ResNet img 2048\").id",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Generate and upload custom embeddings"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "img_emb = []\n\nfor url in tqdm(data_row_urls):\n    try:\n        response = requests.get(url, stream=True)\n        if response.status_code == 200:\n            # Open the image, convert to RGB, and resize to 224x224\n            image = Image.open(response.raw).convert(\"RGB\").resize((224, 224))\n\n            # Preprocess the image for model input\n            img_hf = image_processor(image, return_tensors=\"pt\")\n\n            # Pass the image through the model to get embeddings\n            with torch.no_grad():\n                last_layer = model(**img_hf,\n                                   output_hidden_states=True).last_hidden_state\n                resnet_embeddings = F.adaptive_avg_pool2d(last_layer, (1, 1))\n                resnet_embeddings = torch.flatten(resnet_embeddings,\n                                                  start_dim=1,\n                                                  end_dim=3)\n                img_emb.append(resnet_embeddings.cpu().numpy())\n        else:\n            continue\n    except Exception as e:\n        print(f\"Error processing URL: {url}. Exception: {e}\")\n        continue\n\ndata_rows = []\n\n# Create data rows payload to send to a dataset\nfor url, embedding in tqdm(zip(data_row_urls, img_emb)):\n    data_rows.append({\n        \"row_data\":\n            url,\n        \"embeddings\": [{\n            \"embedding_id\": new_custom_embedding_id,\n            \"vector\": embedding[0].tolist(),\n        }],\n    })",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Upload to a new dataset\ndataset = client.create_dataset(name=\"image_custom_embedding_resnet\",\n                                iam_integration=None)\ntask = dataset.create_data_rows(data_rows)\nprint(task.errors)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/langchain/langchain.ipynb b/examples/integrations/langchain/langchain.ipynb
index 60c9fb099..f6653d001 100644
--- a/examples/integrations/langchain/langchain.ipynb
+++ b/examples/integrations/langchain/langchain.ipynb
@@ -1,178 +1,176 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LangChain -> Labelbox\n",
-                "This notebook is used to show an example workflow of getting LangChain traces into Labelbox conversation data format. Please review the [associated written guide](https://labelbox.com/guides/turn-langchain-logs-into-conversational-data-with-labelbox/) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install --upgrade --quiet  langchain langsmith langchainhub\n%pip install --upgrade --quiet  langchain-openai tiktoken pandas duckduckgo-search\n%pip install --upgrade --quiet  \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom uuid import uuid4\nimport os\nimport functools\n\n# LangSmith Imports\nfrom langsmith.client import Client\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor\nfrom langchain.agents.format_scratchpad.openai_tools import (\n    format_to_openai_tool_messages,)\nfrom langchain.agents.output_parsers.openai_tools import (\n    OpenAIToolsAgentOutputParser,)\nfrom langchain_community.tools import DuckDuckGoSearchResults\nfrom langchain_openai import ChatOpenAI\nfrom langsmith.evaluation import EvaluationResult\nfrom langsmith.schemas import Example, Run, DataType\nfrom langchain.smith import run_on_dataset\nfrom langchain.evaluation import EvaluatorType\nfrom langchain.smith import RunEvalConfig",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Setup\n",
-                "Provide a valid API key below for Labelbox, LangSmith and OpenAI in order for the notebook to work correctly."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "LB_API_KEY = \"\"\nLS_API_KEY = \"\"\nOPENAI_API_KEY = \"\"\n\nunique_id = uuid4().hex[0:8]\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = f\"Tracing Walkthrough - {unique_id}\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = LS_API_KEY\n\nos.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n\nlb_client = lb.Client(LB_API_KEY)\nclient = Client()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### LangSmith Dataset Name\n",
-                "Create a sample chat data set with an example chat based run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset_name = f\"Sample Dataset - {str(uuid4())}\"\ndataset = client.create_dataset(\n    dataset_name,\n    description=\"A sample dataset in LangSmith.\",\n    data_type=DataType.chat,\n)\nclient.create_chat_example(\n    messages=[\n        {\n            \"type\": \"ai\",\n            \"data\": {\n                \"content\": \"hi how are you\"\n            }\n        },\n        {\n            \"type\": \"human\",\n            \"data\": {\n                \"content\": \"Im doing great how about you\"\n            }\n        },\n    ],\n    generations={\n        \"type\": \"ai\",\n        \"data\": {\n            \"content\": \"Im doing great\"\n        },\n    },  # Custom model output\n    dataset_id=dataset.id,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### LangSmith\n",
-                "Below is an example of running a list of raw text evaluation strings and a LangSmith example run with Chat Gpt 3.5. Please review [LangSmith Docs](https://docs.smith.langchain.com/) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "tools = [\n    DuckDuckGoSearchResults(\n        name=\"duck_duck_go\"),  # General internet search using DuckDuckGo\n]\n\nllm = ChatOpenAI(\n    model=\"gpt-3.5-turbo-16k\",\n    temperature=0,\n)\n\n\n# Since chains can be stateful (e.g. they can have memory), we provide\n# a way to initialize a new chain for each row in the dataset. This is done\n# by passing in a factory function that returns a new chain for each row.\ndef create_agent(prompt, llm_with_tools):\n    runnable_agent = ({\n        \"input\":\n            lambda x: x[\"input\"],\n        \"agent_scratchpad\":\n            lambda x: format_to_openai_tool_messages(x[\"intermediate_steps\"]),\n    } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser())\n    return AgentExecutor(agent=runnable_agent,\n                         tools=tools,\n                         handle_parsing_errors=True)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "def max_pred_length(runs, examples):\n    predictions = [len(run.outputs[\"output\"]) for run in runs]\n    return EvaluationResult(key=\"max_pred_length\", score=max(predictions))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "def check_not_idk(run: Run, example: Example):\n    \"\"\"Illustration of a custom evaluator.\"\"\"\n    agent_response = run.outputs[\"output\"]\n    if \"don't know\" in agent_response or \"not sure\" in agent_response:\n        score = 0\n    else:\n        score = 1\n    # You can access the dataset labels in example.outputs[key]\n    # You can also access the model inputs in run.inputs[key]\n    return EvaluationResult(\n        key=\"not_uncertain\",\n        score=score,\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "evaluation_config = RunEvalConfig(\n    # Evaluators can either be an evaluator type (e.g., \"qa\", \"criteria\", \"embedding_distance\", etc.) or a configuration for that evaluator\n    evaluators=[\n        check_not_idk,\n        # Measures whether a QA response is \"Correct\", based on a reference answer\n        # You can also select via the raw string \"qa\"\n        EvaluatorType.QA,\n        # Measure the embedding distance between the output and the reference answer\n        # Equivalent to: EvalConfig.EmbeddingDistance(embeddings=OpenAIEmbeddings())\n        EvaluatorType.EMBEDDING_DISTANCE,\n        # Grade whether the output satisfies the stated criteria.\n        # You can select a default one such as \"helpfulness\" or provide your own.\n        RunEvalConfig.LabeledCriteria(\"helpfulness\"),\n        # The LabeledScoreString evaluator outputs a score on a scale from 1-10.\n        # You can use default criteria or write our own rubric\n        RunEvalConfig.LabeledScoreString(\n            {\n                \"accuracy\":\n                    \"\"\"\nScore 1: The answer is completely unrelated to the reference.\nScore 3: The answer has minor relevance but does not align with the reference.\nScore 5: The answer has moderate relevance but contains inaccuracies.\nScore 7: The answer aligns with the reference but has minor errors or omissions.\nScore 10: The answer is completely accurate and aligns perfectly with the reference.\"\"\"\n            },\n            normalize_by=10,\n        ),\n    ],\n    batch_evaluators=[max_pred_length],\n)\n\nllm_with_tools = llm.bind_tools(tools)\nprompt = hub.pull(\"gabe/labelboxtutorialdemo\"\n                 )  # Change prompt in LangSmith hub to reflect example run\n\nchain_results = run_on_dataset(\n    dataset_name=dataset_name,\n    llm_or_chain_factory=functools.partial(create_agent,\n                                           prompt=prompt,\n                                           llm_with_tools=llm_with_tools),\n    evaluation=evaluation_config,\n    verbose=True,\n    client=client,\n    project_name=f\"tools-agent-test-5d466cbc-{unique_id}\",\n    # Project metadata communicates the experiment parameters,\n    # Useful for reviewing the test results\n    project_metadata={\n        \"env\": \"testing-notebook\",\n        \"model\": \"gpt-3.5-turbo\",\n        \"prompt\": \"5d466cbc\",\n    },\n)\n\n# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.\n# These are logged as warnings here and captured as errors in the tracing UI.",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Labelbox\n",
-                "Below converts the results of the above LangSmith run to Labelbox conversation text. Please review [Labelbox conversation data docs](https://docs.labelbox.com/docs/llm-human-preference) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "def import_conversational(\n        chain_results: dict[str:str],\n        user_id_dict: dict[str:dict[str:str]]) -> dict[str:str]:\n    \"\"\"Converts LangSmith chain_results from model invocation to Labelbox conversation data for model response comparison. Output is based on popular model response and custom model response towards prompts.\n\n    Args:\n      chain_results(dict[str:str]): Results from LangSmith model invocation against example dataset runs.\n      user_id_dict(dict[str:dict[str:str]]): Dictionary mapping of LangSmith example run type to Labelbox chat names and alignment.\n\n    Returns:\n        dict[str:str]: Labelbox conversation text format\n    \"\"\"\n    lb_conversations = []\n    for key, conversational in chain_results[\"results\"].items():\n        lb_conversation = {\n            \"row_data\": {\n                \"type\": \"application/vnd.labelbox.conversational\",\n                \"version\": 1,\n                \"messages\": [],\n                \"modelOutputs\": [],\n            },\n            \"global_key\": key,\n            \"media_type\": \"CONVERSATIONAL\",\n        }\n        if \"input\" in conversational[\"output\"]:\n            for i, input in enumerate(conversational[\"output\"][\"input\"]):\n                lb_conversation[\"row_data\"][\"messages\"].append({\n                    \"content\": input[\"data\"][\"content\"],\n                    \"timestampUsec\": i + 1,\n                    \"user\": {\n                        \"userId\": user_id_dict[input[\"type\"]][\"id\"],\n                        \"name\": input[\"type\"],\n                    },\n                    \"canLabel\": True,\n                    \"align\": user_id_dict[input[\"type\"]][\"align\"],\n                    \"messageId\": str(uuid4()),\n                })\n\n        # Custom model output\n        if \"reference\" in conversational:\n            reference = conversational[\"reference\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Custom Model Response\",\n                \"content\": reference[\"data\"][\"content\"],\n                \"modelConfigName\": \"Custom Model - Example Config\",\n            })\n\n        # Popular model output\n        if \"output\" in conversational[\"output\"]:\n            output = conversational[\"output\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Popular LLM Response\",\n                \"content\": output,\n                \"modelConfigName\": \"GPT-3.5 - Example Config\",\n            })\n\n        lb_conversations.append(lb_conversation)\n    return lb_conversations",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Labelbox Dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = lb_client.create_dataset(name=\"demo_langchain\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Attach Conversation Text to Dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "task = dataset.create_data_rows(\n    import_conversational(\n        chain_results,\n        {\n            \"human\": {\n                \"id\": \"human\",\n                \"align\": \"right\"\n            },\n            \"ai\": {\n                \"id\": \"ai\",\n                \"align\": \"left\"\n            },\n        },\n    ))\ntask.wait_till_done()\n\nprint(task.errors)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/langchain/langchain.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LangChain -> Labelbox\n",
+        "This notebook is used to show an example workflow of getting LangChain traces into Labelbox conversation data format. Please review the [associated written guide](https://labelbox.com/guides/turn-langchain-logs-into-conversational-data-with-labelbox/) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install --upgrade --quiet  langchain langsmith langchainhub\n%pip install --upgrade --quiet  langchain-openai tiktoken pandas duckduckgo-search\n%pip install --upgrade --quiet  \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom uuid import uuid4\nimport os\nimport functools\n\n# LangSmith Imports\nfrom langsmith.client import Client\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor\nfrom langchain.agents.format_scratchpad.openai_tools import (\n    format_to_openai_tool_messages,)\nfrom langchain.agents.output_parsers.openai_tools import (\n    OpenAIToolsAgentOutputParser,)\nfrom langchain_community.tools import DuckDuckGoSearchResults\nfrom langchain_openai import ChatOpenAI\nfrom langsmith.evaluation import EvaluationResult\nfrom langsmith.schemas import Example, Run, DataType\nfrom langchain.smith import run_on_dataset\nfrom langchain.evaluation import EvaluatorType\nfrom langchain.smith import RunEvalConfig",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Setup\n",
+        "Provide a valid API key below for Labelbox, LangSmith and OpenAI in order for the notebook to work correctly."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "LB_API_KEY = \"\"\nLS_API_KEY = \"\"\nOPENAI_API_KEY = \"\"\n\nunique_id = uuid4().hex[0:8]\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = f\"Tracing Walkthrough - {unique_id}\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = LS_API_KEY\n\nos.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n\nlb_client = lb.Client(LB_API_KEY)\nclient = Client()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### LangSmith Dataset Name\n",
+        "Create a sample chat data set with an example chat based run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset_name = f\"Sample Dataset - {str(uuid4())}\"\ndataset = client.create_dataset(\n    dataset_name,\n    description=\"A sample dataset in LangSmith.\",\n    data_type=DataType.chat,\n)\nclient.create_chat_example(\n    messages=[\n        {\n            \"type\": \"ai\",\n            \"data\": {\n                \"content\": \"hi how are you\"\n            }\n        },\n        {\n            \"type\": \"human\",\n            \"data\": {\n                \"content\": \"Im doing great how about you\"\n            }\n        },\n    ],\n    generations={\n        \"type\": \"ai\",\n        \"data\": {\n            \"content\": \"Im doing great\"\n        },\n    },  # Custom model output\n    dataset_id=dataset.id,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### LangSmith\n",
+        "Below is an example of running a list of raw text evaluation strings and a LangSmith example run with Chat Gpt 3.5. Please review [LangSmith Docs](https://docs.smith.langchain.com/) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "tools = [\n    DuckDuckGoSearchResults(\n        name=\"duck_duck_go\"),  # General internet search using DuckDuckGo\n]\n\nllm = ChatOpenAI(\n    model=\"gpt-3.5-turbo-16k\",\n    temperature=0,\n)\n\n\n# Since chains can be stateful (e.g. they can have memory), we provide\n# a way to initialize a new chain for each row in the dataset. This is done\n# by passing in a factory function that returns a new chain for each row.\ndef create_agent(prompt, llm_with_tools):\n    runnable_agent = ({\n        \"input\":\n            lambda x: x[\"input\"],\n        \"agent_scratchpad\":\n            lambda x: format_to_openai_tool_messages(x[\"intermediate_steps\"]),\n    } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser())\n    return AgentExecutor(agent=runnable_agent,\n                         tools=tools,\n                         handle_parsing_errors=True)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "def max_pred_length(runs, examples):\n    predictions = [len(run.outputs[\"output\"]) for run in runs]\n    return EvaluationResult(key=\"max_pred_length\", score=max(predictions))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "def check_not_idk(run: Run, example: Example):\n    \"\"\"Illustration of a custom evaluator.\"\"\"\n    agent_response = run.outputs[\"output\"]\n    if \"don't know\" in agent_response or \"not sure\" in agent_response:\n        score = 0\n    else:\n        score = 1\n    # You can access the dataset labels in example.outputs[key]\n    # You can also access the model inputs in run.inputs[key]\n    return EvaluationResult(\n        key=\"not_uncertain\",\n        score=score,\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "evaluation_config = RunEvalConfig(\n    # Evaluators can either be an evaluator type (e.g., \"qa\", \"criteria\", \"embedding_distance\", etc.) or a configuration for that evaluator\n    evaluators=[\n        check_not_idk,\n        # Measures whether a QA response is \"Correct\", based on a reference answer\n        # You can also select via the raw string \"qa\"\n        EvaluatorType.QA,\n        # Measure the embedding distance between the output and the reference answer\n        # Equivalent to: EvalConfig.EmbeddingDistance(embeddings=OpenAIEmbeddings())\n        EvaluatorType.EMBEDDING_DISTANCE,\n        # Grade whether the output satisfies the stated criteria.\n        # You can select a default one such as \"helpfulness\" or provide your own.\n        RunEvalConfig.LabeledCriteria(\"helpfulness\"),\n        # The LabeledScoreString evaluator outputs a score on a scale from 1-10.\n        # You can use default criteria or write our own rubric\n        RunEvalConfig.LabeledScoreString(\n            {\n                \"accuracy\":\n                    \"\"\"\nScore 1: The answer is completely unrelated to the reference.\nScore 3: The answer has minor relevance but does not align with the reference.\nScore 5: The answer has moderate relevance but contains inaccuracies.\nScore 7: The answer aligns with the reference but has minor errors or omissions.\nScore 10: The answer is completely accurate and aligns perfectly with the reference.\"\"\"\n            },\n            normalize_by=10,\n        ),\n    ],\n    batch_evaluators=[max_pred_length],\n)\n\nllm_with_tools = llm.bind_tools(tools)\nprompt = hub.pull(\"gabe/labelboxtutorialdemo\"\n                 )  # Change prompt in LangSmith hub to reflect example run\n\nchain_results = run_on_dataset(\n    dataset_name=dataset_name,\n    llm_or_chain_factory=functools.partial(create_agent,\n                                           prompt=prompt,\n                                           llm_with_tools=llm_with_tools),\n    evaluation=evaluation_config,\n    verbose=True,\n    client=client,\n    project_name=f\"tools-agent-test-5d466cbc-{unique_id}\",\n    # Project metadata communicates the experiment parameters,\n    # Useful for reviewing the test results\n    project_metadata={\n        \"env\": \"testing-notebook\",\n        \"model\": \"gpt-3.5-turbo\",\n        \"prompt\": \"5d466cbc\",\n    },\n)\n\n# Sometimes, the agent will error due to parsing issues, incompatible tool inputs, etc.\n# These are logged as warnings here and captured as errors in the tracing UI.",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Labelbox\n",
+        "Below converts the results of the above LangSmith run to Labelbox conversation text. Please review [Labelbox conversation data docs](https://docs.labelbox.com/docs/llm-human-preference) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "def import_conversational(\n        chain_results: dict[str:str],\n        user_id_dict: dict[str:dict[str:str]]) -> dict[str:str]:\n    \"\"\"Converts LangSmith chain_results from model invocation to Labelbox conversation data for model response comparison. Output is based on popular model response and custom model response towards prompts.\n\n    Args:\n      chain_results(dict[str:str]): Results from LangSmith model invocation against example dataset runs.\n      user_id_dict(dict[str:dict[str:str]]): Dictionary mapping of LangSmith example run type to Labelbox chat names and alignment.\n\n    Returns:\n        dict[str:str]: Labelbox conversation text format\n    \"\"\"\n    lb_conversations = []\n    for key, conversational in chain_results[\"results\"].items():\n        lb_conversation = {\n            \"row_data\": {\n                \"type\": \"application/vnd.labelbox.conversational\",\n                \"version\": 1,\n                \"messages\": [],\n                \"modelOutputs\": [],\n            },\n            \"global_key\": key,\n            \"media_type\": \"CONVERSATIONAL\",\n        }\n        if \"input\" in conversational[\"output\"]:\n            for i, input in enumerate(conversational[\"output\"][\"input\"]):\n                lb_conversation[\"row_data\"][\"messages\"].append({\n                    \"content\": input[\"data\"][\"content\"],\n                    \"timestampUsec\": i + 1,\n                    \"user\": {\n                        \"userId\": user_id_dict[input[\"type\"]][\"id\"],\n                        \"name\": input[\"type\"],\n                    },\n                    \"canLabel\": True,\n                    \"align\": user_id_dict[input[\"type\"]][\"align\"],\n                    \"messageId\": str(uuid4()),\n                })\n\n        # Custom model output\n        if \"reference\" in conversational:\n            reference = conversational[\"reference\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Custom Model Response\",\n                \"content\": reference[\"data\"][\"content\"],\n                \"modelConfigName\": \"Custom Model - Example Config\",\n            })\n\n        # Popular model output\n        if \"output\" in conversational[\"output\"]:\n            output = conversational[\"output\"][\"output\"]\n            lb_conversation[\"row_data\"][\"modelOutputs\"].append({\n                \"title\": \"Popular LLM Response\",\n                \"content\": output,\n                \"modelConfigName\": \"GPT-3.5 - Example Config\",\n            })\n\n        lb_conversations.append(lb_conversation)\n    return lb_conversations",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Labelbox Dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset = lb_client.create_dataset(name=\"demo_langchain\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Attach Conversation Text to Dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "task = dataset.create_data_rows(\n    import_conversational(\n        chain_results,\n        {\n            \"human\": {\n                \"id\": \"human\",\n                \"align\": \"right\"\n            },\n            \"ai\": {\n                \"id\": \"ai\",\n                \"align\": \"left\"\n            },\n        },\n    ))\ntask.wait_till_done()\n\nprint(task.errors)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/sam/meta_sam.ipynb b/examples/integrations/sam/meta_sam.ipynb
index 96c1ef9a0..4802f7603 100644
--- a/examples/integrations/sam/meta_sam.ipynb
+++ b/examples/integrations/sam/meta_sam.ipynb
@@ -1,251 +1,249 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup\n",
-                "This notebook is used to show how to use Meta's Segment Anything model to create masks that can then be uploaded to a Labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "from IPython import display\n\ndisplay.clear_output()\n\nimport ultralytics\n\nultralytics.checks()\n\nimport cv2\nimport numpy as np\nfrom ultralytics import YOLO\nfrom IPython.display import display, Image\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import (\n    sam_model_registry,\n    SamAutomaticMaskGenerator,\n    SamPredictor,\n)\nimport os\nimport urllib.request\nimport uuid\n\nimport labelbox as lb\nimport labelbox.types as lb_types\n\nHOME = os.getcwd()\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Predicting bounding boxes around common objects using YOLOv8\n",
-                "\n",
-                "First, we start with loading the YOLOv8 model, getting a sample image, and running the model on it to generate bounding boxes around some common objects."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Utilize YOLOV8 to Create Bounding Boxes\n",
-                "\n",
-                "We use YOLOV8 in this demo to obtain bounding boxes around our images that we can later feed into SAM for our masks."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Below we run inference on a image using the YOLOv8 model."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# You can also use the Labelbox Client API to get specific images or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n\nIMAGE_PATH = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/chairs.jpeg\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "model = YOLO(f\"{HOME}/yolov8n.pt\")\nresults = model.predict(source=IMAGE_PATH, conf=0.25)\n\n# print(results[0].boxes.xyxy) # print bounding box coordinates\n\n# print(results[0].boxes.conf) # print confidence scores\n\n# for c in results[0].boxes.cls:\n# print(model.names[int(c)]) # print predicted classes",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Below we visualize the bounding boxes on the image using CV2."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "image_bgr = cv2.imread(\"./chairs.jpeg\")\n\nfor box in results[0].boxes.xyxy:\n    cv2.rectangle(\n        image_bgr,\n        (int(box[0]), int(box[1])),\n        (int(box[2]), int(box[3])),\n        (0, 255, 0),\n        2,\n    )\n\nif IN_COLAB:\n    cv2_imshow(image_bgr)\nelse:\n    cv2.imshow(\"demo\", image_bgr)\n    cv2.waitKey()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Predicting segmentation masks using Meta's Segment Anything model\n",
-                "\n",
-                "Now we load Meta's Segment Anything model and feed the bounding boxes to it, so it can generate segmentation masks within them."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Download SAM model weights\n\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nMODEL_TYPE = \"vit_h\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(\n    device=DEVICE)\nmask_predictor = SamPredictor(sam)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n    results[0].boxes.xyxy, image_bgr.shape[:2])\n\nmask_predictor.set_image(image_bgr)\n\nmasks, scores, logits = mask_predictor.predict_torch(\n    boxes=transformed_boxes,\n    multimask_output=False,\n    point_coords=None,\n    point_labels=None,\n)\nmasks = np.array(masks.cpu())\n\n# print(masks)\n# print(scores)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Here we visualize the segmentation masks drawn on the image."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n\nfinal_mask = None\nfor i in range(len(masks) - 1):\n    if final_mask is None:\n        final_mask = np.bitwise_or(masks[i][0], masks[i + 1][0])\n    else:\n        final_mask = np.bitwise_or(final_mask, masks[i + 1][0])\n\nplt.figure(figsize=(10, 10))\nplt.imshow(image_rgb)\nplt.axis(\"off\")\nplt.imshow(final_mask, cmap=\"gray\", alpha=0.7)\n\nplt.show()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Uploading predicted segmentation masks with class names to Labelbox using Python SDK"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox ObjectAnnotation of type mask for each predicted mask\n\n# Identifying what values in the numpy array correspond to the mask annotation\ncolor = (1, 1, 1)\n\nclass_names = []\nfor c in results[0].boxes.cls:\n    class_names.append(model.names[int(c)])\n\nannotations = []\nfor idx, mask in enumerate(masks):\n    mask_data = lb_types.MaskData.from_2D_arr(np.asarray(mask[0],\n                                                         dtype=\"uint8\"))\n    mask_annotation = lb_types.ObjectAnnotation(\n        name=class_names[\n            idx],  # this is the class predicted in Step 1 (object detector)\n        value=lb_types.Mask(mask=mask_data, color=color),\n    )\n    annotations.append(mask_annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new dataset\n\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = \"my_unique_global_key\"\n\ntest_img_url = {\"row_data\": IMAGE_PATH, \"global_key\": global_key}\n\ndataset = client.create_dataset(name=\"auto-mask-classification-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new ontology if you don't have one\n\n# Add all unique classes detected in Step 1\ntools = []\nfor name in set(class_names):\n    tools.append(lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=name))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"auto-mask-classification-ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"Demo Chair\").get_one()\n\n# ontology = client.get_ontology(\"clhee8kzt049v094h7stq7v25\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"auto-mask-classification-project\",\n                                media_type=lb.MediaType.Image)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new batch of data for the project you specified above\n\ndata_row_ids = client.get_data_row_ids_for_global_keys([global_key])[\"results\"]\n\nbatch = project.create_batch(\n    \"auto-mask-classification-batch\",  # each batch in a project must have a unique name\n    data_rows=data_row_ids,\n    # you can also specify global_keys instead of data_rows\n    # global_keys=[global_key],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the predictions to your specified project and data rows as pre-labels\n\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# dataset.delete()\n# project.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup\n",
+        "This notebook is used to show how to use Meta's Segment Anything model to create masks that can then be uploaded to a Labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "from IPython import display\n\ndisplay.clear_output()\n\nimport ultralytics\n\nultralytics.checks()\n\nimport cv2\nimport numpy as np\nfrom ultralytics import YOLO\nfrom IPython.display import display, Image\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import (\n    sam_model_registry,\n    SamAutomaticMaskGenerator,\n    SamPredictor,\n)\nimport os\nimport urllib.request\nimport uuid\n\nimport labelbox as lb\nimport labelbox.types as lb_types\n\nHOME = os.getcwd()\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Predicting bounding boxes around common objects using YOLOv8\n",
+        "\n",
+        "First, we start with loading the YOLOv8 model, getting a sample image, and running the model on it to generate bounding boxes around some common objects."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Utilize YOLOV8 to Create Bounding Boxes\n",
+        "\n",
+        "We use YOLOV8 in this demo to obtain bounding boxes around our images that we can later feed into SAM for our masks."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Below we run inference on a image using the YOLOv8 model."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# You can also use the Labelbox Client API to get specific images or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\n\nIMAGE_PATH = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/chairs.jpeg\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "model = YOLO(f\"{HOME}/yolov8n.pt\")\nresults = model.predict(source=IMAGE_PATH, conf=0.25)\n\n# print(results[0].boxes.xyxy) # print bounding box coordinates\n\n# print(results[0].boxes.conf) # print confidence scores\n\n# for c in results[0].boxes.cls:\n# print(model.names[int(c)]) # print predicted classes",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Below we visualize the bounding boxes on the image using CV2."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "image_bgr = cv2.imread(\"./chairs.jpeg\")\n\nfor box in results[0].boxes.xyxy:\n    cv2.rectangle(\n        image_bgr,\n        (int(box[0]), int(box[1])),\n        (int(box[2]), int(box[3])),\n        (0, 255, 0),\n        2,\n    )\n\nif IN_COLAB:\n    cv2_imshow(image_bgr)\nelse:\n    cv2.imshow(\"demo\", image_bgr)\n    cv2.waitKey()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Predicting segmentation masks using Meta's Segment Anything model\n",
+        "\n",
+        "Now we load Meta's Segment Anything model and feed the bounding boxes to it, so it can generate segmentation masks within them."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Download SAM model weights\n\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nMODEL_TYPE = \"vit_h\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(\n    device=DEVICE)\nmask_predictor = SamPredictor(sam)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n    results[0].boxes.xyxy, image_bgr.shape[:2])\n\nmask_predictor.set_image(image_bgr)\n\nmasks, scores, logits = mask_predictor.predict_torch(\n    boxes=transformed_boxes,\n    multimask_output=False,\n    point_coords=None,\n    point_labels=None,\n)\nmasks = np.array(masks.cpu())\n\n# print(masks)\n# print(scores)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Here we visualize the segmentation masks drawn on the image."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n\nfinal_mask = None\nfor i in range(len(masks) - 1):\n    if final_mask is None:\n        final_mask = np.bitwise_or(masks[i][0], masks[i + 1][0])\n    else:\n        final_mask = np.bitwise_or(final_mask, masks[i + 1][0])\n\nplt.figure(figsize=(10, 10))\nplt.imshow(image_rgb)\nplt.axis(\"off\")\nplt.imshow(final_mask, cmap=\"gray\", alpha=0.7)\n\nplt.show()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Uploading predicted segmentation masks with class names to Labelbox using Python SDK"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox ObjectAnnotation of type mask for each predicted mask\n\n# Identifying what values in the numpy array correspond to the mask annotation\ncolor = (1, 1, 1)\n\nclass_names = []\nfor c in results[0].boxes.cls:\n    class_names.append(model.names[int(c)])\n\nannotations = []\nfor idx, mask in enumerate(masks):\n    mask_data = lb_types.MaskData.from_2D_arr(np.asarray(mask[0],\n                                                         dtype=\"uint8\"))\n    mask_annotation = lb_types.ObjectAnnotation(\n        name=class_names[\n            idx],  # this is the class predicted in Step 1 (object detector)\n        value=lb_types.Mask(mask=mask_data, color=color),\n    )\n    annotations.append(mask_annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new dataset\n\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = \"my_unique_global_key\"\n\ntest_img_url = {\"row_data\": IMAGE_PATH, \"global_key\": global_key}\n\ndataset = client.create_dataset(name=\"auto-mask-classification-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new ontology if you don't have one\n\n# Add all unique classes detected in Step 1\ntools = []\nfor name in set(class_names):\n    tools.append(lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=name))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"auto-mask-classification-ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"Demo Chair\").get_one()\n\n# ontology = client.get_ontology(\"clhee8kzt049v094h7stq7v25\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"auto-mask-classification-project\",\n                                media_type=lb.MediaType.Image)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new batch of data for the project you specified above\n\ndata_row_ids = client.get_data_row_ids_for_global_keys([global_key])[\"results\"]\n\nbatch = project.create_batch(\n    \"auto-mask-classification-batch\",  # each batch in a project must have a unique name\n    data_rows=data_row_ids,\n    # you can also specify global_keys instead of data_rows\n    # global_keys=[global_key],  # paginated collection of data row objects, list of data row ids or global keys\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the predictions to your specified project and data rows as pre-labels\n\nupload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\n\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# dataset.delete()\n# project.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/integrations/sam/meta_sam_video.ipynb b/examples/integrations/sam/meta_sam_video.ipynb
index 45e425c36..76e64105e 100644
--- a/examples/integrations/sam/meta_sam_video.ipynb
+++ b/examples/integrations/sam/meta_sam_video.ipynb
@@ -1,235 +1,233 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Setup\n",
-                "\n",
-                "This notebook is used to show how to use Meta's Segment Anything model and YOLO to create masks for videos that can then be uploaded to a Labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### General dependencies"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import os\nimport urllib\nimport cv2\nimport PIL\nfrom PIL import Image\nimport numpy as np\nimport uuid\nimport tempfile\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow\n\nfrom IPython import display\n\ndisplay.clear_output()\nfrom IPython.display import display, Image\nfrom io import BytesIO\n\n# YOLOv8 dependencies\nimport ultralytics\n\nultralytics.checks()\nfrom ultralytics import YOLO\n\n# SAM dependencies\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Labelbox dependencies\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# You can also use the Labelbox Client API to get specific videos or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\nHOME = os.getcwd()\nVIDEO_PATH = os.path.join(HOME, \"skateboarding.mp4\")\n\nif not os.path.isfile(VIDEO_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/skateboarding.mp4\",\n        \"skateboarding.mp4\",\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### YOLOv8 setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Instantiate YOLOv8 model\nmodel = YOLO(f\"{HOME}/yolov8n.pt\")\ncolors = np.random.randint(0, 256, size=(len(model.names), 3))\n\nprint(model.names)\n\n# Specify which classes you care about. The rest of classes will be filtered out.\nchosen_class_ids = [0]  # person",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### SAM setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Download SAM model weights\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Instantiate SAM model\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nsam = sam_model_registry[\"vit_h\"](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\nmask_predictor = SamPredictor(sam)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Labelbox setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Helper functions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Cast color to ints\ndef get_color(color):\n    return (int(color[0]), int(color[1]), int(color[2]))\n\n\n# Get video dimensions\ndef get_video_dimensions(input_cap):\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    return height, width\n\n\n# Get output video writer with same dimensions and fps as input video\ndef get_output_video_writer(input_cap, output_path):\n    # Get the video's properties (width, height, FPS)\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = int(input_cap.get(cv2.CAP_PROP_FPS))\n\n    # Define the output video file\n    output_codec = cv2.VideoWriter_fourcc(*\"mp4v\")  # MP4 codec\n    output_video = cv2.VideoWriter(output_path, output_codec, fps,\n                                   (width, height))\n\n    return output_video\n\n\n# Visualize a video frame with bounding boxes, classes and confidence scores\ndef visualize_detections(frame, boxes, conf_thresholds, class_ids):\n    frame_copy = np.copy(frame)\n    for idx in range(len(boxes)):\n        class_id = int(class_ids[idx])\n        conf = float(conf_thresholds[idx])\n        x1, y1, x2, y2 = (\n            int(boxes[idx][0]),\n            int(boxes[idx][1]),\n            int(boxes[idx][2]),\n            int(boxes[idx][3]),\n        )\n        color = colors[class_id]\n        label = f\"{model.names[class_id]}: {conf:.2f}\"\n        cv2.rectangle(frame_copy, (x1, y1), (x2, y2), get_color(color), 2)\n        cv2.putText(\n            frame_copy,\n            label,\n            (x1, y1 - 10),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            0.9,\n            get_color(color),\n            2,\n        )\n    return frame_copy\n\n\ndef add_color_to_mask(mask, color):\n    next_mask = mask.astype(np.uint8)\n    next_mask = np.expand_dims(next_mask, 0).repeat(3, axis=0)\n    next_mask = np.moveaxis(next_mask, 0, -1)\n    return next_mask * color\n\n\n# Merge masks into a single, multi-colored mask\ndef merge_masks_colored(masks, class_ids):\n    filtered_class_ids = []\n    filtered_masks = []\n    for idx, cid in enumerate(class_ids):\n        if int(cid) in chosen_class_ids:\n            filtered_class_ids.append(cid)\n            filtered_masks.append(masks[idx])\n\n    merged_with_colors = add_color_to_mask(\n        filtered_masks[0][0],\n        get_color(colors[int(filtered_class_ids[0])])).astype(np.uint8)\n\n    if len(filtered_masks) == 1:\n        return merged_with_colors\n\n    for i in range(1, len(filtered_masks)):\n        curr_mask_with_colors = add_color_to_mask(\n            filtered_masks[i][0], get_color(colors[int(filtered_class_ids[i])]))\n        merged_with_colors = np.bitwise_or(merged_with_colors,\n                                           curr_mask_with_colors)\n\n    return merged_with_colors.astype(np.uint8)\n\n\ndef get_instance_uri(client: lb.Client, global_key, array):\n    \"\"\"Reads a numpy array into a temp Labelbox data row to-be-uploaded to Labelbox\n    Args:\n        client        :   Required (lb.Client) - Labelbox Client object\n        global_key    :   Required (str) - Data row global key\n        array         :   Required (np.ndarray) - NumPy ndarray representation of an image\n    Returns:\n        Temp Labelbox data row to-be-uploaded to Labelbox as row data\n    \"\"\"\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n    # Convert PIL image to PNG file bytes\n    image_as_bytes = BytesIO()\n    image_as_pil.save(image_as_bytes, format=\"PNG\")\n    image_as_bytes = image_as_bytes.getvalue()\n    # Convert PNG file bytes to a temporary Labelbox URL\n    url = client.upload_data(\n        content=image_as_bytes,\n        filename=f\"{uuid.uuid4()}{global_key}\",\n        content_type=\"image/jpeg\",\n        sign=True,\n    )\n    # Return the URL\n    return url\n\n\ndef get_local_instance_uri(array):\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n\n    with tempfile.NamedTemporaryFile(suffix=\".png\",\n                                     dir=\"/content\",\n                                     delete=False) as temp_file:\n        image_as_pil.save(temp_file)\n        file_name = temp_file.name\n\n    # Return the URL\n    return file_name\n\n\ndef create_mask_frame(frame_num, instance_uri):\n    return lb_types.MaskFrame(index=frame_num, instance_uri=instance_uri)\n\n\ndef create_mask_instances(class_ids):\n    instances = []\n    for cid in list(set(class_ids)):  # get unique class ids\n        if int(cid) in chosen_class_ids:\n            color = get_color(colors[int(cid)])\n            name = model.names[int(cid)]\n            instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n    return instances\n\n\ndef create_video_mask_annotation(frames, instance):\n    return lb_types.VideoMaskAnnotation(frames=frames, instances=[instance])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Labelbox create dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new dataset\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = os.path.basename(VIDEO_PATH)\n\nasset = {\n    \"row_data\": VIDEO_PATH,\n    \"global_key\": global_key,\n    \"media_type\": \"VIDEO\",\n}\n\ndataset = client.create_dataset(name=\"yolo-sam-video-masks-dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Run through YOLOv8 on the video once quickly to get unique class ids present\n# This will inform which classes we add to the ontology\n\ncap = cv2.VideoCapture(VIDEO_PATH)\n\nunique_class_ids = set()\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frame number\", frame_num)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 and get class ids predicted\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n    for cid in detections[0].boxes.cls:\n        unique_class_ids.add(int(cid))\n    frame_num += 1\n\ncap.release()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "unique_class_ids",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new ontology if you don't have one\n\n# Add all chosen classes into the ontology\ntools = []\nfor cls in chosen_class_ids:\n    tools.append(\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=model.names[cls]))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"yolo-sam-video-masks-ontology\",\n    ontology_builder.asdict(),\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"yolo-sam-video-masks-ontology\").get_one()\n\n# ontology = client.get_ontology(\"\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"yolo-sam-video-masks-project\",\n                                media_type=lb.MediaType.Video)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new batch of data for the project you specified above\n\n# Uncomment if you are using `data_rows` parameter below\n# data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n\nbatch = project.create_batch(\n    \"yolo-sam-video-masks-project\",  # each batch in a project must have a unique name\n    # you can also specify global_keys instead of data_rows\n    global_keys=[global_key],\n    # you can also specify data_rows instead of global_keys\n    # data_rows=data_row_ids,\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "tools = ontology.tools()\n\nfeature_schema_ids = dict()\nfor tool in tools:\n    feature_schema_ids[tool.name] = tool.feature_schema_id\n\nprint(feature_schema_ids)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Loop through each frame of video and process it\n",
-                "* Run YOLOv8 and then SAM on each frame, and write visualization videos to disk\n",
-                "* This might take a few minutes to run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "cap = cv2.VideoCapture(VIDEO_PATH)\n\noutput_video_boxes = get_output_video_writer(\n    cap, \"/content/skateboarding_boxes.mp4\")\noutput_video_masks = get_output_video_writer(\n    cap, \"/content/skateboarding_masks.mp4\")\nmask_frames = []\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frames\", frame_num, \"-\", frame_num + 29)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 to get detections\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n\n    # Write detections to output video\n    frame_with_detections = visualize_detections(\n        frame,\n        detections[0].boxes.cpu().xyxy,\n        detections[0].boxes.cpu().conf,\n        detections[0].boxes.cpu().cls,\n    )\n    output_video_boxes.write(frame_with_detections)\n\n    # Run frame and detections through SAM to get masks\n    transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n        detections[0].boxes.xyxy, list(get_video_dimensions(cap)))\n    if len(transformed_boxes) == 0:\n        print(\"No boxes found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    mask_predictor.set_image(frame)\n    masks, scores, logits = mask_predictor.predict_torch(\n        boxes=transformed_boxes,\n        multimask_output=False,\n        point_coords=None,\n        point_labels=None,\n    )\n    masks = np.array(masks.cpu())\n    if masks is None or len(masks) == 0:\n        print(\"No masks found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    merged_colored_mask = merge_masks_colored(masks, detections[0].boxes.cls)\n\n    # Write masks to output video\n    image_combined = cv2.addWeighted(frame, 0.7, merged_colored_mask, 0.7, 0)\n    output_video_masks.write(image_combined)\n\n    # Create video mask annotation for upload to Labelbox\n    instance_uri = get_instance_uri(client, global_key, merged_colored_mask)\n    mask_frame = create_mask_frame(frame_num, instance_uri)\n    mask_frames.append(mask_frame)\n    print(\"Boxes found on frame\", frame_num)\n    frame_num += 1\n\n    # For the purposes of this demo, only look at the first 80 frames\n    if frame_num > 80:\n        break\n\ncap.release()\noutput_video_boxes.release()\noutput_video_masks.release()\ncv2.destroyAllWindows()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Create annotations for LB upload\nmask_instances = create_mask_instances(unique_class_ids)\nannotations = []\nfor instance in mask_instances:\n    annotations.append(create_video_mask_annotation(mask_frames, instance))\n\nlabels = []\nlabels.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Upload the predictions to your specified project and data rows as pre-labels\n",
-                "\n",
-                "Note: This may take a few minutes, depending on size of video and number of masks"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/integrations/sam/meta_sam_video.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Setup\n",
+        "\n",
+        "This notebook is used to show how to use Meta's Segment Anything model and YOLO to create masks for videos that can then be uploaded to a Labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### General dependencies"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q ultralytics==8.0.20\n%pip install -q \"git+https://github.com/facebookresearch/segment-anything.git\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Check if in google colab\ntry:\n    import google.colab\n\n    IN_COLAB = True\nexcept:\n    IN_COLAB = False",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import os\nimport urllib\nimport cv2\nimport PIL\nfrom PIL import Image\nimport numpy as np\nimport uuid\nimport tempfile\n\nif IN_COLAB:\n    from google.colab.patches import cv2_imshow\n\nfrom IPython import display\n\ndisplay.clear_output()\nfrom IPython.display import display, Image\nfrom io import BytesIO\n\n# YOLOv8 dependencies\nimport ultralytics\n\nultralytics.checks()\nfrom ultralytics import YOLO\n\n# SAM dependencies\nimport torch\nimport matplotlib.pyplot as plt\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Labelbox dependencies\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# You can also use the Labelbox Client API to get specific videos or an entire\n# dataset from your Catalog. Refer to these docs:\n# https://labelbox-python.readthedocs.io/en/latest/#labelbox.client.Client.get_data_row\nHOME = os.getcwd()\nVIDEO_PATH = os.path.join(HOME, \"skateboarding.mp4\")\n\nif not os.path.isfile(VIDEO_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/skateboarding.mp4\",\n        \"skateboarding.mp4\",\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### YOLOv8 setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Instantiate YOLOv8 model\nmodel = YOLO(f\"{HOME}/yolov8n.pt\")\ncolors = np.random.randint(0, 256, size=(len(model.names), 3))\n\nprint(model.names)\n\n# Specify which classes you care about. The rest of classes will be filtered out.\nchosen_class_ids = [0]  # person",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### SAM setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Download SAM model weights\nCHECKPOINT_PATH = os.path.join(HOME, \"sam_vit_h_4b8939.pth\")\n\nif not os.path.isfile(CHECKPOINT_PATH):\n    req = urllib.request.urlretrieve(\n        \"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\",\n        \"sam_vit_h_4b8939.pth\",\n    )",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Instantiate SAM model\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nsam = sam_model_registry[\"vit_h\"](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)\nmask_predictor = SamPredictor(sam)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Labelbox setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Helper functions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Cast color to ints\ndef get_color(color):\n    return (int(color[0]), int(color[1]), int(color[2]))\n\n\n# Get video dimensions\ndef get_video_dimensions(input_cap):\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    return height, width\n\n\n# Get output video writer with same dimensions and fps as input video\ndef get_output_video_writer(input_cap, output_path):\n    # Get the video's properties (width, height, FPS)\n    width = int(input_cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    height = int(input_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = int(input_cap.get(cv2.CAP_PROP_FPS))\n\n    # Define the output video file\n    output_codec = cv2.VideoWriter_fourcc(*\"mp4v\")  # MP4 codec\n    output_video = cv2.VideoWriter(output_path, output_codec, fps,\n                                   (width, height))\n\n    return output_video\n\n\n# Visualize a video frame with bounding boxes, classes and confidence scores\ndef visualize_detections(frame, boxes, conf_thresholds, class_ids):\n    frame_copy = np.copy(frame)\n    for idx in range(len(boxes)):\n        class_id = int(class_ids[idx])\n        conf = float(conf_thresholds[idx])\n        x1, y1, x2, y2 = (\n            int(boxes[idx][0]),\n            int(boxes[idx][1]),\n            int(boxes[idx][2]),\n            int(boxes[idx][3]),\n        )\n        color = colors[class_id]\n        label = f\"{model.names[class_id]}: {conf:.2f}\"\n        cv2.rectangle(frame_copy, (x1, y1), (x2, y2), get_color(color), 2)\n        cv2.putText(\n            frame_copy,\n            label,\n            (x1, y1 - 10),\n            cv2.FONT_HERSHEY_SIMPLEX,\n            0.9,\n            get_color(color),\n            2,\n        )\n    return frame_copy\n\n\ndef add_color_to_mask(mask, color):\n    next_mask = mask.astype(np.uint8)\n    next_mask = np.expand_dims(next_mask, 0).repeat(3, axis=0)\n    next_mask = np.moveaxis(next_mask, 0, -1)\n    return next_mask * color\n\n\n# Merge masks into a single, multi-colored mask\ndef merge_masks_colored(masks, class_ids):\n    filtered_class_ids = []\n    filtered_masks = []\n    for idx, cid in enumerate(class_ids):\n        if int(cid) in chosen_class_ids:\n            filtered_class_ids.append(cid)\n            filtered_masks.append(masks[idx])\n\n    merged_with_colors = add_color_to_mask(\n        filtered_masks[0][0],\n        get_color(colors[int(filtered_class_ids[0])])).astype(np.uint8)\n\n    if len(filtered_masks) == 1:\n        return merged_with_colors\n\n    for i in range(1, len(filtered_masks)):\n        curr_mask_with_colors = add_color_to_mask(\n            filtered_masks[i][0], get_color(colors[int(filtered_class_ids[i])]))\n        merged_with_colors = np.bitwise_or(merged_with_colors,\n                                           curr_mask_with_colors)\n\n    return merged_with_colors.astype(np.uint8)\n\n\ndef get_instance_uri(client: lb.Client, global_key, array):\n    \"\"\"Reads a numpy array into a temp Labelbox data row to-be-uploaded to Labelbox\n    Args:\n        client        :   Required (lb.Client) - Labelbox Client object\n        global_key    :   Required (str) - Data row global key\n        array         :   Required (np.ndarray) - NumPy ndarray representation of an image\n    Returns:\n        Temp Labelbox data row to-be-uploaded to Labelbox as row data\n    \"\"\"\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n    # Convert PIL image to PNG file bytes\n    image_as_bytes = BytesIO()\n    image_as_pil.save(image_as_bytes, format=\"PNG\")\n    image_as_bytes = image_as_bytes.getvalue()\n    # Convert PNG file bytes to a temporary Labelbox URL\n    url = client.upload_data(\n        content=image_as_bytes,\n        filename=f\"{uuid.uuid4()}{global_key}\",\n        content_type=\"image/jpeg\",\n        sign=True,\n    )\n    # Return the URL\n    return url\n\n\ndef get_local_instance_uri(array):\n    # Convert array to PIL image\n    image_as_pil = PIL.Image.fromarray(array)\n\n    with tempfile.NamedTemporaryFile(suffix=\".png\",\n                                     dir=\"/content\",\n                                     delete=False) as temp_file:\n        image_as_pil.save(temp_file)\n        file_name = temp_file.name\n\n    # Return the URL\n    return file_name\n\n\ndef create_mask_frame(frame_num, instance_uri):\n    return lb_types.MaskFrame(index=frame_num, instance_uri=instance_uri)\n\n\ndef create_mask_instances(class_ids):\n    instances = []\n    for cid in list(set(class_ids)):  # get unique class ids\n        if int(cid) in chosen_class_ids:\n            color = get_color(colors[int(cid)])\n            name = model.names[int(cid)]\n            instances.append(lb_types.MaskInstance(color_rgb=color, name=name))\n    return instances\n\n\ndef create_video_mask_annotation(frames, instance):\n    return lb_types.VideoMaskAnnotation(frames=frames, instances=[instance])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Labelbox create dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new dataset\n# read more here: https://docs.labelbox.com/reference/data-row-global-keys\nglobal_key = os.path.basename(VIDEO_PATH)\n\nasset = {\n    \"row_data\": VIDEO_PATH,\n    \"global_key\": global_key,\n    \"media_type\": \"VIDEO\",\n}\n\ndataset = client.create_dataset(name=\"yolo-sam-video-masks-dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Run through YOLOv8 on the video once quickly to get unique class ids present\n# This will inform which classes we add to the ontology\n\ncap = cv2.VideoCapture(VIDEO_PATH)\n\nunique_class_ids = set()\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frame number\", frame_num)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 and get class ids predicted\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n    for cid in detections[0].boxes.cls:\n        unique_class_ids.add(int(cid))\n    frame_num += 1\n\ncap.release()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "unique_class_ids",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new ontology if you don't have one\n\n# Add all chosen classes into the ontology\ntools = []\nfor cls in chosen_class_ids:\n    tools.append(\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=model.names[cls]))\n\nontology_builder = lb.OntologyBuilder(classifications=[], tools=tools)\n\nontology = client.create_ontology(\n    \"yolo-sam-video-masks-ontology\",\n    ontology_builder.asdict(),\n)\n\n# Or get an existing ontology by name or ID (uncomment one of the below)\n\n# ontology = client.get_ontologies(\"yolo-sam-video-masks-ontology\").get_one()\n\n# ontology = client.get_ontology(\"\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new project if you don't have one\n\n# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n# Queue mode will be deprecated once dataset mode is deprecated\nproject = client.create_project(name=\"yolo-sam-video-masks-project\",\n                                media_type=lb.MediaType.Video)\n\n# Or get an existing project by ID (uncomment the below)\n\n# project = get_project(\"fill_in_project_id\")\n\n# If the project already has an ontology set up, comment out this line\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new batch of data for the project you specified above\n\n# Uncomment if you are using `data_rows` parameter below\n# data_row_ids = client.get_data_row_ids_for_global_keys([global_key])['results']\n\nbatch = project.create_batch(\n    \"yolo-sam-video-masks-project\",  # each batch in a project must have a unique name\n    # you can also specify global_keys instead of data_rows\n    global_keys=[global_key],\n    # you can also specify data_rows instead of global_keys\n    # data_rows=data_row_ids,\n    priority=1,  # priority between 1(highest) - 5(lowest)\n)\n\nprint(f\"Batch: {batch}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "tools = ontology.tools()\n\nfeature_schema_ids = dict()\nfor tool in tools:\n    feature_schema_ids[tool.name] = tool.feature_schema_id\n\nprint(feature_schema_ids)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Loop through each frame of video and process it\n",
+        "* Run YOLOv8 and then SAM on each frame, and write visualization videos to disk\n",
+        "* This might take a few minutes to run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "cap = cv2.VideoCapture(VIDEO_PATH)\n\noutput_video_boxes = get_output_video_writer(\n    cap, \"/content/skateboarding_boxes.mp4\")\noutput_video_masks = get_output_video_writer(\n    cap, \"/content/skateboarding_masks.mp4\")\nmask_frames = []\n\n# Loop through the frames of the video\nframe_num = 1\nwhile cap.isOpened():\n    if frame_num % 30 == 0 or frame_num == 1:\n        print(\"Processing frames\", frame_num, \"-\", frame_num + 29)\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    # Run frame through YOLOv8 to get detections\n    detections = model.predict(frame, conf=0.7)  # frame is a numpy array\n\n    # Write detections to output video\n    frame_with_detections = visualize_detections(\n        frame,\n        detections[0].boxes.cpu().xyxy,\n        detections[0].boxes.cpu().conf,\n        detections[0].boxes.cpu().cls,\n    )\n    output_video_boxes.write(frame_with_detections)\n\n    # Run frame and detections through SAM to get masks\n    transformed_boxes = mask_predictor.transform.apply_boxes_torch(\n        detections[0].boxes.xyxy, list(get_video_dimensions(cap)))\n    if len(transformed_boxes) == 0:\n        print(\"No boxes found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    mask_predictor.set_image(frame)\n    masks, scores, logits = mask_predictor.predict_torch(\n        boxes=transformed_boxes,\n        multimask_output=False,\n        point_coords=None,\n        point_labels=None,\n    )\n    masks = np.array(masks.cpu())\n    if masks is None or len(masks) == 0:\n        print(\"No masks found on frame\", frame_num)\n        output_video_masks.write(frame)\n        frame_num += 1\n        continue\n    merged_colored_mask = merge_masks_colored(masks, detections[0].boxes.cls)\n\n    # Write masks to output video\n    image_combined = cv2.addWeighted(frame, 0.7, merged_colored_mask, 0.7, 0)\n    output_video_masks.write(image_combined)\n\n    # Create video mask annotation for upload to Labelbox\n    instance_uri = get_instance_uri(client, global_key, merged_colored_mask)\n    mask_frame = create_mask_frame(frame_num, instance_uri)\n    mask_frames.append(mask_frame)\n    print(\"Boxes found on frame\", frame_num)\n    frame_num += 1\n\n    # For the purposes of this demo, only look at the first 80 frames\n    if frame_num > 80:\n        break\n\ncap.release()\noutput_video_boxes.release()\noutput_video_masks.release()\ncv2.destroyAllWindows()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Create annotations for LB upload\nmask_instances = create_mask_instances(unique_class_ids)\nannotations = []\nfor instance in mask_instances:\n    annotations.append(create_video_mask_annotation(mask_frames, instance))\n\nlabels = []\nlabels.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Upload the predictions to your specified project and data rows as pre-labels\n",
+        "\n",
+        "Note: This may take a few minutes, depending on size of video and number of masks"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job = lb.MALPredictionImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"mal_import_job\" + str(uuid.uuid4()),\n    predictions=labels,\n)\nupload_job.wait_until_done()\nprint(f\"Errors: {upload_job.errors}\",)\nprint(f\"Status of uploads: {upload_job.statuses}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/custom_metrics_basics.ipynb b/examples/model_experiments/custom_metrics_basics.ipynb
index 26a4b8d0f..0face2b24 100644
--- a/examples/model_experiments/custom_metrics_basics.ipynb
+++ b/examples/model_experiments/custom_metrics_basics.ipynb
@@ -1,257 +1,255 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "----\n",
-                "\n",
-                "# Model Diagnostics - Custom Metrics Basics\n",
-                "\n",
-                "\n",
-                "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
-                "    * Iterate faster\n",
-                "    * Measure and report on model quality\n",
-                "    * Understand marginal value of additional labels and modeling efforts\n",
-                "\n",
-                "\n",
-                "* For an end-to-end demo of diagnostics using custom metrics checkout this [notebook](custom_metrics_demo.ipynb)\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Environment Setup\n",
-                "\n",
-                "Install dependencies"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Import libraries"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox.types as lb_types\nimport labelbox as lb\nimport uuid\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Custom Metrics\n",
-                "* Users can provide metrics at the following levels of granularity:\n",
-                "    1. data rows\n",
-                "    2. features\n",
-                "    3. subclasses\n",
-                "* Additionally, metrics can be given custom names to best describe what they are measuring.\n",
-                "    \n",
-                "* Limits and Behavior:\n",
-                "    * At a data row cannot have more than 20 metrics\n",
-                "    * Metrics are upserted, so if a metric already exists, its value will be replaced\n",
-                "    * Metrics can have values in the range [0,100000]\n",
-                "* Currently `ScalarMetric`s and `ConfusionMatrixMetric`s are supported. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### ScalarMetric\n",
-                "    * A `ScalarMetric` is a metric with just a single scalar value."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "from labelbox.data.annotation_types import (\n    ScalarMetric,\n    ScalarMetricAggregation,\n    ConfusionMatrixMetric,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "data_row_metric = ScalarMetric(metric_name=\"iou_custom\", value=0.5)\n\nfeature_metric = ScalarMetric(metric_name=\"iou_custom\",\n                              feature_name=\"cat\",\n                              value=0.5)\n\nsubclass_metric = ScalarMetric(\n    metric_name=\"iou_custom\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=0.5,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### ConfusionMatrixMetric\n",
-                "- A `ConfusionMatrixMetric` contains 4 numbers [True postivie, False Postive, True Negative, False Negateive]\n",
-                "- Confidence is also supported a key value pairs, where the score is the key and the value is the metric value.\n",
-                "- In the user interface, these metrics are used to derive precision,recall, and f1 scores. The reason these are not directly uploaded is that the raw data allows us to do processing on the front end.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_row_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nfeature_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nsubclass_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Confidence\n",
-                "* Users can provide confidence scores along with metrics\n",
-                "* This enables them to explore their model performance without necessarily knowing the optimal thresholds for each class.\n",
-                "* Users can filter on confidence and value in the UI to perform powerful queries.\n",
-                "* The keys represent a confidence score (must be between 0 and 1) and the values represent either a scalar metric or for confusion matrix metrics [TP,FP,TN,FN]"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "confusion_matrix_metric_with_confidence = ConfusionMatrixMetric(\n    metric_name=\"confusion_matrix_50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value={\n        0.1: [1, 0, 1, 0],\n        0.3: [1, 0, 1, 0],\n        0.5: [1, 0, 1, 0],\n        0.7: [1, 0, 1, 0],\n        0.9: [1, 0, 1, 0],\n    },\n)\n\nscalar_metric_with_confidence = ScalarMetric(\n    metric_name=\"iou_custom\",\n    value={\n        0.1: 0.2,\n        0.3: 0.25,\n        0.5: 0.3,\n        0.7: 0.4,\n        0.9: 0.3\n    },\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Aggregations\n",
-                "* This is an optional field on the `ScalarMetric` object (by default it uses Arithmetic Mean).\n",
-                "* Aggregations occur in two cases:\n",
-                "    1. When a user provides a feature or subclass level metric, Labelbox automatically aggregates all metrics with the same parent to create a value for that parent.\n",
-                "        * E.g. A user provides cat and dog iou. The data row level metric for iou is the average of both of those.\n",
-                "        * The exception to this is when the data row level iou is explicitly set, then the aggregation will not take effect (on a per data row basis). \n",
-                "    2. When users create slices or want aggregate statistics on their models, the selected aggregation is applied."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "\"\"\"\nIf the following metrics are uploaded then\nin the web app, users will see:\ntrue positives dog = 4\ntrue positives cat = 3\ntrue positives = 7\n\"\"\"\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"cat\",\n    value=3,\n    aggregation=ScalarMetricAggregation.SUM,\n)\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"dog\",\n    value=4,\n    aggregation=ScalarMetricAggregation.SUM,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Built-in Metrics:\n",
-                "* The SDK Provides a set of default metrics that make metrics easy to use.\n",
-                "1. `confusion_matrix_metric()`\n",
-                "    * Computes a single confusion matrix metric for all the predictions and labels provided. \n",
-                "2. `miou_metric()`\n",
-                "    * Computes a single iou score for all predictions and labels provided    \n",
-                "3. `feature_confusion_matrix_metric()`\n",
-                "    * Computes the iou score for each of the classes found in the predictions and labels\n",
-                "4. `feature_miou_metric()`\n",
-                "     * Computes a confusion matrix metric for each of the classes found in the predictions and labels\n",
-                "------\n",
-                "* Note that all of these functions expect the prediction and ground truth annotations to correspond to the same data row. These functions should be called for each data row that you need metrics for."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "from labelbox.data.metrics import (\n    feature_miou_metric,\n    miou_metric,\n    confusion_matrix_metric,\n    feature_confusion_matrix_metric,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "predictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=8, y=8)),\n    )\n]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(feature_miou_metric(ground_truths, predictions))\nprint(miou_metric(ground_truths, predictions))\nprint(confusion_matrix_metric(ground_truths, predictions))\nprint(feature_confusion_matrix_metric(ground_truths, predictions))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Adjust iou for iou calcuations.\n# Set it higher than 0.64 and we get a false postive and a false negative for the other ground truth object.\nprint(feature_confusion_matrix_metric(ground_truths, predictions, iou=0.9))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# subclasses are included by default\npredictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"tall\")),\n            )\n        ],\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"short\")),\n            )\n        ],\n    )\n]\nconf_matrix_metrics = feature_confusion_matrix_metric(ground_truths,\n                                                      predictions)\niou_metrics = feature_confusion_matrix_metric(ground_truths,\n                                              predictions,\n                                              include_subclasses=False)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(\"Subclasses:\", conf_matrix_metrics[0].value)\nprint(\"Excluding Subclasses:\", iou_metrics[0].value)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Uploading Custom Metrics\n",
-                "* Custom metrics are uploaded the same way as any MEA upload. NDJson must be created. Fortunately this is made easy with converter functions.\n",
-                "* First construct a metric annotation in one of two ways:\n",
-                "    1. Manually\n",
-                "    2. Using one of the provided functions `feature_miou_metric`, `miou_metric`, `confusion_matrix_metric`, `feature_confusion_matrix_metric`.\n",
-                "* Then add the metric annotation to a label ( This step associates the metrics with a data row)\n",
-                "* Convert to ndjson and upload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Continuing with the last example:\nglobal_key = \"<global_key>\"\nmetrics = [*conf_matrix_metrics, *iou_metrics]\nlabels = [\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=metrics)\n]\n# We can upload these metric with other annotations\n# model_run.add_predictions(f'diagnostics-import-{uuid.uuid4()}', labels)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_basics.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "----\n",
+        "\n",
+        "# Model Diagnostics - Custom Metrics Basics\n",
+        "\n",
+        "\n",
+        "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
+        "    * Iterate faster\n",
+        "    * Measure and report on model quality\n",
+        "    * Understand marginal value of additional labels and modeling efforts\n",
+        "\n",
+        "\n",
+        "* For an end-to-end demo of diagnostics using custom metrics checkout this [notebook](custom_metrics_demo.ipynb)\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Environment Setup\n",
+        "\n",
+        "Install dependencies"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Import libraries"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox.types as lb_types\nimport labelbox as lb\nimport uuid\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Custom Metrics\n",
+        "* Users can provide metrics at the following levels of granularity:\n",
+        "    1. data rows\n",
+        "    2. features\n",
+        "    3. subclasses\n",
+        "* Additionally, metrics can be given custom names to best describe what they are measuring.\n",
+        "    \n",
+        "* Limits and Behavior:\n",
+        "    * At a data row cannot have more than 20 metrics\n",
+        "    * Metrics are upserted, so if a metric already exists, its value will be replaced\n",
+        "    * Metrics can have values in the range [0,100000]\n",
+        "* Currently `ScalarMetric`s and `ConfusionMatrixMetric`s are supported. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### ScalarMetric\n",
+        "    * A `ScalarMetric` is a metric with just a single scalar value."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "from labelbox.data.annotation_types import (\n    ScalarMetric,\n    ScalarMetricAggregation,\n    ConfusionMatrixMetric,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "data_row_metric = ScalarMetric(metric_name=\"iou_custom\", value=0.5)\n\nfeature_metric = ScalarMetric(metric_name=\"iou_custom\",\n                              feature_name=\"cat\",\n                              value=0.5)\n\nsubclass_metric = ScalarMetric(\n    metric_name=\"iou_custom\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=0.5,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### ConfusionMatrixMetric\n",
+        "- A `ConfusionMatrixMetric` contains 4 numbers [True postivie, False Postive, True Negative, False Negateive]\n",
+        "- Confidence is also supported a key value pairs, where the score is the key and the value is the metric value.\n",
+        "- In the user interface, these metrics are used to derive precision,recall, and f1 scores. The reason these are not directly uploaded is that the raw data allows us to do processing on the front end.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_row_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nfeature_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)\n\nsubclass_metric = ConfusionMatrixMetric(\n    metric_name=\"50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value=[1, 0, 1, 0],\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Confidence\n",
+        "* Users can provide confidence scores along with metrics\n",
+        "* This enables them to explore their model performance without necessarily knowing the optimal thresholds for each class.\n",
+        "* Users can filter on confidence and value in the UI to perform powerful queries.\n",
+        "* The keys represent a confidence score (must be between 0 and 1) and the values represent either a scalar metric or for confusion matrix metrics [TP,FP,TN,FN]"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "confusion_matrix_metric_with_confidence = ConfusionMatrixMetric(\n    metric_name=\"confusion_matrix_50pct_iou\",\n    feature_name=\"cat\",\n    subclass_name=\"organge\",\n    value={\n        0.1: [1, 0, 1, 0],\n        0.3: [1, 0, 1, 0],\n        0.5: [1, 0, 1, 0],\n        0.7: [1, 0, 1, 0],\n        0.9: [1, 0, 1, 0],\n    },\n)\n\nscalar_metric_with_confidence = ScalarMetric(\n    metric_name=\"iou_custom\",\n    value={\n        0.1: 0.2,\n        0.3: 0.25,\n        0.5: 0.3,\n        0.7: 0.4,\n        0.9: 0.3\n    },\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Aggregations\n",
+        "* This is an optional field on the `ScalarMetric` object (by default it uses Arithmetic Mean).\n",
+        "* Aggregations occur in two cases:\n",
+        "    1. When a user provides a feature or subclass level metric, Labelbox automatically aggregates all metrics with the same parent to create a value for that parent.\n",
+        "        * E.g. A user provides cat and dog iou. The data row level metric for iou is the average of both of those.\n",
+        "        * The exception to this is when the data row level iou is explicitly set, then the aggregation will not take effect (on a per data row basis). \n",
+        "    2. When users create slices or want aggregate statistics on their models, the selected aggregation is applied."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "\"\"\"\nIf the following metrics are uploaded then\nin the web app, users will see:\ntrue positives dog = 4\ntrue positives cat = 3\ntrue positives = 7\n\"\"\"\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"cat\",\n    value=3,\n    aggregation=ScalarMetricAggregation.SUM,\n)\n\nfeature_metric = ScalarMetric(\n    metric_name=\"true_positives\",\n    feature_name=\"dog\",\n    value=4,\n    aggregation=ScalarMetricAggregation.SUM,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Built-in Metrics:\n",
+        "* The SDK Provides a set of default metrics that make metrics easy to use.\n",
+        "1. `confusion_matrix_metric()`\n",
+        "    * Computes a single confusion matrix metric for all the predictions and labels provided. \n",
+        "2. `miou_metric()`\n",
+        "    * Computes a single iou score for all predictions and labels provided    \n",
+        "3. `feature_confusion_matrix_metric()`\n",
+        "    * Computes the iou score for each of the classes found in the predictions and labels\n",
+        "4. `feature_miou_metric()`\n",
+        "     * Computes a confusion matrix metric for each of the classes found in the predictions and labels\n",
+        "------\n",
+        "* Note that all of these functions expect the prediction and ground truth annotations to correspond to the same data row. These functions should be called for each data row that you need metrics for."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "from labelbox.data.metrics import (\n    feature_miou_metric,\n    miou_metric,\n    confusion_matrix_metric,\n    feature_confusion_matrix_metric,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "predictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=8, y=8)),\n    )\n]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(feature_miou_metric(ground_truths, predictions))\nprint(miou_metric(ground_truths, predictions))\nprint(confusion_matrix_metric(ground_truths, predictions))\nprint(feature_confusion_matrix_metric(ground_truths, predictions))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Adjust iou for iou calcuations.\n# Set it higher than 0.64 and we get a false postive and a false negative for the other ground truth object.\nprint(feature_confusion_matrix_metric(ground_truths, predictions, iou=0.9))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# subclasses are included by default\npredictions = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"tall\")),\n            )\n        ],\n    )\n]\n\nground_truths = [\n    lb_types.ObjectAnnotation(\n        name=\"cat\",\n        value=lb_types.Rectangle(start=lb_types.Point(x=0, y=0),\n                                 end=lb_types.Point(x=10, y=10)),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"height\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"short\")),\n            )\n        ],\n    )\n]\nconf_matrix_metrics = feature_confusion_matrix_metric(ground_truths,\n                                                      predictions)\niou_metrics = feature_confusion_matrix_metric(ground_truths,\n                                              predictions,\n                                              include_subclasses=False)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(\"Subclasses:\", conf_matrix_metrics[0].value)\nprint(\"Excluding Subclasses:\", iou_metrics[0].value)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Uploading Custom Metrics\n",
+        "* Custom metrics are uploaded the same way as any MEA upload. NDJson must be created. Fortunately this is made easy with converter functions.\n",
+        "* First construct a metric annotation in one of two ways:\n",
+        "    1. Manually\n",
+        "    2. Using one of the provided functions `feature_miou_metric`, `miou_metric`, `confusion_matrix_metric`, `feature_confusion_matrix_metric`.\n",
+        "* Then add the metric annotation to a label ( This step associates the metrics with a data row)\n",
+        "* Convert to ndjson and upload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Continuing with the last example:\nglobal_key = \"<global_key>\"\nmetrics = [*conf_matrix_metrics, *iou_metrics]\nlabels = [\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=metrics)\n]\n# We can upload these metric with other annotations\n# model_run.add_predictions(f'diagnostics-import-{uuid.uuid4()}', labels)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/custom_metrics_demo.ipynb b/examples/model_experiments/custom_metrics_demo.ipynb
index e6de146b9..8e0412f40 100644
--- a/examples/model_experiments/custom_metrics_demo.ipynb
+++ b/examples/model_experiments/custom_metrics_demo.ipynb
@@ -1,426 +1,424 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "----\n",
-                "\n",
-                "# Model Diagnostics - Custom Metrics Demo\n",
-                "\n",
-                "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
-                "    * Iterate faster\n",
-                "    * Measure and report on model quality\n",
-                "    * Understand marginal value of additional labels and modeling efforts\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Environment setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key\n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    )),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n        lb_types.ClassificationAnswer(\n            name=\"second_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n    ]),\n)\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\":\n                \"first_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n        {\n            \"name\":\n                \"second_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=0.5,\n                    custom_metrics=[\n                        {\n                            \"name\": \"iou\",\n                            \"value\": 0.5\n                        },\n                        {\n                            \"name\": \"f1\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"precision\",\n                            \"value\": 0.55\n                        },\n                        {\n                            \"name\": \"recall\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"tagsCount\",\n                            \"value\": 43\n                        },\n                        {\n                            \"name\": \"metric_with_a_very_long_name\",\n                            \"value\": 0.334332,\n                        },\n                    ],\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\":\n        \"nested_radio_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                            custom_metrics=[\n                                {\n                                    \"name\": \"iou\",\n                                    \"value\": 0.5\n                                },\n                                {\n                                    \"name\": \"f1\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"precision\",\n                                    \"value\": 0.55\n                                },\n                                {\n                                    \"name\": \"recall\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"tagsCount\",\n                                    \"value\": 43\n                                },\n                                {\n                                    \"name\": \"metric_with_a_very_long_name\",\n                                    \"value\": 0.334332,\n                                },\n                            ],\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\":\n                    \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,\n                \"customMetrics\": [\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.55\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 43\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding Box"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "bbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with nested classification "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                custom_metrics=[\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.2\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.1\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.3\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 23\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            )),\n        )\n    ],\n)\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.2\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.1\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.3\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 23\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polygon"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",\n    value=lb_types.Text(\n        answer=\"sample text\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    ),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "polyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\ntest_img_urls = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"Custom metrics demo\",\n                                iam_integration=None)\ntask = dataset.create_data_rows([test_img_urls])\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(\n    name=\"model_with_aggregated_custom_metrics\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the prediction payload using the snippets of code in ***Supported Predictions*** section.\n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to a model run\n",
-                "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations.\n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"image_prediction_many_kinds\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/custom_metrics_demo.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "----\n",
+        "\n",
+        "# Model Diagnostics - Custom Metrics Demo\n",
+        "\n",
+        "* Measuring model quality is critical to efficiently building models. It is important that the metrics used to measure model quality closely align with the business objectives for the model. Otherwise, slight changes in model quality, as they related to these core objectives, are lost to noise. Custom metrics enables users to measure model quality in terms of their exact business goals. By incorporating custom metrics into workflows, users can:\n",
+        "    * Iterate faster\n",
+        "    * Measure and report on model quality\n",
+        "    * Understand marginal value of additional labels and modeling efforts\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Environment setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    )),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.1\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n        lb_types.ClassificationAnswer(\n            name=\"second_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        ),\n    ]),\n)\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\":\n                \"first_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n        {\n            \"name\":\n                \"second_checklist_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=0.5,\n                    custom_metrics=[\n                        {\n                            \"name\": \"iou\",\n                            \"value\": 0.5\n                        },\n                        {\n                            \"name\": \"f1\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"precision\",\n                            \"value\": 0.55\n                        },\n                        {\n                            \"name\": \"recall\",\n                            \"value\": 0.33\n                        },\n                        {\n                            \"name\": \"tagsCount\",\n                            \"value\": 43\n                        },\n                        {\n                            \"name\": \"metric_with_a_very_long_name\",\n                            \"value\": 0.334332,\n                        },\n                    ],\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\":\n        \"nested_radio_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    },\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            custom_metrics=[\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.55\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.33\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 43\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                            custom_metrics=[\n                                {\n                                    \"name\": \"iou\",\n                                    \"value\": 0.5\n                                },\n                                {\n                                    \"name\": \"f1\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"precision\",\n                                    \"value\": 0.55\n                                },\n                                {\n                                    \"name\": \"recall\",\n                                    \"value\": 0.33\n                                },\n                                {\n                                    \"name\": \"tagsCount\",\n                                    \"value\": 43\n                                },\n                                {\n                                    \"name\": \"metric_with_a_very_long_name\",\n                                    \"value\": 0.334332,\n                                },\n                            ],\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"confidence\":\n        0.5,\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"customMetrics\": [\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\":\n                    \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,\n                \"customMetrics\": [\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.55\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.33\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 43\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding Box"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "bbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with nested classification "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "bbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                custom_metrics=[\n                    {\n                        \"name\": \"iou\",\n                        \"value\": 0.5\n                    },\n                    {\n                        \"name\": \"f1\",\n                        \"value\": 0.2\n                    },\n                    {\n                        \"name\": \"precision\",\n                        \"value\": 0.1\n                    },\n                    {\n                        \"name\": \"recall\",\n                        \"value\": 0.3\n                    },\n                    {\n                        \"name\": \"tagsCount\",\n                        \"value\": 23\n                    },\n                    {\n                        \"name\": \"metric_with_a_very_long_name\",\n                        \"value\": 0.334332,\n                    },\n                ],\n            )),\n        )\n    ],\n)\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.2\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.1\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.3\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 23\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"customMetrics\": [\n                {\n                    \"name\": \"iou\",\n                    \"value\": 0.5\n                },\n                {\n                    \"name\": \"f1\",\n                    \"value\": 0.2\n                },\n                {\n                    \"name\": \"precision\",\n                    \"value\": 0.1\n                },\n                {\n                    \"name\": \"recall\",\n                    \"value\": 0.3\n                },\n                {\n                    \"name\": \"tagsCount\",\n                    \"value\": 23\n                },\n                {\n                    \"name\": \"metric_with_a_very_long_name\",\n                    \"value\": 0.334332\n                },\n            ],\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polygon"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\",\n    value=lb_types.Text(\n        answer=\"sample text\",\n        confidence=0.5,\n        custom_metrics=[\n            {\n                \"name\": \"iou\",\n                \"value\": 0.5\n            },\n            {\n                \"name\": \"f1\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"precision\",\n                \"value\": 0.55\n            },\n            {\n                \"name\": \"recall\",\n                \"value\": 0.33\n            },\n            {\n                \"name\": \"tagsCount\",\n                \"value\": 43\n            },\n            {\n                \"name\": \"metric_with_a_very_long_name\",\n                \"value\": 0.334332\n            },\n        ],\n    ),\n)\n\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "polyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    custom_metrics=[\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"customMetrics\": [\n        {\n            \"name\": \"iou\",\n            \"value\": 0.5\n        },\n        {\n            \"name\": \"f1\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"precision\",\n            \"value\": 0.55\n        },\n        {\n            \"name\": \"recall\",\n            \"value\": 0.33\n        },\n        {\n            \"name\": \"tagsCount\",\n            \"value\": 43\n        },\n        {\n            \"name\": \"metric_with_a_very_long_name\",\n            \"value\": 0.334332\n        },\n    ],\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110.jpeg\"\ntest_img_urls = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"Custom metrics demo\",\n                                iam_integration=None)\ntask = dataset.create_data_rows([test_img_urls])\n\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(\n    name=\"model_with_aggregated_custom_metrics\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the prediction payload using the snippets of code in ***Supported Predictions*** section.\n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to a model run\n",
+        "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations.\n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"image_prediction_many_kinds\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n                   annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/model_predictions_to_project.ipynb b/examples/model_experiments/model_predictions_to_project.ipynb
index 9cb07e3b4..a1560feec 100644
--- a/examples/model_experiments/model_predictions_to_project.ipynb
+++ b/examples/model_experiments/model_predictions_to_project.ipynb
@@ -1,272 +1,270 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Import Model Run Predictions to a Project\n",
-                "Throughout the process of training your machine learning (ML) model, you may want to export your model-run predictions and import them to your new project. In this notebook, we will demonstrate the process on how to get those predictions moved over."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating Model Experiment\n",
-                "\n",
-                "In order to interact with Model Run predictions, you must create a Model Experiment with a Model Run and then add predictions. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information.\n",
-                "\n",
-                "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "In this example we are making a simple ontology with a classification feature. The classification feature has two options: option 1 and option 2."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Demo Feature\",\n        options=[lb.Option(value=\"option 1\"),\n                 lb.Option(value=\"option 2\")],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\"Demo Ontology\",\n                                  ontology_builder.asdict(),\n                                  media_type=lb.MediaType.Image)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Experiment"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model = client.create_model(name=f\"Model Experiment Demo {str(uuid.uuid4())}\",\n                            ontology_id=ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating a Model Run from Model Experiment\n",
-                "\n",
-                "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Dataset and Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as data row for a dataset\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create Model Run and Attach Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run_name = f\"Model Run Demo {str(uuid.uuid4())}\"\n\nmodel_run = model.create_model_run(name=model_run_name)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Add Predictions\n",
-                "In the below code snippet we are adding a sample predictions and attaching them to our data row inside our model run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"Demo Feature\",\n    value=lb_types.Checklist(\n        answer=[lb_types.ClassificationAnswer(name=\"option 1\", confidence=0.5)\n               ]),\n)\n\n# Create prediction label\nlabel_prediction = [\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[checklist_prediction],\n    )\n]\n\n# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Setup Project and Add Predictions\n",
-                "In the steps below we will be creating our target project and setting up the project with the ontology we used with our model run. See [Project](https://docs.labelbox.com/reference/dataset) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a new project\nproject = client.create_project(name=\"Model Run Import Demo Project\",\n                                media_type=lb.MediaType.Image)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Setup Ontology\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Ontology Mapping\n",
-                "To send prediction to your annotate project you will need to provide a ontology mapping python dictionary item. This matches ontology feature id to another. You would use this if your ontology was different from your model run to your project. In our case, since we are using the same ontology, you would just need to map the same feature id to each other."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Get ontology dictionary to obtain featureSchemaIds\nontology_normalized = ontology.normalized\n\nPREDICTIONS_ONTOLOGY_MAPPING = {\n    ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Send Model Predictions from Model Run to Annotate\n",
-                "To send our predictions to our project we will be using the `send_to_annotate_from_model` method from our project. See [Foundry apps](https://docs.labelbox.com/reference/foundry-2#send-foundry-generated-annotations-from-catalog-to-annotate) for more information.\n",
-                "##### Parameters\n",
-                "\n",
-                "When you send predicted data rows to annotate from a model run, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
-                "\n",
-                "* `predictions_ontology_mapping`\n",
-                "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
-                "* `exclude_data_rows_in_project`\n",
-                "    - Excludes data rows that are already in the project. \n",
-                "* `override_existing_annotations_rule` \n",
-                "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
-                "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
-                "        * Options include:\n",
-                "            * ConflictResolutionStrategy.KeepExisting\n",
-                "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
-                "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
-                "* `param batch_priority`\n",
-                "    - The priority of the batch.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Import Predictions as pre-labels"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "send_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Send the predictions as pre-labels\nqueue_id = [\n    queue.uid\n    for queue in project.task_queues()\n    if queue.queue_type == \"INITIAL_LABELING_QUEUE\"\n][0]\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths .\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_predictions_to_project.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Import Model Run Predictions to a Project\n",
+        "Throughout the process of training your machine learning (ML) model, you may want to export your model-run predictions and import them to your new project. In this notebook, we will demonstrate the process on how to get those predictions moved over."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nfrom labelbox.schema.conflict_resolution_strategy import (\n    ConflictResolutionStrategy,)\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating Model Experiment\n",
+        "\n",
+        "In order to interact with Model Run predictions, you must create a Model Experiment with a Model Run and then add predictions. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information.\n",
+        "\n",
+        "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "In this example we are making a simple ontology with a classification feature. The classification feature has two options: option 1 and option 2."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Demo Feature\",\n        options=[lb.Option(value=\"option 1\"),\n                 lb.Option(value=\"option 2\")],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\"Demo Ontology\",\n                                  ontology_builder.asdict(),\n                                  media_type=lb.MediaType.Image)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Experiment"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model = client.create_model(name=f\"Model Experiment Demo {str(uuid.uuid4())}\",\n                            ontology_id=ontology.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating a Model Run from Model Experiment\n",
+        "\n",
+        "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Dataset and Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as data row for a dataset\nglobal_key = \"2560px-Kitano_Street_Kobe01s5s4110\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create Model Run and Attach Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run_name = f\"Model Run Demo {str(uuid.uuid4())}\"\n\nmodel_run = model.create_model_run(name=model_run_name)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Add Predictions\n",
+        "In the below code snippet we are adding a sample predictions and attaching them to our data row inside our model run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"Demo Feature\",\n    value=lb_types.Checklist(\n        answer=[lb_types.ClassificationAnswer(name=\"option 1\", confidence=0.5)\n               ]),\n)\n\n# Create prediction label\nlabel_prediction = [\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[checklist_prediction],\n    )\n]\n\n# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Setup Project and Add Predictions\n",
+        "In the steps below we will be creating our target project and setting up the project with the ontology we used with our model run. See [Project](https://docs.labelbox.com/reference/dataset) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a new project\nproject = client.create_project(name=\"Model Run Import Demo Project\",\n                                media_type=lb.MediaType.Image)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Setup Ontology\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Ontology Mapping\n",
+        "To send prediction to your annotate project you will need to provide a ontology mapping python dictionary item. This matches ontology feature id to another. You would use this if your ontology was different from your model run to your project. In our case, since we are using the same ontology, you would just need to map the same feature id to each other."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Get ontology dictionary to obtain featureSchemaIds\nontology_normalized = ontology.normalized\n\nPREDICTIONS_ONTOLOGY_MAPPING = {\n    ontology_normalized[\"classifications\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0]\n        [\"featureSchemaId\"],  # Classification featureSchemaID\n    ontology_normalized[\"classifications\"][0][\"options\"][0][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][0]\n        [\"featureSchemaId\"],  # Different Classification Answer featureSchemaIDs\n    ontology_normalized[\"classifications\"][0][\"options\"][1][\"featureSchemaId\"]:\n        ontology_normalized[\"classifications\"][0][\"options\"][1]\n        [\"featureSchemaId\"],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Send Model Predictions from Model Run to Annotate\n",
+        "To send our predictions to our project we will be using the `send_to_annotate_from_model` method from our project. See [Foundry apps](https://docs.labelbox.com/reference/foundry-2#send-foundry-generated-annotations-from-catalog-to-annotate) for more information.\n",
+        "##### Parameters\n",
+        "\n",
+        "When you send predicted data rows to annotate from a model run, you may choose to include or exclude certain parameters, at a minimum a predictions_ontology_mapping will need to be provided:\n",
+        "\n",
+        "* `predictions_ontology_mapping`\n",
+        "    - A dictionary containing the mapping of the model's ontology feature schema ids to the project's ontology feature schema ids\n",
+        "* `exclude_data_rows_in_project`\n",
+        "    - Excludes data rows that are already in the project. \n",
+        "* `override_existing_annotations_rule` \n",
+        "    - The strategy defining how to handle conflicts in classifications between the data rows that already exist in the project and incoming predictions from the source model run or annotations from the source project. \n",
+        "        * Defaults to ConflictResolutionStrategy.KeepExisting\n",
+        "        * Options include:\n",
+        "            * ConflictResolutionStrategy.KeepExisting\n",
+        "            * ConflictResolutionStrategy.OverrideWithPredictions\n",
+        "            * ConflictResolutionStrategy.OverrideWithAnnotations\n",
+        "* `param batch_priority`\n",
+        "    - The priority of the batch.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Import Predictions as pre-labels"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "send_to_annotations_params = {\n    \"predictions_ontology_mapping\":\n        PREDICTIONS_ONTOLOGY_MAPPING,\n    \"exclude_data_rows_in_project\":\n        False,\n    \"override_existing_annotations_rule\":\n        ConflictResolutionStrategy.OverrideWithPredictions,\n    \"batch_priority\":\n        5,\n}\n\n# Send the predictions as pre-labels\nqueue_id = [\n    queue.uid\n    for queue in project.task_queues()\n    if queue.queue_type == \"INITIAL_LABELING_QUEUE\"\n][0]\n\ntask = model_run.send_to_annotate_from_model(\n    destination_project_id=project.uid,\n    task_queue_id=\n    queue_id,  # ID of workflow task, set ID to None if you want to convert pre-labels to ground truths .\n    batch_name=\"Prediction Import Demo Batch\",\n    data_rows=lb.GlobalKeys(\n        [global_key]  # Provide a list of global keys from foundry app task\n    ),\n    params=send_to_annotations_params,\n)\n\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()\n# model_run.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/model_experiments/model_slices.ipynb b/examples/model_experiments/model_slices.ipynb
index 87c760328..a91709fa0 100644
--- a/examples/model_experiments/model_slices.ipynb
+++ b/examples/model_experiments/model_slices.ipynb
@@ -1,247 +1,245 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Model Slices\n",
-                "\n",
-                "Model Slice functions similarly to Catalog Slice, with both essentially being saved searches. However, there are key differences in their functionalities. While Catalog Slice searches within a specific data catalog, Model Slice extends its data row search across a model run in a model. You can construct a Model Slice by using one or more filters to curate a collection of data rows. Often users will combine filters to surface high-impact data and then save the results as a Model Slice.\n",
-                "\n",
-                "This notebook is used to go over some common Labelbox SDK methods to interact with Model Slices created through the Labelbox platform.\n",
-                "\n",
-                "See [Slices](https://docs.labelbox.com/docs/slices-1) for more information on modifying Model Slices."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install labelbox",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Create Model Slice\n",
-                "\n",
-                "In order to interact with model slices, you must create a Model Experiment with a Model Run and then create a Model Slice through the platform. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating Model Experiment\n",
-                "\n",
-                "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Ontology"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Experiment"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model = client.create_model(name=\"Model Slice Demo\", ontology_id=ontology.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Creating a Model Run from Model Experiment\n",
-                "\n",
-                "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Dataset and Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Model Run and Attach Data Rows"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run_name = \"Model Slice Demo\"\nexample_config = {\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n}\nmodel_run = model.create_model_run(name=model_run_name, config=example_config)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Make Model Slice Inside UI\n",
-                "\n",
-                "Creating a Model Slice is currently not supported through the SDK, however, to showcase how to interact with Model Slice, we are going to generate a Model Slice through the UI.\n",
-                "\n",
-                "#### Workflow\n",
-                "\n",
-                "1. Navigate to ***Model*** section of the Labelbox Platform, select the ***Experiment*** type, and select the Model Experiment that was created.\n",
-                "2. You must have a filter created in order to save a slice. For this example, click ***Search your data*** dropdown and then ***Data row***.\n",
-                "3. Change ***is one of*** dropdown to ***is not one of*** then type \"test\" into the ***Search for an id*** search box.\n",
-                "4. Hit ***Enter*** and select ***Save slice***.\n",
-                "5. Give the slice a name and select ***Save***.\n",
-                "6. Above the ***Search your data*** dropdown you will see your slice's name. Select that dropdown and click ***Copy slice ID***.\n",
-                "7. Paste the ***Slice ID*** below."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "SLICE_ID = \"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Get Model Slice"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_slice = client.get_model_slice(SLICE_ID)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Obtain Data Row IDs from Model Slice"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_row_ids = model_slice.get_data_row_ids(model_run.uid)\n\nfor data_row_id in data_row_ids:\n    print(data_row_id)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Obtain Data Row Identifiers Objects"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "data_rows = model_slice.get_data_row_identifiers(model_run.uid)\n\nfor data_row in data_rows:\n    print(data_row)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Model Slice Attributes"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# name (str)\nmodel_slice.name\n\n# description (str)\nmodel_slice.description\n\n# updated at (datetime)\nmodel_slice.updated_at\n\n# created at (datetime)\nmodel_slice.created_at\n\n# filter (list[dict])\nmodel_slice.filter",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_experiments/model_slices.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Model Slices\n",
+        "\n",
+        "Model Slice functions similarly to Catalog Slice, with both essentially being saved searches. However, there are key differences in their functionalities. While Catalog Slice searches within a specific data catalog, Model Slice extends its data row search across a model run in a model. You can construct a Model Slice by using one or more filters to curate a collection of data rows. Often users will combine filters to surface high-impact data and then save the results as a Model Slice.\n",
+        "\n",
+        "This notebook is used to go over some common Labelbox SDK methods to interact with Model Slices created through the Labelbox platform.\n",
+        "\n",
+        "See [Slices](https://docs.labelbox.com/docs/slices-1) for more information on modifying Model Slices."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install labelbox",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Create Model Slice\n",
+        "\n",
+        "In order to interact with model slices, you must create a Model Experiment with a Model Run and then create a Model Slice through the platform. The steps below go over this process. See [Model](https://docs.labelbox.com/reference/model) from our developer guides for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating Model Experiment\n",
+        "\n",
+        "To create a Model Experiment you will need to create an ontology. See [Ontology](https://docs.labelbox.com/reference/ontology) for more information"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Ontology"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Experiment"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model = client.create_model(name=\"Model Slice Demo\", ontology_id=ontology.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Creating a Model Run from Model Experiment\n",
+        "\n",
+        "On this step we will need to create a dataset to attach data rows to our model run. See [Dataset](https://docs.labelbox.com/reference/dataset) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Dataset and Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as data row for a dataset\nglobal_key = str(uuid.uuid4())\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(name=\"foundry-demo-dataset\")\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\n\nprint(f\"Errors: {task.errors}\")\nprint(f\"Failed data rows: {task.failed_data_rows}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Model Run and Attach Data Rows"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run_name = \"Model Slice Demo\"\nexample_config = {\n    \"learning_rate\": 0.001,\n    \"batch_size\": 32,\n}\nmodel_run = model.create_model_run(name=model_run_name, config=example_config)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Make Model Slice Inside UI\n",
+        "\n",
+        "Creating a Model Slice is currently not supported through the SDK, however, to showcase how to interact with Model Slice, we are going to generate a Model Slice through the UI.\n",
+        "\n",
+        "#### Workflow\n",
+        "\n",
+        "1. Navigate to ***Model*** section of the Labelbox Platform, select the ***Experiment*** type, and select the Model Experiment that was created.\n",
+        "2. You must have a filter created in order to save a slice. For this example, click ***Search your data*** dropdown and then ***Data row***.\n",
+        "3. Change ***is one of*** dropdown to ***is not one of*** then type \"test\" into the ***Search for an id*** search box.\n",
+        "4. Hit ***Enter*** and select ***Save slice***.\n",
+        "5. Give the slice a name and select ***Save***.\n",
+        "6. Above the ***Search your data*** dropdown you will see your slice's name. Select that dropdown and click ***Copy slice ID***.\n",
+        "7. Paste the ***Slice ID*** below."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "SLICE_ID = \"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Get Model Slice"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_slice = client.get_model_slice(SLICE_ID)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Obtain Data Row IDs from Model Slice"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_row_ids = model_slice.get_data_row_ids(model_run.uid)\n\nfor data_row_id in data_row_ids:\n    print(data_row_id)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Obtain Data Row Identifiers Objects"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "data_rows = model_slice.get_data_row_identifiers(model_run.uid)\n\nfor data_row in data_rows:\n    print(data_row)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Model Slice Attributes"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# name (str)\nmodel_slice.name\n\n# description (str)\nmodel_slice.description\n\n# updated at (datetime)\nmodel_slice.updated_at\n\n# created at (datetime)\nmodel_slice.created_at\n\n# filter (list[dict])\nmodel_slice.filter",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_LLM_predictions.ipynb b/examples/prediction_upload/conversational_LLM_predictions.ipynb
index b2de76f56..be9878401 100644
--- a/examples/prediction_upload/conversational_LLM_predictions.ipynb
+++ b/examples/prediction_upload/conversational_LLM_predictions.ipynb
@@ -1,388 +1,386 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# LLM pairwise comparison with Conversational text using Model\n",
-                "\n",
-                "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Set up"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Replace with your API Key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Supported annotations for conversational text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Entity"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
-                "\n",
-                "```\n",
-                "\"modelOutputs\" : [\n",
-                "  {\n",
-                "      \"title\": \"Name of the response option\",\n",
-                "      \"content\": \"Content of the response\",\n",
-                "      \"modelConfigName\": \"Name of model configuration\"\n",
-                "  }\n",
-                "]\n",
-                "```\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Example of row_data with model outputs"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Step 5: Create the predictions payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Setup the payload with the annotations that were created in Step 1."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.1 Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.2 Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.4 Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.5 Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_LLM_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# LLM pairwise comparison with Conversational text using Model\n",
+        "\n",
+        "This demo is meant to showcase how to upload conversational row data that contains model outputs for pairwise comparisons analysis in the model product.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Set up"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Replace with your API Key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Supported annotations for conversational text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Entity"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name=\"Response B\",\n                                                              confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"Choose the best response\",\n    \"answer\": {\n        \"name\": \"Response B\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "text_prediction = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\",\n                        confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"Provide a reason for your choice\",\n    \"answer\": \"This is the more concise answer\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"message-1\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"message-1\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows with \"modelOutputs\" into Catalog\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "In addition to your message based data, you will need to add a list of model outputs to your JSON file:\n",
+        "\n",
+        "```\n",
+        "\"modelOutputs\" : [\n",
+        "  {\n",
+        "      \"title\": \"Name of the response option\",\n",
+        "      \"content\": \"Content of the response\",\n",
+        "      \"modelConfigName\": \"Name of model configuration\"\n",
+        "  }\n",
+        "]\n",
+        "```\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Example of row_data with model outputs"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "pairwise_shopping_2 = \"\"\"\n {\n  \"type\": \"application/vnd.labelbox.conversational\",\n  \"version\": 1,\n  \"messages\": [\n    {\n      \"messageId\": \"message-0\",\n      \"timestampUsec\": 1530718491,\n      \"content\": \"Hi! How can I help?\",\n      \"user\": {\n        \"userId\": \"Bot 002\",\n        \"name\": \"Bot\"\n      },\n      \"align\": \"left\",\n      \"canLabel\": false\n    },\n    {\n      \"messageId\": \"message-1\",\n      \"timestampUsec\": 1530718503,\n      \"content\": \"I just bought a vacuum cleaner from you guys like a week ago and it's already broken!!\",\n      \"user\": {\n        \"userId\": \"User 00686\",\n        \"name\": \"User\"\n      },\n      \"align\": \"right\",\n      \"canLabel\": true\n    }\n\n  ],\n  \"modelOutputs\": [\n    {\n      \"title\": \"Response A\",\n      \"content\": \"I'm really sorry to hear that your vacuum cleaner is not working as expected. We certainly understand how frustrating this can be, especially with a recent purchase. I assure you, we're here to help!\\n\\n To proceed with resolving this issue, could you please provide some details about the problem you're experiencing with the vacuum cleaner? Additionally, if you could share your order number or receipt details, that would be very helpful. We will definitely look into the available options for you, which may include a replacement or a refund, depending on the specific circumstances and our return policy.\\n\\n Your satisfaction is our priority, and we'll do our best to resolve this matter promptly and to your satisfaction.\",\n      \"modelConfigName\": \"GPT-3.5 with temperature 0\"\n    },\n    {\n      \"title\": \"Response B\",\n      \"content\": \"I'm sorry about the issue with your vacuum cleaner. Please send us the order number or receipt details so we can quickly assist you with a replacement. Your satisfaction is important to us!\",\n      \"modelConfigName\": \"Fine Tuned GPT-3.5 with demo data\"\n    }\n  ]\n}\n\"\"\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"pairwise_shooping_asset\"\nconvo_data = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/conversational-sample-data/pairwise_shopping_2.json\",\n    \"global_key\":\n        global_key,\n}\n# Create a dataset\ndataset = client.create_dataset(name=\"pairwise_prediction_demo\")\n# Create a datarows\ntask = dataset.create_data_rows([convo_data])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create an ontology with relevant classifications\n\nontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\"),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            scope=lb.Classification.Scope.GLOBAL,\n            name=\"Choose the best response\",\n            options=[\n                lb.Option(value=\"Response A\"),\n                lb.Option(value=\"Response B\"),\n                lb.Option(value=\"Tie\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"Provide a reason for your choice\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Pairwise comparison ontology\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Conversational,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create model\nmodel = client.create_model(name=\"Comparison_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create model run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Step 5: Create the predictions payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            text_prediction,\n            checklist_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            nested_checklist_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Setup the payload with the annotations that were created in Step 1."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.1 Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(\n    name=\"Conversational Human Evaluation Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.2 Create a batch to send to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"message-1\"),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"Choose the best response\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"Response B\")),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"Provide a reason for your choice\",\n    value=lb_types.Text(answer=\"the answer to the text questions right here\"),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"message-1\",  # Message specific annotation\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"message-1\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.4 Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_annotation = []\nlabel_annotation.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.5 Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"label_import_job\" + str(uuid.uuid4()),\n    labels=label_annotation,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Option deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/conversational_predictions.ipynb b/examples/prediction_upload/conversational_predictions.ipynb
index c2063cc8d..b6a649d56 100644
--- a/examples/prediction_upload/conversational_predictions.ipynb
+++ b/examples/prediction_upload/conversational_predictions.ipynb
@@ -1,359 +1,357 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Conversational Text Prediction Import\n",
-                "* This notebook will provide examples of each supported prediction type for conversational text assets, and also cover MAL and Label Import methods:\n",
-                "\n",
-                "Suported annotations that can be uploaded through the SDK\n",
-                "\n",
-                "* Classification Radio \n",
-                "* Classification Checklist \n",
-                "* Classification Free Text \n",
-                "* NER\n",
-                "\n",
-                "**Not** supported annotations\n",
-                "\n",
-                "* Bouding box \n",
-                "* Polygon \n",
-                "* Point\n",
-                "* Polyline \n",
-                "* Segmentation Mask \n",
-                "* Relationships\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification ###########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# message based classifications\nner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Classification free text #####\n# Confidence scores are not supported for text predictions\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions are right here\",\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "##### Checklist Classification #######\n\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"2\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Radio Classification ######\n\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n    message_id=\"0\",\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n    \"messageId\": \"0\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n\n# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\")],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Mode and Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(\n    name=\"Conversational_model_run_\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the predictions payload\n",
-                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting payload should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Python annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "if using NDJSON : "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotations)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7 : Send annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.1 Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(\n    name=\"Conversational Text Prediction Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.2 Create a batch to send to the project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.4 Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.5 Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.6 Send the annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/conversational_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Conversational Text Prediction Import\n",
+        "* This notebook will provide examples of each supported prediction type for conversational text assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Suported annotations that can be uploaded through the SDK\n",
+        "\n",
+        "* Classification Radio \n",
+        "* Classification Checklist \n",
+        "* Classification Free Text \n",
+        "* NER\n",
+        "\n",
+        "**Not** supported annotations\n",
+        "\n",
+        "* Bouding box \n",
+        "* Polygon \n",
+        "* Point\n",
+        "* Polyline \n",
+        "* Segmentation Mask \n",
+        "* Relationships\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport uuid\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification ###########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# message based classifications\nner_prediction = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    confidence=0.5,\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\nner_prediction_ndjson = {\n    \"name\": \"ner\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 0,\n        \"end\": 8\n    },\n    \"messageId\": \"4\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Classification free text #####\n# Confidence scores are not supported for text predictions\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"text_convo\",\n    \"answer\": \"the answer to the text questions are right here\",\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "##### Checklist Classification #######\n\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n    message_id=\"2\",\n)\n\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_convo\",\n    \"answers\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n    \"messageId\": \"2\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Radio Classification ######\n\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n    message_id=\"0\",\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_convo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n    \"messageId\": \"0\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n\n# Message based\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n# Message based\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"messageId\":\n        \"10\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n# Global\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n# Global\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create one Labelbox dataset\n\nglobal_key = \"conversation-1.json\"\n\nasset = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/1000-conversations/conversation-1.json\",\n    \"global_key\":\n        global_key,\n}\n\ndataset = client.create_dataset(\n    name=\"conversational_annotation_import_demo_dataset\")\ntask = dataset.create_data_rows([asset])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(tool=lb.Tool.Type.NER, name=\"ner\")],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"text_convo\",\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            scope=lb.Classification.Scope.INDEX,\n            name=\"checklist_convo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_convo\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\"Ontology Conversation Annotations\",\n                                  ontology_builder.asdict())",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Mode and Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(\n    name=\"Conversational_model_run_\" + str(uuid.uuid4()),\n    ontology_id=ontology.uid,\n)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the predictions payload\n",
+        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting payload should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Python annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            ner_prediction,\n            checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "if using NDJSON : "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\nfor annotations in [\n        ner_prediction_ndjson,\n        text_prediction_ndjson,\n        checklist_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    annotations.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotations)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7 : Send annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.1 Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(\n    name=\"Conversational Text Prediction Import Demo\",\n    media_type=lb.MediaType.Conversational,\n)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.2 Create a batch to send to the project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_convo_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ner_annotation = lb_types.ObjectAnnotation(\n    name=\"ner\",\n    value=lb_types.ConversationEntity(start=0, end=8, message_id=\"4\"),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"text_convo\",\n    value=lb_types.Text(\n        answer=\"the answer to the text questions are right here\"),\n    message_id=\"0\",\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_convo\",  # must match your ontology feature\"s name\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n    message_id=\"2\",\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_convo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n    message_id=\"0\",\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    message_id=\"10\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.4 Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.ConversationData(global_key=global_key),\n        annotations=[\n            ner_annotation,\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            nested_checklist_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.5 Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.6 Send the annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Option deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/geospatial_predictions.ipynb b/examples/prediction_upload/geospatial_predictions.ipynb
index f0125193f..e3c1212f3 100644
--- a/examples/prediction_upload/geospatial_predictions.ipynb
+++ b/examples/prediction_upload/geospatial_predictions.ipynb
@@ -1,381 +1,379 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Geospatial Prediction Import \n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for tiled imagery assets.\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n",
-                "**Supported annotations that can be uploaded through the SDK**\n",
-                "- Bounding box\n",
-                "- Point\n",
-                "- Polygons \n",
-                "- Polyline\n",
-                "- Free form text classifications\n",
-                "- Classification - radio\n",
-                "- Classification - checklist\n",
-                "\n",
-                "**NOT** supported:\n",
-                "- Segmentation masks\n",
-                "\n",
-                "\n",
-                "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import os\n\nimport uuid\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions\n",
-                "- Each cell shows the python annotation and the NDJson annotation for each annotation type."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Point #######\n\n# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    confidence=0.4,\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point_geo\",\n    \"confidence\": 0.4,\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    confidence=0.5,\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"confidence\": 0.5,\n    \"line\": line_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_prediction_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"confidence\": 0.5,\n    \"polygon\": polygon_points_ndjson,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"confidence\": 0.5,\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"third_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.2)),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.2,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.3\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"geospatial_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=23,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_prediction_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\",\n                                                   confidence=0.5)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            bbox_prediction,\n            radio_prediction,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_prediction,\n            polygon_prediction_two,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# If using NDJSON"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson = []\nfor prediction in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        polygon_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    prediction.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(prediction)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(name=\"prediction_upload_job\" +\n                                                  str(uuid.uuid4()),\n                                                  predictions=labels)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"geospatial_prediction_demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_geospatial_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key],  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n####### Polyline #######\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data=lb_types.TiledImageData(\n            global_key=global_key,\n            tile_layer=tile_layer,\n            tile_bounds=bounds,\n            zoom_levels=[12, 20],\n        ),\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"geospatial_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6. Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# upload_job\n# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/geospatial_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Geospatial Prediction Import \n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for tiled imagery assets.\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n",
+        "**Supported annotations that can be uploaded through the SDK**\n",
+        "- Bounding box\n",
+        "- Point\n",
+        "- Polygons \n",
+        "- Polyline\n",
+        "- Free form text classifications\n",
+        "- Classification - radio\n",
+        "- Classification - checklist\n",
+        "\n",
+        "**NOT** supported:\n",
+        "- Segmentation masks\n",
+        "\n",
+        "\n",
+        "Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import os\n\nimport uuid\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions\n",
+        "- Each cell shows the python annotation and the NDJson annotation for each annotation type."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Point #######\n\n# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    confidence=0.4,\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point_geo\",\n    \"confidence\": 0.4,\n    \"point\": {\n        \"x\": -99.20647859573366,\n        \"y\": 19.40018029091072\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polyline #######\n# Coordinates\ncoords = [\n    [-99.20842051506044, 19.40032196622975],\n    [-99.20809864997865, 19.39758963475322],\n    [-99.20758366584778, 19.39776167179227],\n    [-99.20728325843811, 19.3973265189299],\n]\n\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    confidence=0.5,\n    value=lb_types.Line(points=line_points),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\": \"polyline_geo\",\n    \"confidence\": 0.5,\n    \"line\": line_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Polygon #######\n# Coordinates in the desired EPSG coordinate system\ncoords_polygon = [\n    [-99.21042680740356, 19.40036244486966],\n    [-99.2104160785675, 19.40017017124035],\n    [-99.2103409767151, 19.400008256428897],\n    [-99.21014785766603, 19.400008256428897],\n    [-99.21019077301027, 19.39983622176518],\n    [-99.21022295951845, 19.399674306621385],\n    [-99.21029806137086, 19.39951239131646],\n    [-99.2102873325348, 19.399340356128437],\n    [-99.21025514602663, 19.399117722085677],\n    [-99.21024441719057, 19.39892544698541],\n    [-99.2102336883545, 19.39874329141769],\n    [-99.21021223068239, 19.398561135646027],\n    [-99.21018004417421, 19.398399219233365],\n    [-99.21011567115785, 19.39822718286836],\n    [-99.20992255210878, 19.398136104719125],\n    [-99.20974016189577, 19.398085505725305],\n    [-99.20957922935487, 19.398004547302467],\n    [-99.20939683914186, 19.39792358883935],\n    [-99.20918226242067, 19.39786286996558],\n    [-99.20899987220764, 19.397822390703805],\n    [-99.20891404151918, 19.397994427496787],\n    [-99.20890331268312, 19.398176583902874],\n    [-99.20889258384706, 19.398368859888045],\n    [-99.20889258384706, 19.398540896103246],\n    [-99.20890331268312, 19.39872305189756],\n    [-99.20889258384706, 19.39890520748796],\n    [-99.20889258384706, 19.39907724313608],\n    [-99.20889258384706, 19.399259398329956],\n    [-99.20890331268312, 19.399431433603585],\n    [-99.20890331268312, 19.39961358840092],\n    [-99.20890331268312, 19.399785623300048],\n    [-99.20897841453552, 19.399937418648214],\n    [-99.20919299125673, 19.399937418648214],\n    [-99.2093861103058, 19.39991717927664],\n    [-99.20956850051881, 19.39996777770086],\n    [-99.20961141586305, 19.40013981222548],\n    [-99.20963287353517, 19.40032196622975],\n    [-99.20978307724, 19.4004130431554],\n    [-99.20996546745302, 19.40039280384301],\n    [-99.21019077301027, 19.400372564528084],\n    [-99.21042680740356, 19.40036244486966],\n]\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=polygon_points),\n)\n\n# NDJSON\npolygon_prediction_ndjson = {\n    \"name\": \"polygon_geo\",\n    \"confidence\": 0.5,\n    \"polygon\": polygon_points_ndjson,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding Box #######\ncoord_object = {\n    \"coordinates\": [[\n        [-99.20746564865112, 19.39799442829336],\n        [-99.20746564865112, 19.39925939999194],\n        [-99.20568466186523, 19.39925939999194],\n        [-99.20568466186523, 19.39799442829336],\n        [-99.20746564865112, 19.39799442829336],\n    ]]\n}\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bbox_geo\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\":\n            coord_object[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object[\"coordinates\"][0][3][1] -\n            coord_object[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object[\"coordinates\"][0][3][0] -\n            coord_object[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - radio (single choice) #######\n\n# Python Annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question_geo\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\ncoord_object_checklist = {\n    \"coordinates\": [[\n        [-99.210266, 19.39540372195134],\n        [-99.210266, 19.396901],\n        [-99.20621067903966, 19.396901],\n        [-99.20621067903966, 19.39540372195134],\n        [-99.210266, 19.39540372195134],\n    ]]\n}\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\n# NDJSON\nbbox_with_checklist_subclass_ndjson = {\n    \"name\": \"bbox_checklist_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"checklist_class_name\",\n        \"answer\": [{\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_checklist[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_checklist[\"coordinates\"][0][3][1] -\n            coord_object_checklist[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_checklist[\"coordinates\"][0][3][0] -\n            coord_object_checklist[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification free form text with bbox #######\n\ncoord_object_text = {\n    \"coordinates\": [[\n        [-99.21019613742828, 19.397447957052933],\n        [-99.21019613742828, 19.39772119262215],\n        [-99.20986354351044, 19.39772119262215],\n        [-99.20986354351044, 19.397447957052933],\n        [-99.21019613742828, 19.397447957052933],\n    ]]\n}\n# Python Annotation\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\n# NDJSON\nbbox_with_free_text_subclass_ndjson = {\n    \"name\": \"bbox_text_geo\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"free_text_geo\",\n        \"confidence\": 0.5,\n        \"answer\": \"sample text\"\n    }],\n    \"bbox\": {\n        \"top\":\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"left\":\n            coord_object_text[\"coordinates\"][0][1][0],\n        \"height\":\n            coord_object_text[\"coordinates\"][0][3][1] -\n            coord_object_text[\"coordinates\"][0][1][1],\n        \"width\":\n            coord_object_text[\"coordinates\"][0][3][0] -\n            coord_object_text[\"coordinates\"][0][1][0],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Classification - Checklist (multi-choice) #######\n\n# Python Annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question_geo\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"third_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifications)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.2)),\n            )\n        ],\n    )),\n)\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.2,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.3\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "top_left_bound = lb_types.Point(x=-99.21052827588443, y=19.400498983095076)\nbottom_right_bound = lb_types.Point(x=-99.20534818927473, y=19.39533555271248)\n\nepsg = lb_types.EPSG.EPSG4326\nbounds = lb_types.TiledBounds(epsg=epsg,\n                              bounds=[top_left_bound, bottom_right_bound])\nglobal_key = \"mexico_city\"\n\ntile_layer = lb_types.TileLayer(\n    url=\n    \"https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png\"\n)\n\ntiled_image_data = lb_types.TiledImageData(tile_layer=tile_layer,\n                                           tile_bounds=bounds,\n                                           zoom_levels=[17, 23])\n\nasset = {\n    \"row_data\": tiled_image_data.asdict(),\n    \"global_key\": global_key,\n    \"media_type\": \"TMS_GEO\",\n}\n\ndataset = client.create_dataset(name=\"geo_demo_dataset\")\ntask = dataset.create_data_rows([asset])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_geo\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo\"),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_geo\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_checklist_geo\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class_name\",\n                    options=[lb.Option(value=\"first_checklist_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_text_geo\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"free_text_geo\"),\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question_geo\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question_geo\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                ),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Geospatial Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Geospatial_Tile,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"geospatial_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n\nhsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\nmask = cv2.inRange(hsv, (25, 50, 25), (100, 150, 255))\nkernel = np.ones((15, 20), np.uint8)\nmask = cv2.erode(mask, kernel)\nmask = cv2.dilate(mask, kernel)\nmask_annotation = lb_types.MaskData.from_2D_arr(mask)\nmask_data = lb_types.Mask(mask=mask_annotation, color=[255, 255, 255])\nh, w, _ = tiled_image_data.value.shape\npixel_bounds = lb_types.TiledBounds(\n    epsg=lb_types.EPSG.SIMPLEPIXEL,\n    bounds=[lb_types.Point(x=0, y=0),\n            lb_types.Point(x=w, y=h)],\n)\ntransformer = lb_types.EPSGTransformer.create_pixel_to_geo_transformer(\n    src_epsg=pixel_bounds.epsg,\n    pixel_bounds=pixel_bounds,\n    geo_bounds=tiled_image_data.tile_bounds,\n    zoom=23,\n)\npixel_polygons = mask_data.shapely.simplify(3)\nlist_of_polygons = [\n    transformer(lb_types.Polygon.from_shapely(p)) for p in pixel_polygons.geoms\n]\npolygon_prediction_two = lb_types.ObjectAnnotation(value=list_of_polygons[0],\n                                                   name=\"polygon_geo_2\",\n                                                   confidence=0.5)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data={\n            \"global_key\": global_key,\n            \"tile_layer\": tile_layer,\n            \"tile_bounds\": bounds,\n            \"zoom_levels\": [12, 20],\n        },\n        annotations=[\n            point_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            bbox_prediction,\n            radio_prediction,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_prediction,\n            polygon_prediction_two,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# If using NDJSON"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson = []\nfor prediction in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_with_free_text_subclass_ndjson,\n        bbox_with_checklist_subclass_ndjson,\n        bbox_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        polygon_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n]:\n    prediction.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_ndjson.append(prediction)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(name=\"prediction_upload_job\" +\n                                                  str(uuid.uuid4()),\n                                                  predictions=labels)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"geospatial_prediction_demo\",\n                                media_type=lb.MediaType.Geospatial_Tile)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_geospatial_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key],  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Point #######\n\n# Python Annotation\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point_geo\",\n    value=lb_types.Point(x=-99.20647859573366, y=19.40018029091072),\n)\n\n####### Polyline #######\nline_points = []\nline_points_ndjson = []\n\nfor sub in coords:\n    line_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    line_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline_geo\",\n    value=lb_types.Line(points=line_points),\n)\n\npolygon_points = []\npolygon_points_ndjson = []\n\nfor sub in coords_polygon:\n    polygon_points.append(lb_types.Point(x=sub[0], y=sub[1]))\n    polygon_points_ndjson.append({\"x\": sub[0], \"y\": sub[1]})\n\n# Python Annotation\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon_geo\",\n    value=lb_types.Polygon(points=polygon_points),\n)\n\nbbox_top_left = lb_types.Point(x=-99.20746564865112, y=19.39799442829336)\nbbox_bottom_right = lb_types.Point(x=-99.20568466186523, y=19.39925939999194)\n\n# Python Annotation\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_geo\",\n    value=lb_types.Rectangle(start=bbox_top_left, end=bbox_bottom_right),\n)\n\n# Python Annotation\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question_geo\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\n# Python Annotation\nbbox_with_checklist_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_checklist_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.210266, y=19.39540372195134),  # Top left\n        end=lb_types.Point(x=-99.20621067903966, y=19.396901),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"checklist_class_name\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n            ]),\n        )\n    ],\n)\n\nbbox_with_free_text_subclass = lb_types.ObjectAnnotation(\n    name=\"bbox_text_geo\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=-99.21019613742828,\n                             y=19.397447957052933),  # Top left\n        end=lb_types.Point(x=-99.20986354351044,\n                           y=19.39772119262215),  # Bottom right\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"free_text_geo\", value=lb_types.Text(answer=\"sample text\"))\n    ],\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question_geo\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\nlabels.append(\n    lb_types.Label(\n        data=lb_types.TiledImageData(\n            global_key=global_key,\n            tile_layer=tile_layer,\n            tile_bounds=bounds,\n            zoom_levels=[12, 20],\n        ),\n        annotations=[\n            point_annotation,\n            polyline_annotation,\n            polygon_annotation,\n            bbox_annotation,\n            radio_annotation,\n            bbox_with_checklist_subclass,\n            bbox_with_free_text_subclass,\n            checklist_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"geospatial_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6. Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# upload_job\n# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/html_predictions.ipynb b/examples/prediction_upload/html_predictions.ipynb
index f5b4bb94f..14e5510a1 100644
--- a/examples/prediction_upload/html_predictions.ipynb
+++ b/examples/prediction_upload/html_predictions.ipynb
@@ -1,339 +1,337 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# HTML Prediction Import\n",
-                "\n",
-                "This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for HTML assets.\n",
-                "\n",
-                "**Supported predictions**\n",
-                "- Radio Classification \n",
-                "- Checklist Classification\n",
-                "- free-text Classification\n",
-                "\n",
-                "**Not supported:**\n",
-                "- Bounding Box\n",
-                "- Polygon\n",
-                "- Point\n",
-                "- Polyline\n",
-                "- Masks\n",
-                "- NER\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport numpy as np",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification ###########\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",  # Should match the name in the ontology\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "#### Nested Classifications ######\n\n# Python annotation\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification Free-Form text  ##########\n## Text classifications do not support confidence values\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"sample_html_2.html\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"html prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names should match the name field in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\n            \"radio_question\",  # name matching the tool used in the annotation\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ])\n\nontology = client.create_ontology(\n    \"Ontology HTML Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"HTML_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.HTMLData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            checklist_prediction,\n            text_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\nfor annot in [\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_prediction_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"HTML prediction import demo\",\n                                media_type=lb.MediaType.Html)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_prediction_html\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######  Annotations ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"html_annotation_import\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/html_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# HTML Prediction Import\n",
+        "\n",
+        "This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for HTML assets.\n",
+        "\n",
+        "**Supported predictions**\n",
+        "- Radio Classification \n",
+        "- Checklist Classification\n",
+        "- free-text Classification\n",
+        "\n",
+        "**Not supported:**\n",
+        "- Bounding Box\n",
+        "- Polygon\n",
+        "- Point\n",
+        "- Polyline\n",
+        "- Masks\n",
+        "- NER\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid\nimport numpy as np",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification ###########\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",  # Should match the name in the ontology\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\"\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "#### Nested Classifications ######\n\n# Python annotation\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification Free-Form text  ##########\n## Text classifications do not support confidence values\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"sample_html_2.html\"\n\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/html_sample_data/sample_html_2.html\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"html prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names should match the name field in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\n            \"radio_question\",  # name matching the tool used in the annotation\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ])\n\nontology = client.create_ontology(\n    \"Ontology HTML Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Html,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"HTML_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of code in the **Supported Predictions** section.\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.HTMLData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            checklist_prediction,\n            text_prediction,\n            nested_checklist_prediction,\n            nested_radio_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\nfor annot in [\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_prediction_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"HTML prediction import demo\",\n                                media_type=lb.MediaType.Html)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_prediction_html\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######  Annotations ######\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label = []\nlabel.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            text_annotation,\n            checklist_annotation,\n            radio_annotation,\n            nested_checklist_annotation,\n            nested_radio_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"html_annotation_import\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/image_predictions.ipynb b/examples/prediction_upload/image_predictions.ipynb
index b0ce9a97f..b28c9f78f 100644
--- a/examples/prediction_upload/image_predictions.ipynb
+++ b/examples/prediction_upload/image_predictions.ipynb
@@ -1,473 +1,471 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Image Prediction Import\n",
-                "\n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for image assets. \n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n",
-                "**Supported annotations that can be uploaded through the SDK**\n",
-                "\n",
-                "- Bounding box \n",
-                "- Polygon\n",
-                "- Point\n",
-                "- Polyline \n",
-                "- Raster Segmentation\n",
-                "- Classification free-text\n",
-                "- Classification - radio\n",
-                "- Classification - checklist\n",
-                "\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Notes:\n",
-                "    * If you are importing more than 1,000 mask predictions at a time, consider submitting separate jobs, as they can take longer than other prediction types to import.\n",
-                "    * After the execution of this notebook a complete Model Run with predictions will be created in your organization. "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Radio (single-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Nested radio and checklist"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Checklist (multi-choice)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotations\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding Box"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Bounding box with nested classification "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding box with nested classification #######\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\",\n            \"confidence\": 0.5\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polygon"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\n# NDJSON\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Classification: Free-form text"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Segmentation mask"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "### Raster Segmentation (Byte string array)\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\nresponse = requests.get(url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nmask_prediction = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_prediction_ndjson = {\n    \"name\": \"mask\",\n    \"classifications\": [],\n    \"mask\": {\n        \"instanceURI\": url,\n        \"colorRGB\": (255, 255, 255)\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Segmentation mask with nested classification"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "url_2 = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg_with_subclass.png\"\nresponse_2 = requests.get(url_2)\nmask_data_2 = lb_types.MaskData(im_bytes=response_2.content)\n\n# Python annotation\nmask_with_text_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_with_text_subclass_prediction_ndjson = {\n    \"name\":\n        \"mask_with_text_subclass\",\n    \"mask\": {\n        \"instanceURI\": url_2,\n        \"colorRGB\": (255, 255, 255)\n    },\n    \"classifications\": [{\n        \"name\": \"sub_free_text\",\n        \"answer\": \"free text answer\"\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Point"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Polyline"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\n\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s.jpeg\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(name=\"image_prediction_demo\")\ntask = dataset.create_data_rows([test_img_url])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"image_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            mask_prediction,\n            mask_with_text_subclass_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON:"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        mask_prediction_ndjson,\n        mask_with_text_subclass_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to a model run\n",
-                "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations. \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Image Prediction Demo\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nmask_annotation = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\nmask_with_text_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    mask_annotation,\n    mask_with_text_subclass_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the annotations from the project and add them to the model\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/image_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Image Prediction Import\n",
+        "\n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for image assets. \n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n",
+        "**Supported annotations that can be uploaded through the SDK**\n",
+        "\n",
+        "- Bounding box \n",
+        "- Polygon\n",
+        "- Point\n",
+        "- Polyline \n",
+        "- Raster Segmentation\n",
+        "- Classification free-text\n",
+        "- Classification - radio\n",
+        "- Classification - checklist\n",
+        "\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Notes:\n",
+        "    * If you are importing more than 1,000 mask predictions at a time, consider submitting separate jobs, as they can take longer than other prediction types to import.\n",
+        "    * After the execution of this notebook a complete Model Run with predictions will be created in your organization. "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Radio (single-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"second_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Nested radio and checklist"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "nested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Checklist (multi-choice)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotations\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding Box"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"confidence\": 0.5,\n    \"bbox\": {\n        \"top\": 977,\n        \"left\": 1690,\n        \"height\": 330,\n        \"width\": 225\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Bounding box with nested classification "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding box with nested classification #######\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\n## NDJSON\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"confidence\": 0.5,\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\": \"first_sub_radio_answer\",\n            \"confidence\": 0.5\n        },\n    }],\n    \"bbox\": {\n        \"top\": 933,\n        \"left\": 541,\n        \"height\": 191,\n        \"width\": 330\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polygon"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Anotation\npolygon_prediction = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    confidence=0.5,\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\n# NDJSON\n\npolygon_prediction_ndjson = {\n    \"name\":\n        \"polygon\",\n    \"confidence\":\n        0.5,\n    \"polygon\": [\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n        {\n            \"x\": 2278.306,\n            \"y\": 256.885\n        },\n        {\n            \"x\": 2428.197,\n            \"y\": 200.437\n        },\n        {\n            \"x\": 2560.0,\n            \"y\": 335.419\n        },\n        {\n            \"x\": 2557.386,\n            \"y\": 503.165\n        },\n        {\n            \"x\": 2320.596,\n            \"y\": 503.103\n        },\n        {\n            \"x\": 2156.083,\n            \"y\": 628.943\n        },\n        {\n            \"x\": 2161.111,\n            \"y\": 785.519\n        },\n        {\n            \"x\": 2002.115,\n            \"y\": 894.647\n        },\n        {\n            \"x\": 1838.456,\n            \"y\": 877.874\n        },\n        {\n            \"x\": 1436.53,\n            \"y\": 874.636\n        },\n        {\n            \"x\": 1411.403,\n            \"y\": 758.579\n        },\n        {\n            \"x\": 1353.853,\n            \"y\": 751.74\n        },\n        {\n            \"x\": 1345.264,\n            \"y\": 453.461\n        },\n        {\n            \"x\": 1426.011,\n            \"y\": 421.129\n        },\n        {\n            \"x\": 1489.581,\n            \"y\": 183.934\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Classification: Free-form text"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python annotation\ntext_annotation = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n# NDJSON\ntext_annotation_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Segmentation mask"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "### Raster Segmentation (Byte string array)\nurl = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\nresponse = requests.get(url)\n\nmask_data = lb.types.MaskData(\n    im_bytes=response.content\n)  # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\nmask_prediction = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_prediction_ndjson = {\n    \"name\": \"mask\",\n    \"classifications\": [],\n    \"mask\": {\n        \"instanceURI\": url,\n        \"colorRGB\": (255, 255, 255)\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Segmentation mask with nested classification"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "url_2 = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg_with_subclass.png\"\nresponse_2 = requests.get(url_2)\nmask_data_2 = lb_types.MaskData(im_bytes=response_2.content)\n\n# Python annotation\nmask_with_text_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\n# NDJSON using instanceURI, bytes array is not fully supported.\nmask_with_text_subclass_prediction_ndjson = {\n    \"name\":\n        \"mask_with_text_subclass\",\n    \"mask\": {\n        \"instanceURI\": url_2,\n        \"colorRGB\": (255, 255, 255)\n    },\n    \"classifications\": [{\n        \"name\": \"sub_free_text\",\n        \"answer\": \"free text answer\"\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Point"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_prediction = lb_types.ObjectAnnotation(\n    name=\"point\",\n    confidence=0.5,\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\": \"point\",\n    \"confidence\": 0.5,\n    \"classifications\": [],\n    \"point\": {\n        \"x\": 1166.606,\n        \"y\": 1441.768\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Polyline"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\n\npolyline_prediction = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    confidence=0.5,\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"polyline\",\n    \"confidence\":\n        0.5,\n    \"classifications\": [],\n    \"line\": [\n        {\n            \"x\": 2534.353,\n            \"y\": 249.471\n        },\n        {\n            \"x\": 2429.492,\n            \"y\": 182.092\n        },\n        {\n            \"x\": 2294.322,\n            \"y\": 221.962\n        },\n        {\n            \"x\": 2224.491,\n            \"y\": 180.463\n        },\n        {\n            \"x\": 2136.123,\n            \"y\": 204.716\n        },\n        {\n            \"x\": 1712.247,\n            \"y\": 173.949\n        },\n        {\n            \"x\": 1703.838,\n            \"y\": 84.438\n        },\n        {\n            \"x\": 1579.772,\n            \"y\": 82.61\n        },\n        {\n            \"x\": 1583.442,\n            \"y\": 167.552\n        },\n        {\n            \"x\": 1478.869,\n            \"y\": 164.903\n        },\n        {\n            \"x\": 1418.941,\n            \"y\": 318.149\n        },\n        {\n            \"x\": 1243.128,\n            \"y\": 400.815\n        },\n        {\n            \"x\": 1022.067,\n            \"y\": 319.007\n        },\n        {\n            \"x\": 892.367,\n            \"y\": 379.216\n        },\n        {\n            \"x\": 670.273,\n            \"y\": 364.408\n        },\n        {\n            \"x\": 613.114,\n            \"y\": 288.16\n        },\n        {\n            \"x\": 377.559,\n            \"y\": 238.251\n        },\n        {\n            \"x\": 368.087,\n            \"y\": 185.064\n        },\n        {\n            \"x\": 246.557,\n            \"y\": 167.286\n        },\n        {\n            \"x\": 236.648,\n            \"y\": 285.61\n        },\n        {\n            \"x\": 90.929,\n            \"y\": 326.412\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"2560px-Kitano_Street_Kobe01s.jpeg\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(name=\"image_prediction_demo\")\ntask = dataset.create_data_rows([test_img_url])\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of tools\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[lb.Option(value=\"first_sub_radio_answer\")],\n                ),\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.RASTER_SEGMENTATION,\n            name=\"mask_with_text_subclass\",\n            classifications=[\n                lb.Classification(class_type=lb.Classification.Type.TEXT,\n                                  name=\"sub_free_text\")\n            ],\n        ),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Image Prediction Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"image_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_prediction = []\nlabel_prediction.append(\n    lb_types.Label(\n        data=lb_types.ImageData(global_key=global_key),\n        annotations=[\n            radio_prediction,\n            nested_radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            polyline_prediction,\n            polygon_prediction,\n            mask_prediction,\n            mask_with_text_subclass_prediction,\n            point_prediction,\n            text_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON:"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_prediction_ndjson = []\n\nfor annot in [\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        polygon_prediction_ndjson,\n        mask_prediction_ndjson,\n        mask_with_text_subclass_prediction_ndjson,\n        point_prediction_ndjson,\n        polyline_prediction_ndjson,\n        text_annotation_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_prediction,\n)\n\n# Errors will appear for prediction uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to a model run\n",
+        "To visualize both annotations and predictions in the model run we will create a project with ground truth annotations. \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Image Prediction Demo\",\n                                media_type=lb.MediaType.Image)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_predictions_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########### Annotations ###########\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"second_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n        end=lb_types.Point(x=1915,\n                           y=1307),  # x= left + width , y = top + height\n    ),\n)\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.Rectangle(\n        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\", confidence=0.5)),\n        )\n    ],\n)\n\npolygon_annotation = lb_types.ObjectAnnotation(\n    name=\"polygon\",\n    value=lb_types.Polygon(points=[\n        lb_types.Point(x=1489.581, y=183.934),\n        lb_types.Point(x=2278.306, y=256.885),\n        lb_types.Point(x=2428.197, y=200.437),\n        lb_types.Point(x=2560.0, y=335.419),\n        lb_types.Point(x=2557.386, y=503.165),\n        lb_types.Point(x=2320.596, y=503.103),\n        lb_types.Point(x=2156.083, y=628.943),\n        lb_types.Point(x=2161.111, y=785.519),\n        lb_types.Point(x=2002.115, y=894.647),\n        lb_types.Point(x=1838.456, y=877.874),\n        lb_types.Point(x=1436.53, y=874.636),\n        lb_types.Point(x=1411.403, y=758.579),\n        lb_types.Point(x=1353.853, y=751.74),\n        lb_types.Point(x=1345.264, y=453.461),\n        lb_types.Point(x=1426.011, y=421.129),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nmask_annotation = lb_types.ObjectAnnotation(name=\"mask\",\n                                            value=lb_types.Mask(mask=mask_data,\n                                                                color=(255, 255,\n                                                                       255)))\n\nmask_with_text_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"mask_with_text_subclass\",  # must match your ontology feature\"s name\n    value=lb_types.Mask(mask=mask_data_2, color=(255, 255, 255)),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_free_text\",\n            value=lb_types.Text(answer=\"free text answer\"))\n    ],\n)\n\npoint_annotation = lb_types.ObjectAnnotation(\n    name=\"point\",\n    value=lb_types.Point(x=1166.606, y=1441.768),\n)\n\npolyline_annotation = lb_types.ObjectAnnotation(\n    name=\"polyline\",\n    value=lb_types.Line(points=[\n        lb_types.Point(x=2534.353, y=249.471),\n        lb_types.Point(x=2429.492, y=182.092),\n        lb_types.Point(x=2294.322, y=221.962),\n        lb_types.Point(x=2224.491, y=180.463),\n        lb_types.Point(x=2136.123, y=204.716),\n        lb_types.Point(x=1712.247, y=173.949),\n        lb_types.Point(x=1703.838, y=84.438),\n        lb_types.Point(x=1579.772, y=82.61),\n        lb_types.Point(x=1583.442, y=167.552),\n        lb_types.Point(x=1478.869, y=164.903),\n        lb_types.Point(x=1418.941, y=318.149),\n        lb_types.Point(x=1243.128, y=400.815),\n        lb_types.Point(x=1022.067, y=319.007),\n        lb_types.Point(x=892.367, y=379.216),\n        lb_types.Point(x=670.273, y=364.408),\n        lb_types.Point(x=613.114, y=288.16),\n        lb_types.Point(x=377.559, y=238.251),\n        lb_types.Point(x=368.087, y=185.064),\n        lb_types.Point(x=246.557, y=167.286),\n        lb_types.Point(x=236.648, y=285.61),\n        lb_types.Point(x=90.929, y=326.412),\n    ]),\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel = []\nannotations = [\n    radio_annotation,\n    nested_radio_annotation,\n    checklist_annotation,\n    nested_checklist_annotation,\n    text_annotation,\n    bbox_annotation,\n    bbox_with_radio_subclass_annotation,\n    polygon_annotation,\n    mask_annotation,\n    mask_with_text_subclass_annotation,\n    point_annotation,\n    polyline_annotation,\n]\nlabel.append(\n    lb_types.Label(data={\"global_key\": global_key}, annotations=annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"annotation_import_\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the annotations from the project and add them to the model\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/pdf_predictions.ipynb b/examples/prediction_upload/pdf_predictions.ipynb
index a07642a63..82d9f05cb 100644
--- a/examples/prediction_upload/pdf_predictions.ipynb
+++ b/examples/prediction_upload/pdf_predictions.ipynb
@@ -1,422 +1,420 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# PDF Prediction Import "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "*Annotation types*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box\n",
-                "- Entities\n",
-                "- Relationships (only supported for MAL imports)\n",
-                "\n",
-                "\n",
-                "*NDJson*\n",
-                "- Checklist classification (including nested classifications)\n",
-                "- Radio classifications (including nested classifications)\n",
-                "- Free text classifications\n",
-                "- Bounding box \n",
-                "- Entities \n",
-                "- Relationships (only supported for MAL imports)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API key"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entity ##########\n\n# Annotation Types\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"confidence\":\n        0.5,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########### Radio Classification #########\n\n# Annotation types\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ Bounding Box ###########\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": bbox_dim_1,\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# ############ global nested classifications ###########\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############## Classification Free-form text ##############\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### BBOX with nested classifications #########\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\",\n                                confidence=0.5,\n                            )),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\",\n                    \"confidence\": 0.5,\n                },\n            }],\n        },\n    }],\n    \"bbox\": bbox_dim,\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
-                "\n",
-                "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
-                "- The document must have no more than 15 pages.\n",
-                "- The file size should not exceed 20 MB.\n",
-                "\n",
-                "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
-                "\n",
-                "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
-                "\n",
-                "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "global_key = \"0801.3483.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"PDF_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5: Create the predictions payload\n",
-                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting payload should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "To extract the generated text layer url we first need to export the data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n\n# re-write the entity annotation with text selections\nentities_prediction_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", confidence=0.5, textSelections=text_selections)\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_prediction_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    textSelections=text_selections_ner,\n)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_prediction_ndjson}\")\nprint(f\"entities_annotation={entities_prediction}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_prediction_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Python annotation \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_predictions = []\n\nlabel_predictions.append(\n    lb_types.Label(\n        data=lb_types.DocumentData(global_key=global_key),\n        annotations=[\n            entities_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            ner_with_checklist_subclass_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_predictions_ndjson = []\nfor annot in [\n        entities_prediction_ndjson,\n        checklist_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        ner_with_checklist_subclass_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_predictions_ndjson.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6: Upload the predictions payload to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run\n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.1 Create a labelbox project \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project = client.create_project(name=\"Document Prediction Import Demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.2 Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "entities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(name=\"named_entity\",\n                                  textSelections=text_selections),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",)),\n            )\n        ],\n    )),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\",\n                                  text_selections=text_selections_ner),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.4 Create the label object "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.5 Upload annotations to the project using Label import\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "7.6 Send the annotations to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Option deletions for cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/pdf_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# PDF Prediction Import "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "*Annotation types*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box\n",
+        "- Entities\n",
+        "- Relationships (only supported for MAL imports)\n",
+        "\n",
+        "\n",
+        "*NDJson*\n",
+        "- Checklist classification (including nested classifications)\n",
+        "- Radio classifications (including nested classifications)\n",
+        "- Free text classifications\n",
+        "- Bounding box \n",
+        "- Entities \n",
+        "- Relationships (only supported for MAL imports)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import uuid\nimport json\nimport requests\nimport labelbox as lb\nimport labelbox.types as lb_types",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API key"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entity ##########\n\n# Annotation Types\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"named_entity\",\n        textSelections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\":\n        \"named_entity\",\n    \"confidence\":\n        0.5,\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\",],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1,\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########### Radio Classification #########\n\n# Annotation types\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Checklist Classification ###########\n\n# Annotation types\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\":\n        \"checklist_question\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ Bounding Box ###########\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_prediction = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nbbox_prediction_ndjson = {\n    \"name\": \"bounding_box\",\n    \"bbox\": bbox_dim_1,\n    \"page\": 0,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# ############ global nested classifications ###########\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=\n                            0.5,  # Confidence scores should be added to the answer\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\":\n                    0.5,  # Confidence scores should be added to the answer\n            },\n        }],\n    }],\n}\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",\n                    confidence=\n                    0.5,  # Confidence scores should be added to the answer\n                )),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############## Classification Free-form text ##############\n\ntext_prediction = lb_types.ClassificationAnnotation(\n    name=\"free_text\",  # must match your ontology feature\"s name\n    value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n)\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### BBOX with nested classifications #########\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\",\n                                confidence=0.5,\n                            )),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nbbox_with_radio_subclass_prediction_ndjson = {\n    \"name\": \"bbox_with_radio_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_radio_question\",\n        \"answer\": {\n            \"name\":\n                \"first_sub_radio_answer\",\n            \"confidence\":\n                0.5,\n            \"classifications\": [{\n                \"name\": \"second_sub_radio_question\",\n                \"answer\": {\n                    \"name\": \"second_sub_radio_answer\",\n                    \"confidence\": 0.5,\n                },\n            }],\n        },\n    }],\n    \"bbox\": bbox_dim,\n    \"page\": 1,\n    \"unit\": \"POINTS\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "############ NER with nested classifications ########\n\nner_with_checklist_subclass_prediction = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=lb_types.DocumentEntity(\n        name=\"ner_with_checklist_subclass\",\n        text_selections=[\n            lb_types.DocumentTextSelection(token_ids=[], group_id=\"\", page=1)\n        ],\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                              confidence=0.5)\n            ]),\n        )\n    ],\n)\n\nner_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"ner_with_checklist_subclass\",\n    \"classifications\": [{\n        \"name\": \"sub_checklist_question\",\n        \"answer\": [{\n            \"name\": \"first_sub_checklist_answer\",\n            \"confidence\": 0.5\n        }],\n    }],\n    \"textSelections\": [{\n        \"tokenIds\": [\"<UUID>\"],\n        \"groupId\": \"<UUID>\",\n        \"page\": 1\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Passing a `text_layer_url` is not longer required. Labelbox automatically generates a text layer using Google Document AI and its OCR engine to detect tokens. \n",
+        "\n",
+        "However, it's important to note that Google Document AI imposes specific restrictions on document size:\n",
+        "- The document must have no more than 15 pages.\n",
+        "- The file size should not exceed 20 MB.\n",
+        "\n",
+        "Furthermore, Google Document AI optimizes documents before OCR processing. This optimization might include rotating images or pages to ensure that text appears horizontally. Consequently, token coordinates are calculated based on the rotated/optimized images, resulting in potential discrepancies with the original PDF document.\n",
+        "\n",
+        "For example, in a landscape-oriented PDF, the document is rotated by 90 degrees before processing. As a result, all tokens in the text layer are also rotated by 90 degrees.\n",
+        "\n",
+        "You may still pass a `text_layer_url` if you wish to bypass the automatic text layer generation"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "global_key = \"0801.3483.pdf\"\nimg_url = {\n    \"row_data\": {\n        \"pdf_url\":\n            \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\"\n    },\n    \"global_key\": global_key,\n}\n\ndataset = client.create_dataset(name=\"pdf_demo_dataset\")\ntask = dataset.create_data_rows([img_url])\ntask.wait_till_done()\nprint(f\"Failed data rows: {task.failed_data_rows}\")\nprint(f\"Errors: {task.errors}\")\n\nif task.errors:\n    for error in task.errors:\n        if (\"Duplicate global key\" in error[\"message\"] and\n                dataset.row_count == 0):\n            # If the global key already  exists in the workspace the dataset will be created empty, so we can delete it.\n            print(f\"Deleting empty dataset: {dataset}\")\n            dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.TEXT,\n            name=\"free_text\",\n            scope=lb.Classification.Scope.GLOBAL,\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            scope=lb.Classification.Scope.GLOBAL,\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bounding_box\"),\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\"),\n        lb.Tool(\n            tool=lb.Tool.Type.NER,\n            name=\"ner_with_checklist_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"sub_checklist_question\",\n                    options=[lb.Option(value=\"first_sub_checklist_answer\")],\n                )\n            ],\n        ),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_with_radio_subclass\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.RADIO,\n                    name=\"sub_radio_question\",\n                    options=[\n                        lb.Option(\n                            value=\"first_sub_radio_answer\",\n                            options=[\n                                lb.Classification(\n                                    class_type=lb.Classification.Type.RADIO,\n                                    name=\"second_sub_radio_question\",\n                                    options=[\n                                        lb.Option(\"second_sub_radio_answer\")\n                                    ],\n                                )\n                            ],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Document Annotation Import Demo\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Document,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"PDF_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5: Create the predictions payload\n",
+        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting payload should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "To import ner annotations, you must pass a `text_layer_url`, Labelbox automatically generates a `text_layer_url` after importing a pdf asset that doesn't include a `text_layer_url`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "To extract the generated text layer url we first need to export the data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Helper method\ndef update_text_selections(annotation, group_id, list_tokens, page):\n    return annotation.update({\n        \"textSelections\": [{\n            \"groupId\": group_id,\n            \"tokenIds\": list_tokens,\n            \"page\": page\n        }]\n    })\n\n\n# Fetch the content of the text layer\nres = requests.get(text_layer)\n\n# Phrases that we want to annotation obtained from the text layer url\ncontent_phrases = [\n    \"Metal-insulator (MI) transitions have been one of the\",\n    \"T. Sasaki, N. Yoneyama, and N. Kobayashi\",\n]\n\n# Parse the text layer\ntext_selections = []\ntext_selections_ner = []\n\nfor obj in json.loads(res.text):\n    for group in obj[\"groups\"]:\n        if group[\"content\"] == content_phrases[0]:\n            list_tokens = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            document_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens, page=1)\n            text_selections.append(document_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=entities_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens,  # ids representing individual words from the group\n                page=1,\n            )\n        if group[\"content\"] == content_phrases[1]:\n            list_tokens_2 = [x[\"id\"] for x in group[\"tokens\"]]\n            # build text selections for Python Annotation Types\n            ner_text_selection = lb_types.DocumentTextSelection(\n                groupId=group[\"id\"], tokenIds=list_tokens_2, page=1)\n            text_selections_ner.append(ner_text_selection)\n            # build text selection for the NDJson annotations\n            update_text_selections(\n                annotation=ner_with_checklist_subclass_prediction_ndjson,\n                group_id=group[\"id\"],  # id representing group of words\n                list_tokens=\n                list_tokens_2,  # ids representing individual words from the group\n                page=1,\n            )\n\n# re-write the entity annotation with text selections\nentities_prediction_document_entity = lb_types.DocumentEntity(\n    name=\"named_entity\", confidence=0.5, textSelections=text_selections)\nentities_prediction = lb_types.ObjectAnnotation(\n    name=\"named_entity\", value=entities_prediction_document_entity)\n\n# re-write the entity annotation + subclassification with text selections\nclassifications = [\n    lb_types.ClassificationAnnotation(\n        name=\"sub_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    )\n]\nner_annotation_with_subclass = lb_types.DocumentEntity(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    textSelections=text_selections_ner,\n)\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    confidence=0.5,\n    value=ner_annotation_with_subclass,\n    classifications=classifications,\n)\n\n# Final NDJSON and python annotations\nprint(f\"entities_annotations_ndjson={entities_prediction_ndjson}\")\nprint(f\"entities_annotation={entities_prediction}\")\nprint(\n    f\"nested_entities_annotation_ndjson={ner_with_checklist_subclass_prediction_ndjson}\"\n)\nprint(f\"nested_entities_annotation={ner_with_checklist_subclass_annotation}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Python annotation \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_predictions = []\n\nlabel_predictions.append(\n    lb_types.Label(\n        data=lb_types.DocumentData(global_key=global_key),\n        annotations=[\n            entities_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n            radio_prediction,\n            nested_radio_prediction,\n            bbox_prediction,\n            bbox_with_radio_subclass_prediction,\n            ner_with_checklist_subclass_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_predictions_ndjson = []\nfor annot in [\n        entities_prediction_ndjson,\n        checklist_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        radio_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        bbox_prediction_ndjson,\n        bbox_with_radio_subclass_prediction_ndjson,\n        ner_with_checklist_subclass_prediction_ndjson,\n]:\n    annot.update({\n        \"dataRow\": {\n            \"globalKey\": global_key\n        },\n    })\n    label_predictions_ndjson.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6: Upload the predictions payload to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run\n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.1 Create a labelbox project \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project = client.create_project(name=\"Document Prediction Import Demo\",\n                                media_type=lb.MediaType.Document)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.2 Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "entities_annotation = lb_types.ObjectAnnotation(\n    name=\"named_entity\",\n    value=lb_types.DocumentEntity(name=\"named_entity\",\n                                  textSelections=text_selections),\n)\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n    ]),\n)\n\nbbox_dim_1 = {\"top\": 135.3, \"left\": 102.771, \"height\": 109.843, \"width\": 415.8}\nbbox_annotation = lb_types.ObjectAnnotation(\n    name=\"bounding_box\",  # must match your ontology feature\"s name\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim_1[\"left\"],\n                             y=bbox_dim_1[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim_1[\"left\"] + bbox_dim_1[\"width\"],\n            y=bbox_dim_1[\"top\"] + bbox_dim_1[\"height\"],\n        ),  # x= left + width , y = top + height\n        page=0,\n        unit=lb_types.RectangleUnit.POINTS,\n    ),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",)\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\",)),\n            )\n        ],\n    )),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))\n\nbbox_dim = {\n    \"top\": 226.757,\n    \"left\": 317.271,\n    \"height\": 194.229,\n    \"width\": 249.386,\n}\n\nbbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"bbox_with_radio_subclass\",\n    value=lb_types.DocumentRectangle(\n        start=lb_types.Point(x=bbox_dim[\"left\"],\n                             y=bbox_dim[\"top\"]),  # x = left, y = top\n        end=lb_types.Point(\n            x=bbox_dim[\"left\"] + bbox_dim[\"width\"],\n            y=bbox_dim[\"top\"] + bbox_dim[\"height\"],\n        ),  # x= left + width , y = top + height\n        unit=lb_types.RectangleUnit.POINTS,\n        page=1,\n    ),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_radio_question\",\n            value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                name=\"first_sub_radio_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"second_sub_radio_question\",\n                        value=lb_types.Radio(\n                            answer=lb_types.ClassificationAnswer(\n                                name=\"second_sub_radio_answer\")),\n                    )\n                ],\n            )),\n        )\n    ],\n)\n\nner_with_checklist_subclass_annotation = lb_types.ObjectAnnotation(\n    name=\"ner_with_checklist_subclass\",\n    value=lb_types.DocumentEntity(name=\"ner_with_checklist_subclass\",\n                                  text_selections=text_selections_ner),\n    classifications=[\n        lb_types.ClassificationAnnotation(\n            name=\"sub_checklist_question\",\n            value=lb_types.Checklist(answer=[\n                lb_types.ClassificationAnswer(name=\"first_sub_checklist_answer\")\n            ]),\n        )\n    ],\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.4 Create the label object "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "labels = []\n\nlabels.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n            radio_annotation,\n            nested_radio_annotation,\n            bbox_annotation,\n            bbox_with_radio_subclass_annotation,\n            ner_with_checklist_subclass_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.5 Upload annotations to the project using Label import\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "7.6 Send the annotations to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Option deletions for cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/text_predictions.ipynb b/examples/prediction_upload/text_predictions.ipynb
index 4d80894f9..8943ca17b 100644
--- a/examples/prediction_upload/text_predictions.ipynb
+++ b/examples/prediction_upload/text_predictions.ipynb
@@ -1,348 +1,346 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Text Prediction Import\n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for text assets. \n",
-                "\n",
-                "Supported annotations that can be uploaded through the SDK: \n",
-                "\n",
-                "* Entity\n",
-                "* Classification radio \n",
-                "* Classification checklist \n",
-                "* Classification free-form text \n",
-                "\n",
-                "**Not** supported:\n",
-                "* Segmentation mask\n",
-                "* Polygon\n",
-                "* Bounding box \n",
-                "* Polyline\n",
-                "* Point \n",
-                "\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nentities_prediction = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\",\n                                                confidence=0.5)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\": \"named_entity\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 10,\n        \"end\": 20\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"lorem-ipsum.txt\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"text prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\")\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Text Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Text,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"text_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
-                "\n",
-                "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
-                "\n",
-                "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label for predictions\nlabel_predictions = []\nlabel_predictions.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_prediction,\n            nested_radio_prediction,\n            radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "If using NDJSON: "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_ndjson_predictions = []\nfor annot in [\n        entities_prediction_ndjson,\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson_predictions.append(annot)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Text Prediction Import Demo\",\n                                media_type=lb.MediaType.Text)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "named_entity = lb_types.TextEntity(start=10, end=20)\nentities_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\")\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n# Create a Label for predictions\nlabel = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.TextData(global_key=global_key),\n        annotations=[\n            entities_annotation,\n            nested_radio_annotation,\n            radio_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n        ],\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6 Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/text_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Text Prediction Import\n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for text assets. \n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "\n",
+        "* Entity\n",
+        "* Classification radio \n",
+        "* Classification checklist \n",
+        "* Classification free-form text \n",
+        "\n",
+        "**Not** supported:\n",
+        "* Segmentation mask\n",
+        "* Polygon\n",
+        "* Bounding box \n",
+        "* Polyline\n",
+        "* Point \n",
+        "\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "########## Entities ##########\n\n# Python annotation\nnamed_entity = lb_types.TextEntity(start=10, end=20)\nentities_prediction = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\",\n                                                confidence=0.5)\n\n# NDJSON\nentities_prediction_ndjson = {\n    \"name\": \"named_entity\",\n    \"confidence\": 0.5,\n    \"location\": {\n        \"start\": 10,\n        \"end\": 20\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio (single choice ) ##########\n\n# Python annotation\nradio_prediction = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\", confidence=0.5)),\n)\n\n# NDJSON\nradio_prediction_ndjson = {\n    \"name\": \"radio_question\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification - Radio and Checklist (with subclassifcations)  ##########\n\nnested_radio_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        confidence=0.5,  # Confidence scores should be added to the answer\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\", confidence=0.5)),\n            )\n        ],\n    )),\n)\n\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\nnested_checklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            confidence=0.5,  # Confidence scores should be added to the answer\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\",\n                            confidence=0.5,\n                        )\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,  # Confidence scores should be added to the answer\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Checklist ##########\n\n# Python annotation\nchecklist_prediction = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                      confidence=0.5),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\",\n                                      confidence=0.5),\n    ]),\n)\n\n# NDJSON\nchecklist_prediction_ndjson = {\n    \"name\": \"checklist_question\",\n    \"answer\": [{\n        \"name\": \"first_checklist_answer\",\n        \"confidence\": 0.5\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classification Free-Form text  ##########\n\n# Python annotation\ntext_prediction = lb_types.ClassificationAnnotation(name=\"free_text\",\n                                                    value=lb_types.Text(\n                                                        answer=\"sample text\",\n                                                        confidence=0.5))\n\n#  NDJSON\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"answer\": \"sample text\",\n    \"confidence\": 0.5,\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"lorem-ipsum.txt\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"text prediction demo dataset\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors:\", task.errors)\nprint(\"Failed data rows:\", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Setup the ontology and link the tools created above.\n\nontology_builder = lb.OntologyBuilder(\n    classifications=[  # List of Classification objects\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_question\",\n            options=[lb.Option(value=\"first_radio_answer\")],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    value=\"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(value=\"first_sub_radio_answer\")],\n                        ),\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_question\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n                lb.Option(value=\"third_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n    ],\n    tools=[  # List of Tool objects\n        lb.Tool(tool=lb.Tool.Type.NER, name=\"named_entity\")\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Text Predictions\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Text,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"text_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the prediction payload using the snippets of code in the **Supported Predcitions** section\n",
+        "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
+        "\n",
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label for predictions\nlabel_predictions = []\nlabel_predictions.append(\n    lb_types.Label(\n        data={\"global_key\": global_key},\n        annotations=[\n            entities_prediction,\n            nested_radio_prediction,\n            radio_prediction,\n            checklist_prediction,\n            nested_checklist_prediction,\n            text_prediction,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "If using NDJSON: "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_ndjson_predictions = []\nfor annot in [\n        entities_prediction_ndjson,\n        radio_prediction_ndjson,\n        checklist_prediction_ndjson,\n        text_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n]:\n    annot.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_ndjson_predictions.append(annot)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"Text Prediction Import Demo\",\n                                media_type=lb.MediaType.Text)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_text_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[\n        global_key\n    ],  # Paginated collection of data row objects, list of data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "named_entity = lb_types.TextEntity(start=10, end=20)\nentities_annotation = lb_types.ObjectAnnotation(value=named_entity,\n                                                name=\"named_entity\")\n\nradio_annotation = lb_types.ClassificationAnnotation(\n    name=\"radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\")),\n)\n\nnested_radio_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_radio_question\",\n    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n        name=\"first_radio_answer\",\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"sub_radio_question\",\n                value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                    name=\"first_sub_radio_answer\")),\n            )\n        ],\n    )),\n)\n\nnested_checklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"nested_checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(\n            name=\"first_checklist_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_checklist_question\",\n                    value=lb_types.Checklist(answer=[\n                        lb_types.ClassificationAnswer(\n                            name=\"first_sub_checklist_answer\")\n                    ]),\n                )\n            ],\n        )\n    ]),\n)\n\nchecklist_annotation = lb_types.ClassificationAnnotation(\n    name=\"checklist_question\",\n    value=lb_types.Checklist(answer=[\n        lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        lb_types.ClassificationAnswer(name=\"third_checklist_answer\"),\n    ]),\n)\n\ntext_annotation = lb_types.ClassificationAnnotation(\n    name=\"free_text\", value=lb_types.Text(answer=\"sample text\"))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n# Create a Label for predictions\nlabel = []\nlabel.append(\n    lb_types.Label(\n        data=lb_types.TextData(global_key=global_key),\n        annotations=[\n            entities_annotation,\n            nested_radio_annotation,\n            radio_annotation,\n            checklist_annotation,\n            nested_checklist_annotation,\n            text_annotation,\n        ],\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"text_label_import_job\" + str(uuid.uuid4()),\n    labels=label,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6 Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/prediction_upload/video_predictions.ipynb b/examples/prediction_upload/video_predictions.ipynb
index 0899ee332..b4a7a5cd4 100644
--- a/examples/prediction_upload/video_predictions.ipynb
+++ b/examples/prediction_upload/video_predictions.ipynb
@@ -1,378 +1,376 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 0,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Video Prediction Import \n",
-                "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for video assets.\n",
-                "\n",
-                "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
-                "\n",
-                "**Supported annotations that can be uploaded through the SDK**\n",
-                "- Bounding box\n",
-                "- Point\n",
-                "- Polyline\n",
-                "- Classification - radio\n",
-                "- Classification - checklist\n",
-                "- Classification - free text\n",
-                "- Nested classifications \n",
-                "\n",
-                "**NOT** supported:\n",
-                "- Polygons [not supported in video editor or model]\n",
-                "- Raster segmentation masks [not supported in model]\n",
-                "- Vector segmentation masks [not supported in video editor]\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Setup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Replace with your API Key \n",
-                "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Supported Predictions\n",
-                "- Confidence scores are currently not supported for segment or frame annotations, which are required for bounding box, point, and line for video assets. For this tutorial, only the radio and checklist annotations will have confidence scores."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "####### Bounding box (frame specific) ###########\n\n# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 15,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Point ########\n# Confidence score is not supported for VideoObjectAnnotation\n# Python Annotation\npoint_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"confidence\":\n        0.5,\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Polyline (frame specific) ########\n# confidence scores are not supported in polyline annotations\n\n# Python Annotation\npolyline_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        12,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 280\n                        },\n                        {\n                            \"x\": 300,\n                            \"y\": 380\n                        },\n                        {\n                            \"x\": 400,\n                            \"y\": 460\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######## Frame base classifications ########\n\n# Python Annotation\nradio_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n]\n\nchecklist_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_prediction_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n## frame specific\nframe_checklist_classification_prediction_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 39,\n                \"end\": 45\n            }],\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "####### Global Classifications #########\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    )\n]\n\nglobal_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}\n\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\", confidence=0.5)),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\",\n                                confidence=0.5,\n                            )\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "########## Classifications under frame base tools ##########\n# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                                  confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\", confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "######### Free text classification ###########\ntext_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n    )\n]\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"confidence\": 0.5,\n    \"answer\": \"sample text\",\n}",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 1: Import data rows into Catalog"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# send a sample image as batch to the project\nglobal_key = \"sample-video-2.mp4\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"Video prediction demo\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors: \", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 2: Create/select an Ontology for your model predictions\n",
-                "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.\n                    INDEX,  ## defined scope for frame classifications\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Video Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 3: Create a Model and Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# create Model\nmodel = client.create_model(name=\"video_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 4: Send data rows to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "model_run.upsert_data_rows(global_keys=[global_key])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 5. Create the predictions payload\n",
-                "\n",
-                "Create the annotations payload using the snippets of [code here](https://docs.labelbox.com/reference/import-video-annotations).\n",
-                "\n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Python Annotation Types"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "label_predictions = []\nannotations_list = [\n    point_prediction,\n    bbox_prediction,\n    polyline_prediction,\n    checklist_prediction,\n    radio_prediction,\n    nested_radio_prediction,\n    nested_checklist_prediction,\n    frame_bbox_with_checklist_subclass_prediction,\n    global_radio_prediction,\n    global_checklist_prediction,\n    text_prediction,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabel_predictions.append(\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=flatten_list_annotations))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### NDJSON annotations"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel_prediction_ndjson = []\n\nfor annotation in [\n        point_prediction_ndjson,\n        bbox_prediction_ndjson,\n        polyline_prediction_ndjson,\n        frame_checklist_classification_prediction_ndjson,\n        frame_radio_classification_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        frame_bbox_with_checklist_subclass_prediction_ndjson,\n        global_radio_classification_ndjson,\n        global_checklist_classification_ndjson,\n        text_prediction_ndjson,\n]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotation)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 6. Upload the predictions payload to the Model Run "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Step 7: Send annotations to the Model Run \n",
-                "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.1. Create a labelbox project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Labelbox project\nproject = client.create_project(name=\"video_prediction_demo\",\n                                media_type=lb.MediaType.Video)\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.2. Create a batch to send to the project "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "project.create_batch(\n    \"batch_video_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # A list of data rows, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.3 Create the annotations payload"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n]\n\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\nframe_bbox_with_checklist_subclass = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.4. Create the label object"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n\nlabels = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabels.append(\n    lb_types.Label(\n        data=lb_types.VideoData(global_key=global_key),\n        annotations=flatten_list_annotations,\n    ))",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.5. Upload annotations to the project using Label Import"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"video_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "##### 7.6. Send the annotations to the Model Run"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Optional deletions for cleanup \n"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/prediction_upload/video_predictions.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Video Prediction Import \n",
+        "* This notebook walks you through the process of uploading model predictions to a Model Run. This notebook provides an example for each supported prediction type for video assets.\n",
+        "\n",
+        "A Model Run is a container for the predictions, annotations and metrics of a specific experiment in your ML model development cycle.\n",
+        "\n",
+        "**Supported annotations that can be uploaded through the SDK**\n",
+        "- Bounding box\n",
+        "- Point\n",
+        "- Polyline\n",
+        "- Classification - radio\n",
+        "- Classification - checklist\n",
+        "- Classification - free text\n",
+        "- Nested classifications \n",
+        "\n",
+        "**NOT** supported:\n",
+        "- Polygons [not supported in video editor or model]\n",
+        "- Raster segmentation masks [not supported in model]\n",
+        "- Vector segmentation masks [not supported in video editor]\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nimport labelbox.types as lb_types\nimport uuid",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Replace with your API Key \n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "API_KEY = \"\"\nclient = lb.Client(API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Supported Predictions\n",
+        "- Confidence scores are currently not supported for segment or frame annotations, which are required for bounding box, point, and line for video assets. For this tutorial, only the radio and checklist annotations will have confidence scores."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "####### Bounding box (frame specific) ###########\n\n# Confidence scores are not supported for frame specific bounding box annotations and VideoObjectAnnotation\n\n# bbox dimensions\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\n\n# Python Annotation\nbbox_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\n# NDJSON\nbbox_prediction_ndjson = {\n    \"name\":\n        \"bbox_video\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 13,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 15,\n                \"bbox\": bbox_dm\n            },\n            {\n                \"frame\": 19,\n                \"bbox\": bbox_dm\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Point ########\n# Confidence score is not supported for VideoObjectAnnotation\n# Python Annotation\npoint_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n# NDJSON\npoint_prediction_ndjson = {\n    \"name\":\n        \"point_video\",\n    \"confidence\":\n        0.5,\n    \"segments\": [{\n        \"keyframes\": [{\n            \"frame\": 17,\n            \"point\": {\n                \"x\": 660.134,\n                \"y\": 407.926\n            }\n        }]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Polyline (frame specific) ########\n# confidence scores are not supported in polyline annotations\n\n# Python Annotation\npolyline_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\n# NDJSON\npolyline_prediction_ndjson = {\n    \"name\":\n        \"line_video_frame\",\n    \"segments\": [\n        {\n            \"keyframes\": [\n                {\n                    \"frame\":\n                        5,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 100\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 190\n                        },\n                        {\n                            \"x\": 190,\n                            \"y\": 220\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        12,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 280\n                        },\n                        {\n                            \"x\": 300,\n                            \"y\": 380\n                        },\n                        {\n                            \"x\": 400,\n                            \"y\": 460\n                        },\n                    ],\n                },\n                {\n                    \"frame\":\n                        20,\n                    \"line\": [\n                        {\n                            \"x\": 680,\n                            \"y\": 180\n                        },\n                        {\n                            \"x\": 100,\n                            \"y\": 200\n                        },\n                        {\n                            \"x\": 200,\n                            \"y\": 260\n                        },\n                    ],\n                },\n            ]\n        },\n        {\n            \"keyframes\": [\n                {\n                    \"frame\": 24,\n                    \"line\": [{\n                        \"x\": 300,\n                        \"y\": 310\n                    }, {\n                        \"x\": 330,\n                        \"y\": 430\n                    }],\n                },\n                {\n                    \"frame\": 45,\n                    \"line\": [{\n                        \"x\": 600,\n                        \"y\": 810\n                    }, {\n                        \"x\": 900,\n                        \"y\": 930\n                    }],\n                },\n            ]\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######## Frame base classifications ########\n\n# Python Annotation\nradio_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    ),\n]\n\nchecklist_prediction = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5)\n        ]),\n    ),\n]\n\n## NDJSON\nframe_radio_classification_prediction_ndjson = {\n    \"name\": \"radio_class\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"frames\": [{\n            \"start\": 9,\n            \"end\": 15\n        }],\n    },\n}\n\n## frame specific\nframe_checklist_classification_prediction_ndjson = {\n    \"name\":\n        \"checklist_class\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"frames\": [{\n                \"start\": 29,\n                \"end\": 35\n            }],\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"frames\": [{\n                \"start\": 39,\n                \"end\": 45\n            }],\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "####### Global Classifications #########\n\n# Python Annotation\n## For global classifications use ClassificationAnnotation\nglobal_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\", confidence=0.5)),\n    )\n]\n\nglobal_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                          confidence=0.5),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\",\n                                          confidence=0.5),\n        ]),\n    )\n]\n\n# NDJSON\nglobal_radio_classification_ndjson = {\n    \"name\": \"radio_class_global\",\n    \"answer\": {\n        \"name\": \"first_radio_answer\",\n        \"confidence\": 0.5\n    },\n}\n\nglobal_checklist_classification_ndjson = {\n    \"name\":\n        \"checklist_class_global\",\n    \"answer\": [\n        {\n            \"name\": \"first_checklist_answer\",\n            \"confidence\": 0.5\n        },\n        {\n            \"name\": \"second_checklist_answer\",\n            \"confidence\": 0.5\n        },\n    ],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Nested Global Classification ###########\n\n# Python Annotation\nnested_radio_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            confidence=0.5,\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\", confidence=0.5)),\n                )\n            ],\n        )),\n    )\n]\n\n# NDJSON\nnested_radio_prediction_ndjson = {\n    \"name\": \"nested_radio_question\",\n    \"answer\": {\n        \"name\":\n            \"first_radio_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_radio_question\",\n            \"answer\": {\n                \"name\": \"first_sub_radio_answer\",\n                \"confidence\": 0.5\n            },\n        }],\n    },\n}\n\n# Python Annotation\nnested_checklist_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                confidence=0.5,\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\",\n                                confidence=0.5,\n                            )\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\n# NDJSON\nnested_checklist_prediction_ndjson = {\n    \"name\":\n        \"nested_checklist_question\",\n    \"answer\": [{\n        \"name\":\n            \"first_checklist_answer\",\n        \"confidence\":\n            0.5,\n        \"classifications\": [{\n            \"name\": \"sub_checklist_question\",\n            \"answer\": {\n                \"name\": \"first_sub_checklist_answer\",\n                \"confidence\": 0.5,\n            },\n        }],\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "########## Classifications under frame base tools ##########\n# Confidence scores are not supported for frame specific bounding box annotations with sub-classifications\n\n# bounding box dimensions\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\n\n# Python Annotation\nframe_bbox_with_checklist_subclass_prediction = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\",\n                                                  confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"], y=bbox_dm2[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\", confidence=0.5)\n                ]),\n            )\n        ],\n    ),\n]\n\nframe_bbox_with_checklist_subclass_prediction_ndjson = {\n    \"name\":\n        \"bbox_class\",\n    \"segments\": [{\n        \"keyframes\": [\n            {\n                \"frame\": 10,\n                \"bbox\": bbox_dm2\n            },\n            {\n                \"frame\":\n                    11,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"first_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n            {\n                \"frame\":\n                    13,\n                \"bbox\":\n                    bbox_dm2,\n                \"classifications\": [{\n                    \"name\":\n                        \"bbox_radio\",\n                    \"answer\": [{\n                        \"name\": \"second_checklist_answer\",\n                        \"confidence\": 0.5,\n                    }],\n                }],\n            },\n        ]\n    }],\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "######### Free text classification ###########\ntext_prediction = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\", confidence=0.5),\n    )\n]\n\ntext_prediction_ndjson = {\n    \"name\": \"free_text\",\n    \"confidence\": 0.5,\n    \"answer\": \"sample text\",\n}",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 1: Import data rows into Catalog"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# send a sample image as batch to the project\nglobal_key = \"sample-video-2.mp4\"\ntest_img_url = {\n    \"row_data\":\n        \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4\",\n    \"global_key\":\n        global_key,\n}\ndataset = client.create_dataset(\n    name=\"Video prediction demo\",\n    iam_integration=\n    None,  # Removing this argument will default to the organziation's default iam integration\n)\ntask = dataset.create_data_rows([test_img_url])\ntask.wait_till_done()\nprint(\"Errors: \", task.errors)\nprint(\"Failed data rows: \", task.failed_data_rows)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 2: Create/select an Ontology for your model predictions\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched.\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "ontology_builder = lb.OntologyBuilder(\n    tools=[\n        lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n        lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n        lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n        lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n        lb.Tool(\n            tool=lb.Tool.Type.BBOX,\n            name=\"bbox_class\",\n            classifications=[\n                lb.Classification(\n                    class_type=lb.Classification.Type.CHECKLIST,\n                    name=\"checklist_class\",\n                    scope=lb.Classification.Scope.\n                    INDEX,  ## defined scope for frame classifications\n                    options=[\n                        lb.Option(value=\"first_checklist_answer\"),\n                        lb.Option(value=\"second_checklist_answer\"),\n                    ],\n                )\n            ],\n        ),\n    ],\n    classifications=[\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class\",\n            scope=lb.Classification.Scope.\n            INDEX,  ## defined scope for frame classifications\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class\",\n            scope=lb.Classification.Scope.INDEX,\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"nested_radio_question\",\n            options=[\n                lb.Option(\n                    \"first_radio_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.RADIO,\n                            name=\"sub_radio_question\",\n                            options=[lb.Option(\"first_sub_radio_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"nested_checklist_question\",\n            options=[\n                lb.Option(\n                    \"first_checklist_answer\",\n                    options=[\n                        lb.Classification(\n                            class_type=lb.Classification.Type.CHECKLIST,\n                            name=\"sub_checklist_question\",\n                            options=[lb.Option(\"first_sub_checklist_answer\")],\n                        )\n                    ],\n                )\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.RADIO,\n            name=\"radio_class_global\",\n            options=[\n                lb.Option(value=\"first_radio_answer\"),\n                lb.Option(value=\"second_radio_answer\"),\n            ],\n        ),\n        lb.Classification(\n            class_type=lb.Classification.Type.CHECKLIST,\n            name=\"checklist_class_global\",\n            options=[\n                lb.Option(value=\"first_checklist_answer\"),\n                lb.Option(value=\"second_checklist_answer\"),\n            ],\n        ),\n        lb.Classification(class_type=lb.Classification.Type.TEXT,\n                          name=\"free_text\"),\n    ],\n)\n\nontology = client.create_ontology(\n    \"Ontology Video Annotations\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Video,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 3: Create a Model and Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# create Model\nmodel = client.create_model(name=\"video_model_run_\" + str(uuid.uuid4()),\n                            ontology_id=ontology.uid)\n# create Model Run\nmodel_run = model.create_model_run(\"iteration 1\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 4: Send data rows to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "model_run.upsert_data_rows(global_keys=[global_key])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 5. Create the predictions payload\n",
+        "\n",
+        "Create the annotations payload using the snippets of [code here](https://docs.labelbox.com/reference/import-video-annotations).\n",
+        "\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Python Annotation Types"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "label_predictions = []\nannotations_list = [\n    point_prediction,\n    bbox_prediction,\n    polyline_prediction,\n    checklist_prediction,\n    radio_prediction,\n    nested_radio_prediction,\n    nested_checklist_prediction,\n    frame_bbox_with_checklist_subclass_prediction,\n    global_radio_prediction,\n    global_checklist_prediction,\n    text_prediction,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabel_predictions.append(\n    lb_types.Label(data={\"global_key\": global_key},\n                   annotations=flatten_list_annotations))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### NDJSON annotations"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\nlabel_prediction_ndjson = []\n\nfor annotation in [\n        point_prediction_ndjson,\n        bbox_prediction_ndjson,\n        polyline_prediction_ndjson,\n        frame_checklist_classification_prediction_ndjson,\n        frame_radio_classification_prediction_ndjson,\n        nested_radio_prediction_ndjson,\n        nested_checklist_prediction_ndjson,\n        frame_bbox_with_checklist_subclass_prediction_ndjson,\n        global_radio_classification_ndjson,\n        global_checklist_classification_ndjson,\n        text_prediction_ndjson,\n]:\n    annotation.update({\"dataRow\": {\"globalKey\": global_key}})\n    label_prediction_ndjson.append(annotation)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 6. Upload the predictions payload to the Model Run "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Upload the prediction label to the Model Run\nupload_job_prediction = model_run.add_predictions(\n    name=\"prediction_upload_job\" + str(uuid.uuid4()),\n    predictions=label_predictions,\n)\n\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_prediction.errors)\nprint(\"Status of uploads: \", upload_job_prediction.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Step 7: Send annotations to the Model Run \n",
+        "To send annotations to a Model Run, we must first import them into a project, create a label payload and then send them to the Model Run."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.1. Create a labelbox project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Labelbox project\nproject = client.create_project(name=\"video_prediction_demo\",\n                                media_type=lb.MediaType.Video)\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.2. Create a batch to send to the project "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "project.create_batch(\n    \"batch_video_prediction_demo\",  # Each batch in a project must have a unique name\n    global_keys=[global_key\n                ],  # A list of data rows, data row ids or global keys\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.3 Create the annotations payload"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Python Annotation\npoint_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"point_video\",\n        keyframe=True,\n        frame=17,\n        value=lb_types.Point(x=660.134, y=407.926),\n    )\n]\n\n######## Polyline ########\n\n# Python Annotation\npolyline_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=5,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=12,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=20,\n        segment_index=0,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=24,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"line_video_frame\",\n        keyframe=True,\n        frame=45,\n        segment_index=1,\n        value=lb_types.Line(\n            points=[lb_types.Point(x=680, y=100),\n                    lb_types.Point(x=100, y=190)]),\n    ),\n]\n\nradio_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=9,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"radio_class\",\n        frame=15,\n        segment_index=0,\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    ),\n]\n\nchecklist_annotation = [\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=29,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=35,\n        segment_index=0,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=39,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n    lb_types.VideoClassificationAnnotation(\n        name=\"checklist_class\",\n        frame=45,\n        segment_index=1,\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\")\n        ]),\n    ),\n]\n\nglobal_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"radio_class_global\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\")),\n    )\n]\n\nglobal_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"checklist_class_global\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(name=\"first_checklist_answer\"),\n            lb_types.ClassificationAnswer(name=\"second_checklist_answer\"),\n        ]),\n    )\n]\n\nnested_radio_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_radio_question\",\n        value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n            name=\"first_radio_answer\",\n            classifications=[\n                lb_types.ClassificationAnnotation(\n                    name=\"sub_radio_question\",\n                    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(\n                        name=\"first_sub_radio_answer\")),\n                )\n            ],\n        )),\n    )\n]\n\nnested_checklist_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"nested_checklist_question\",\n        value=lb_types.Checklist(answer=[\n            lb_types.ClassificationAnswer(\n                name=\"first_checklist_answer\",\n                classifications=[\n                    lb_types.ClassificationAnnotation(\n                        name=\"sub_checklist_question\",\n                        value=lb_types.Checklist(answer=[\n                            lb_types.ClassificationAnswer(\n                                name=\"first_sub_checklist_answer\")\n                        ]),\n                    )\n                ],\n            )\n        ]),\n    )\n]\n\nbbox_dm2 = {\"top\": 146.0, \"left\": 98.0, \"height\": 382.0, \"width\": 341.0}\nframe_bbox_with_checklist_subclass = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=10,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=11,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(name=\"first_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_class\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm2[\"left\"],\n                                 y=bbox_dm2[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm2[\"left\"] + bbox_dm2[\"width\"],\n                y=bbox_dm2[\"top\"] + bbox_dm2[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n        classifications=[\n            lb_types.ClassificationAnnotation(\n                name=\"checklist_class\",\n                value=lb_types.Checklist(answer=[\n                    lb_types.ClassificationAnswer(\n                        name=\"second_checklist_answer\")\n                ]),\n            )\n        ],\n    ),\n]\n\nbbox_dm = {\"top\": 617, \"left\": 1371, \"height\": 419, \"width\": 505}\nbbox_annotation = [\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=13,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"],\n                                 y=bbox_dm[\"top\"]),  # x = left, y = top\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),  # x= left + width , y = top + height\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=15,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n    lb_types.VideoObjectAnnotation(\n        name=\"bbox_video\",\n        keyframe=True,\n        frame=19,\n        segment_index=0,\n        value=lb_types.Rectangle(\n            start=lb_types.Point(x=bbox_dm[\"left\"], y=bbox_dm[\"top\"]),\n            end=lb_types.Point(\n                x=bbox_dm[\"left\"] + bbox_dm[\"width\"],\n                y=bbox_dm[\"top\"] + bbox_dm[\"height\"],\n            ),\n        ),\n    ),\n]\n\ntext_annotation = [\n    lb_types.ClassificationAnnotation(\n        name=\"free_text\",  # must match your ontology feature's name\n        value=lb_types.Text(answer=\"sample text\"),\n    )\n]",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.4. Create the label object"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create a Label object by identifying the applicable data row in Labelbox and providing a list of annotations\n\nlabels = []\nannotations_list = [\n    checklist_annotation,\n    radio_annotation,\n    bbox_annotation,\n    frame_bbox_with_checklist_subclass,\n    point_annotation,\n    polyline_annotation,\n    global_checklist_annotation,\n    global_radio_annotation,\n    nested_checklist_annotation,\n    nested_radio_annotation,\n    text_annotation,\n]\n\nflatten_list_annotations = [\n    ann for ann_sublist in annotations_list for ann in ann_sublist\n]\n\nlabels.append(\n    lb_types.Label(\n        data=lb_types.VideoData(global_key=global_key),\n        annotations=flatten_list_annotations,\n    ))",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.5. Upload annotations to the project using Label Import"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "upload_job_annotation = lb.LabelImport.create_from_objects(\n    client=client,\n    project_id=project.uid,\n    name=\"video_annotations_import_\" + str(uuid.uuid4()),\n    labels=labels,\n)\n\nupload_job_annotation.wait_until_done()\n# Errors will appear for annotation uploads that failed.\nprint(\"Errors:\", upload_job_annotation.errors)\nprint(\"Status of uploads: \", upload_job_annotation.statuses)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "##### 7.6. Send the annotations to the Model Run"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# get the labels id from the project\nmodel_run.upsert_labels(project_id=project.uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Optional deletions for cleanup \n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/project_setup.ipynb b/examples/project_configuration/project_setup.ipynb
index d35aa8067..2733332b1 100644
--- a/examples/project_configuration/project_setup.ipynb
+++ b/examples/project_configuration/project_setup.ipynb
@@ -1,178 +1,176 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Project Setup\n",
-                "* This notebok describes how to create and configure a project\n",
-                "* This is the same as creating a new project in the editor and going through all of the steps."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
-                "    * An ontology must be set\n",
-                "    * A Batch must be created"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install \"labelbox[data]\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "Provide a valid api key below in order to properly connect to the Labelbox Client."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Dataset\n",
-                "* Create dataset and attach data\n",
-                "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Or Select an Ontology\n",
-                "* Optionally create an ontology or select from an existing one.\n",
-                "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
-                "   "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Project and Setup the Editor\n",
-                "* Setting up a project will add an ontology and will enable labeling to begin\n",
-                "* Creating batches will add all data_rows belonging to the dataset to the queue."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Add data to your projects "
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Review"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/project_setup.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Project Setup\n",
+        "* This notebok describes how to create and configure a project\n",
+        "* This is the same as creating a new project in the editor and going through all of the steps."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* When a user creates a project with client.create_project() the project is not ready for labeling.\n",
+        "    * An ontology must be set\n",
+        "    * A Batch must be created"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install \"labelbox[data]\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your api key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Dataset\n",
+        "* Create dataset and attach data\n",
+        "* More details on attaching data can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/data_rows.ipynb)"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "dataset = client.create_dataset(name=\"project_setup_demo\")\nglobal_keys = [\"id1\", \"id2\", \"id3\", \"id4\"]\n## Example image\nuploads = []\n# Generate data rows\nfor i in range(1, 5):\n    uploads.append({\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_keys[i - 1],\n    })\ntask = dataset.create_data_rows(uploads)\ntask.wait_till_done()\nprint(\"ERRORS: \", task.errors)\nprint(\"RESULT URL: \", task.result_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Or Select an Ontology\n",
+        "* Optionally create an ontology or select from an existing one.\n",
+        "* More details on ontology management can be found [here](https://github.com/Labelbox/labelbox-python/blob/master/examples/basics/ontologies.ipynb)\n",
+        "   "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create\nontology_builder = lb.OntologyBuilder(\n    tools=[lb.Tool(name=\"cat\", tool=lb.Tool.Type.BBOX)])\nontology = client.create_ontology(\"project-setup-demo-ontology\",\n                                  ontology_builder.asdict())\n# Select existing ontology\n# ontology = client.get_ontology(\"<ontology_id>\")\n# ontology = existing_project.ontology()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Project and Setup the Editor\n",
+        "* Setting up a project will add an ontology and will enable labeling to begin\n",
+        "* Creating batches will add all data_rows belonging to the dataset to the queue."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "batch_project = client.create_project(\n    name=\"Project Setup Demo\",\n    quality_mode=QualityMode.\n    Consensus,  # For benchmarks use quality_mode = QualityMode.Benchmark\n    media_type=lb.MediaType.Image,\n)\n\nbatch_project.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Add data to your projects "
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## When creating a batch you can also setup the data rows priority\nbatch = batch_project.create_batch(\n    \"batch-demo-4\",  # Each batch in a project must have a unique name\n    global_keys=global_keys,  # A list of data rows or data row ids\n    priority=5,  # priority between 1(Highest) - 5(lowest)\n    consensus_settings={\n        \"number_of_labels\": 2,\n        \"coverage_percentage\": 1\n    },\n)\nprint(\"Batch: \", batch)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Review"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Note setup_complete will be None if it fails.\nprint(batch_project.setup_complete)\nprint(batch_project.ontology())\nprint([ds.name for ds in batch_project.batches()])",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(f\"https://app.labelbox.com/projects/{batch_project.uid}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# batch_project.delete()\n# dataset_project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/queue_management.ipynb b/examples/project_configuration/queue_management.ipynb
index fd5fad0dc..3ee993dd0 100644
--- a/examples/project_configuration/queue_management.ipynb
+++ b/examples/project_configuration/queue_management.ipynb
@@ -1,208 +1,206 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Queue Management"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* The queue is used to task labelers with specific assets\n",
-                "* We can do any of the following:\n",
-                "    * Set quality settings\n",
-                "    * Set the order of items in the queue\n",
-                "    * Set the percent of assets to review"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q numpy",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nfrom uuid import uuid4\nimport json",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# API Key and Client\n",
-                "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Set up demo project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Create Labelbox  project\n\nproject = client.create_project(\n    name=\"batch-test-project\",\n    description=\"a description\",\n    quality_mode=QualityMode.\n    Benchmark,  # For Consensus projects use quality_mode = QualityMode.Consensus\n    media_type=lb.MediaType.Image,\n)\n\ndataset = client.create_dataset(name=\"queue_dataset\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Create ontology and attach to project"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Add data to your dataset"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "## Example image\nuploads = []\nglobal_keys = []\n# Generate data rows\nfor i in range(1, 5):\n    global_key = str(uuid4())\n    row = {\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_key,\n    }\n    global_keys.append(global_key)\n    uploads.append(row)\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"Errors\", data_rows.errors)\nprint(\"Dataset status: \", data_rows.status)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Attach data to your project and set data row priority"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "######## Create batches\n\n# Create the batch\n\nbatch = project.create_batch(\n    \"batch-demo\",  # Each batch in a project must have a unique name\n    global_keys=global_keys[\n        0:2],  # A list of data rows, data row ids or global keys\n    priority=\n    5,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nbatch2 = project.create_batch(\n    \"batch-demo-2\",  # Each batch in a project must have a unique name\n    # Provide a slice of the data since you can't import assets with global keys that already exist in the project.\n    global_keys=global_keys[\n        2:4],  # A list of data rows, data row ids or global keys\n    priority=\n    1,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nprint(\"Batch: \", batch)\nprint(\"Batch2: \", batch2)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "print(\"View the results here:\",\n      f\"https://app.labelbox.com/projects/{project.uid}\")\n# Click `start labeling` to see the images in order",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Queue Order\n",
-                "- Add priority for each data row\n",
-                "- Update priority for each data row"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "export_task = project.export()\nexport_task.wait_till_done()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Get data rows from project\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(lb.GlobalKey(data_row[\"data_row\"][\"global_key\"])\n                    )  # Convert json data row into data row identifier object\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Get label parameter overrides (LPOs)\nproject_lpos = project.labeling_parameter_overrides()\n\nfor lpo in project_lpos:\n    print(lpo)\n    print(\"Data row:\", lpo.data_row().uid)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Add LPOs\nlpos = []\npriority = 1\nfor data_row in data_rows:\n    lpos.append((data_row, priority))\n    priority += 1\n\nproject.set_labeling_parameter_overrides(lpos)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Update LPOs\nglobal_keys = []\nfor data_row in data_rows:\n    global_keys.append(data_row.key)\n\nproject.update_data_row_labeling_priority(data_rows=lb.GlobalKeys(global_keys),\n                                          priority=1)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Cleanup"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# project.delete()\n# dataset.delete()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/queue_management.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Queue Management"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* The queue is used to task labelers with specific assets\n",
+        "* We can do any of the following:\n",
+        "    * Set quality settings\n",
+        "    * Set the order of items in the queue\n",
+        "    * Set the percent of assets to review"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q numpy",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom labelbox.schema.quality_mode import QualityMode\nfrom uuid import uuid4\nimport json",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# API Key and Client\n",
+        "See the developer guide for [creating an API key](https://docs.labelbox.com/reference/create-api-key)."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = \"\"\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Set up demo project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Create Labelbox  project\n\nproject = client.create_project(\n    name=\"batch-test-project\",\n    description=\"a description\",\n    quality_mode=QualityMode.\n    Benchmark,  # For Consensus projects use quality_mode = QualityMode.Consensus\n    media_type=lb.MediaType.Image,\n)\n\ndataset = client.create_dataset(name=\"queue_dataset\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Create ontology and attach to project"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "classification_features = [\n    lb.Classification(\n        class_type=lb.Classification.Type.CHECKLIST,\n        name=\"Quality Issues\",\n        options=[\n            lb.Option(value=\"blurry\", label=\"Blurry\"),\n            lb.Option(value=\"distorted\", label=\"Distorted\"),\n        ],\n    )\n]\n\nontology_builder = lb.OntologyBuilder(tools=[],\n                                      classifications=classification_features)\n\nontology = client.create_ontology(\n    \"Ontology from new features\",\n    ontology_builder.asdict(),\n    media_type=lb.MediaType.Image,\n)\n\nproject.setup_editor(ontology)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Add data to your dataset"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "## Example image\nuploads = []\nglobal_keys = []\n# Generate data rows\nfor i in range(1, 5):\n    global_key = str(uuid4())\n    row = {\n        \"row_data\":\n            f\"https://storage.googleapis.com/labelbox-datasets/People_Clothing_Segmentation/jpeg_images/IMAGES/img_000{i}.jpeg\",\n        \"global_key\":\n            global_key,\n    }\n    global_keys.append(global_key)\n    uploads.append(row)\n\ndata_rows = dataset.create_data_rows(uploads)\ndata_rows.wait_till_done()\nprint(\"Errors\", data_rows.errors)\nprint(\"Dataset status: \", data_rows.status)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Attach data to your project and set data row priority"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "######## Create batches\n\n# Create the batch\n\nbatch = project.create_batch(\n    \"batch-demo\",  # Each batch in a project must have a unique name\n    global_keys=global_keys[\n        0:2],  # A list of data rows, data row ids or global keys\n    priority=\n    5,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nbatch2 = project.create_batch(\n    \"batch-demo-2\",  # Each batch in a project must have a unique name\n    # Provide a slice of the data since you can't import assets with global keys that already exist in the project.\n    global_keys=global_keys[\n        2:4],  # A list of data rows, data row ids or global keys\n    priority=\n    1,  # priority between 1(Highest) - 5(lowest) 5 is the max priority that can be set\n)\n\nprint(\"Batch: \", batch)\nprint(\"Batch2: \", batch2)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "print(\"View the results here:\",\n      f\"https://app.labelbox.com/projects/{project.uid}\")\n# Click `start labeling` to see the images in order",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Queue Order\n",
+        "- Add priority for each data row\n",
+        "- Update priority for each data row"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "export_task = project.export()\nexport_task.wait_till_done()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Get data rows from project\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(lb.GlobalKey(data_row[\"data_row\"][\"global_key\"])\n                    )  # Convert json data row into data row identifier object\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Get label parameter overrides (LPOs)\nproject_lpos = project.labeling_parameter_overrides()\n\nfor lpo in project_lpos:\n    print(lpo)\n    print(\"Data row:\", lpo.data_row().uid)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Add LPOs\nlpos = []\npriority = 1\nfor data_row in data_rows:\n    lpos.append((data_row, priority))\n    priority += 1\n\nproject.set_labeling_parameter_overrides(lpos)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Update LPOs\nglobal_keys = []\nfor data_row in data_rows:\n    global_keys.append(data_row.key)\n\nproject.update_data_row_labeling_priority(data_rows=lb.GlobalKeys(global_keys),\n                                          priority=1)\n\n# Check results\nproject_lpos = list(project.labeling_parameter_overrides())\n\nfor lpo in project_lpos:\n    print(lpo)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Cleanup"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# project.delete()\n# dataset.delete()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/project_configuration/webhooks.ipynb b/examples/project_configuration/webhooks.ipynb
index 1ed5ec118..36b6f977b 100644
--- a/examples/project_configuration/webhooks.ipynb
+++ b/examples/project_configuration/webhooks.ipynb
@@ -1,212 +1,210 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 5,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": [
-                "# Webhook Configuration"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "Webhooks are supported for the following events:\n",
-                "* label_created\n",
-                "* label_updated\n",
-                "* label_deleted"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n%pip install -q requests\n%pip install -q hmac\n%pip install -q hashlib\n%pip install -q flask\n%pip install -q Werkzeug",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Configure NGROK (Optional)\n",
-                "* If you do not have a public ip address then follow along\n",
-                "\n",
-                "1. Create an account:\n",
-                "    https://dashboard.ngrok.com/get-started/setup\n",
-                "2. Download ngrok and extract the zip file\n",
-                "3. Add ngrok to your path\n",
-                "4. Add the authtoken `ngrok authtoken <token>`"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Configure server to receive requests"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### Test server"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "print(requests.get(\"http://localhost:3001\").text)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Create Webhook"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "- Set ip address if your ip is publicly accessible.\n",
-                "- Otherwise use the following to get ngrok public_url"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Update Webhook"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### List and delete all webhooks"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/project_configuration/webhooks.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "# Webhook Configuration"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "Webhooks are supported for the following events:\n",
+        "* label_created\n",
+        "* label_updated\n",
+        "* label_deleted"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n%pip install -q requests\n%pip install -q hmac\n%pip install -q hashlib\n%pip install -q flask\n%pip install -q Werkzeug",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\nfrom flask import Flask, request\nimport hmac\nimport hashlib\nimport threading\nfrom werkzeug.serving import run_simple\nimport json\nimport requests\nimport os\nfrom getpass import getpass\nimport socket",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# If you don\"t want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n    %pip install colab-env -qU\n    from colab_env import envvar_handler\n\n    envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n    API_KEY = getpass(\"Please enter your labelbox api key\")\n    if COLAB:\n        envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Set this to a project that you want to use for the webhook\nPROJECT_ID = \"\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "client = lb.Client(api_key=API_KEY, endpoint=ENDPOINT)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# We are using port 3001 for this example.\n# Feel free to set to whatever port you want\nWH_PORT = 3001",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Configure NGROK (Optional)\n",
+        "* If you do not have a public ip address then follow along\n",
+        "\n",
+        "1. Create an account:\n",
+        "    https://dashboard.ngrok.com/get-started/setup\n",
+        "2. Download ngrok and extract the zip file\n",
+        "3. Add ngrok to your path\n",
+        "4. Add the authtoken `ngrok authtoken <token>`"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "if not COLAB:\n    os.system(f\"ngrok http {WH_PORT} &\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Configure server to receive requests"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# This can be any secret that matches your webhook config (we will set later)\nsecret = b\"example_secret\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "app = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return \"Hello, World!\"\n\n\n@app.route(\"/webhook-endpoint\", methods=[\"POST\"])\ndef print_webhook_info():\n    payload = request.data\n    computed_signature = hmac.new(secret, msg=payload,\n                                  digestmod=hashlib.sha1).hexdigest()\n    if request.headers[\"X-Hub-Signature\"] != \"sha1=\" + computed_signature:\n        print(\n            \"Error: computed_signature does not match signature provided in the headers\"\n        )\n        return \"Error\", 500, 200\n\n    print(\"=========== New Webhook Delivery ============\")\n    print(\"Delivery ID: %s\" % request.headers[\"X-Labelbox-Id\"])\n    print(\"Event: %s\" % request.headers[\"X-Labelbox-Event\"])\n    print(\"Payload: %s\" %\n          json.dumps(json.loads(payload.decode(\"utf8\")), indent=4))\n    return \"Success\"\n\n\nthread = threading.Thread(target=lambda: run_simple(\"0.0.0.0\", WH_PORT, app))\nthread.start()",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### Test server"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "print(requests.get(\"http://localhost:3001\").text)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Create Webhook"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "- Set ip address if your ip is publicly accessible.\n",
+        "- Otherwise use the following to get ngrok public_url"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "if not COLAB:\n    res = requests.get(\"http://localhost:4040/api/tunnels\")\n    assert (res.status_code == 200\n           ), f\"ngrok probably isn't running. {res.status_code}, {res.text}\"\n    tunnels = res.json()[\"tunnels\"]\n    tunnel = [\n        t for t in tunnels if t[\"config\"][\"addr\"].split(\":\")[-1] == str(WH_PORT)\n    ]\n    tunnel = tunnel[0]  # Should only be one..\n    public_url = tunnel[\"public_url\"]\nelse:\n    public_url = (\n        f\"http://{socket.gethostbyname(socket.getfqdn(socket.gethostname()))}\")\nprint(public_url)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Set project to limit the scope to a single project\nproject = client.get_project(PROJECT_ID)\ntopics = {topic.value for topic in lb.Webhook.Topic}\n# For Global Webhooks (Global = per workspace) project = None\nwebhook = lb.Webhook.create(\n    client,\n    topics=topics,\n    url=public_url,\n    secret=secret.decode(),\n    project=project,\n)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "# Ok so we should be configured assuming everything is setup correctly.\n# Go to the following url and make a new label to see if it works\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Update Webhook"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# url, topics, and status can all be updated\nupdated_url = f\"{public_url}/webhook-endpoint\"\nprint(updated_url)\nwebhook.update(url=updated_url)\n# Go to the following url and try one last time.\n# Any supported action should work (create, delete, or update a label)\nprint(f\"https://app.labelbox.com/projects/{PROJECT_ID}\")",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### List and delete all webhooks"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# DELETE:\nwebhook.update(status=lb.Webhook.Status.INACTIVE.value)\n\n# FETCH ALL WEBHOOKS:\norg = client.get_organization()\nwebhooks = org.webhooks()\n\n# Run this to clear all.\n# WARNING!!! THIS WILL DELETE ALL WEBHOOKS FOR YOUR ORG\n# ONLY RUN THIS IS YOU KNOW WHAT YOU ARE DOING.\n# for webhook in webhooks:\n#    print(webhook)\n#    webhook.update(status = lb.Webhook.Status.INACTIVE.value)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/examples/pyproject.toml b/examples/pyproject.toml
index 5c746f6b2..05b4ed0af 100644
--- a/examples/pyproject.toml
+++ b/examples/pyproject.toml
@@ -2,10 +2,10 @@
 name = "examples"
 version = "0.1.0"
 description = "Labelbox Python Example Notebooks"
-authors = [{ name = "Labelbox", email = "support@labelbox.com" }]
+authors = [{ name = "Labelbox", email = "docs@labelbox.com" }]
 readme = "README.md"
 # Python version matches labelbox SDK
-requires-python = ">=3.8"
+requires-python = ">=3."
 dependencies = []
 
 [project.urls]
@@ -23,11 +23,11 @@ dev-dependencies = [
     "black[jupyter]>=24.4.2",
     "databooks>=1.3.10",
     # higher versions dont support python 3.8
-    "pandas==1.5.3",
+    "pandas>=1.5.3",
 ]
 
 [tool.rye.scripts]
-clean = { chain = ["clean:databooks", "clean:black", "clean:format"] }
+clean = { chain = ["clean:black", "clean:format", "clean:databooks"] }
 "clean:databooks" = 'databooks meta --rm-outs . --yes'
 "clean:black" = 'black --line-length 80 . --quiet'
 "clean:format" = { call = "scripts.format_notebooks" }
diff --git a/examples/scripts/format_notebooks.py b/examples/scripts/format_notebooks.py
index aafa0d6de..36972e96a 100644
--- a/examples/scripts/format_notebooks.py
+++ b/examples/scripts/format_notebooks.py
@@ -15,23 +15,22 @@
     "source": [
         "<td>",
         '   <a target="_blank" href="https://labelbox.com" ><img src="https://labelbox.com/blog/content/images/2021/02/logo-v4.svg" width=256/></a>',
-        "</td>",
+        "</td>\n",
     ],
 }
-
 LINK_CELL = {
     "cell_type": "markdown",
     "id": "cb5611d0",
     "metadata": {},
     "source": [
-        "<td>",
-        '<a href="{colab}" target="_blank"><img',
-        'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>',
-        "</td>",
+        "<td>\n",
+        '<a href="{colab}" target="_blank"><img\n',
+        'src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>\n',
+        "</td>\n",
         "\n",
-        "<td>",
-        '<a href="{github}" target="_blank"><img',
-        'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>',
+        "<td>\n",
+        '<a href="{github}" target="_blank"><img\n',
+        'src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"></a>\n',
         "</td>",
     ],
 }
diff --git a/examples/template.ipynb b/examples/template.ipynb
index 416bb963f..6a052a9b6 100644
--- a/examples/template.ipynb
+++ b/examples/template.ipynb
@@ -1,135 +1,133 @@
 {
-    "nbformat": 4,
-    "nbformat_minor": 2,
-    "metadata": {},
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "db768cda",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
-                "</td>"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cb5611d0",
-            "metadata": {},
-            "source": [
-                "<td>",
-                "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>",
-                "</td>",
-                "\n",
-                "<td>",
-                "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img",
-                "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>",
-                "</td>"
-            ]
-        },
-        {
-            "metadata": {},
-            "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar imports together",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## API key and client\n",
-                "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Main section header\n",
-                "\n",
-                "* Include information on what section discusses"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Sub section header\n",
-                "\n",
-                "* Include information on what sub section discusses\n",
-                "* If parameters are needed specify they need included"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Notebook section with steps\n",
-                "\n",
-                "* For long winded processes step headers should be included"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "### Step \"step number\""
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "* Include information on step"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "#### \"step number.sub step number\"\n",
-                "\n",
-                "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": [
-                "## Clean up\n",
-                "Uncomment and run the cell below to optionally delete Labelbox objects created"
-            ],
-            "cell_type": "markdown"
-        },
-        {
-            "metadata": {},
-            "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
-            "cell_type": "code",
-            "outputs": [],
-            "execution_count": null
-        }
-    ]
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {},
+  "cells": [
+    {
+      "metadata": {},
+      "source": [
+        "<td>",
+        "   <a target=\"_blank\" href=\"https://labelbox.com\" ><img src=\"https://labelbox.com/blog/content/images/2021/02/logo-v4.svg\" width=256/></a>",
+        "</td>\n"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "<td>\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
+        "</td>\n",
+        "\n",
+        "<td>\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/template.ipynb\" target=\"_blank\"><img\n",
+        "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
+        "</td>"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "%pip install -q \"labelbox[data]\"\n# Always install \"labelbox[data]\" over labelbox to ensure dependencies are installed correctly\n# Include other installs in this block with similar format: \"%pip install -q <package name>\"",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": "import labelbox as lb\n\n# Include other imports in this block try grouping similar imports together",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## API key and client\n",
+        "Provide a valid API key below in order to properly connect to the Labelbox client. Please review [Create API key guide](https://docs.labelbox.com/reference/create-api-key) for more information."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# Add your API key\nAPI_KEY = None\n# To get your API key go to: Workspace settings -> API -> Create API Key\nclient = lb.Client(api_key=API_KEY)",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Main section header\n",
+        "\n",
+        "* Include information on what section discusses"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Sub section header\n",
+        "\n",
+        "* Include information on what sub section discusses\n",
+        "* If parameters are needed specify they need included"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "DATASET_ID = None\n# Inline Parameters should be all caps with a default value typically None",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Notebook section with steps\n",
+        "\n",
+        "* For long winded processes step headers should be included"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "### Step \"step number\""
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "* Include information on step"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "#### \"step number.sub step number\"\n",
+        "\n",
+        "* Some steps need sub steps and should be labeled like above. For example, 7.1, 7.2, etc."
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": [
+        "## Clean up\n",
+        "Uncomment and run the cell below to optionally delete Labelbox objects created"
+      ],
+      "cell_type": "markdown"
+    },
+    {
+      "metadata": {},
+      "source": "# batch.delete()\n# project.delete()\n# dataset.delete()\n\n## Include other objects that were created",
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
 }
\ No newline at end of file
diff --git a/libs/labelbox/CHANGELOG.md b/libs/labelbox/CHANGELOG.md
index 5073ffc1b..615d99acd 100644
--- a/libs/labelbox/CHANGELOG.md
+++ b/libs/labelbox/CHANGELOG.md
@@ -1,4 +1,31 @@
 # Changelog
+# Version 3.70.0 (2024-05-20)
+## Added
+* Added chat model evaluation support
+  * client.create_model_config()
+  * ModelConfig project_model_configs()
+  * ModelConfig add_model_config()
+  * ModelConfig delete_project_model_config()
+  * ProjectModelConfig delete()
+  * client.create_model_evaluation_project()
+* Update existing methods to support chat model evaluation project
+  * client.create_ontology()
+  * client.create_ontology_from_feature_schemas()
+* Coco deprecation message
+
+## Fixed
+* Fixed error reporting for client.create_project()
+* Do not retry http 422 errors
+
+## Notebooks
+* Send_to_annotate_from_catalog functionalities outside Foundry
+
+## Fixed in Notebooks
+* Fixed meta notebook
+* Modified queue_management.ipynb to remove some parameters
+* Update_huggingface.ipynb
+* Corrected_HF.ipynb
+
 # Version 3.69.1 (2024-05-01)
 ## Fixed
 * Fixed a bug with certain types of content not being returned as a result of `ExportTask.result` or `ExportTask.errors`
diff --git a/libs/labelbox/Dockerfile b/libs/labelbox/Dockerfile
index a85845d07..3fa56a99b 100644
--- a/libs/labelbox/Dockerfile
+++ b/libs/labelbox/Dockerfile
@@ -5,7 +5,7 @@ ENV LANG="C.UTF-8" \
     LC_ALL="C.UTF-8" \
     PATH="/home/python/.local/bin:/home/python/.rye/shims:$PATH" \
     PIP_NO_CACHE_DIR="false" \
-    RYE_VERSION="0.32.0" \
+    RYE_VERSION="0.34.0" \
     RYE_INSTALL_OPTION="--yes" \
     LABELBOX_TEST_ENVIRON="prod"
 
@@ -31,7 +31,7 @@ RUN groupadd --gid 1000 python && \
 USER 1000
 WORKDIR /home/python/
 
-RUN curl -sSf https://rye-up.com/get | bash -
+RUN curl -sSf https://rye.astral.sh/get | bash -
 
 COPY --chown=python:python . /home/python/labelbox-python/
 WORKDIR /home/python/labelbox-python
@@ -41,4 +41,4 @@ RUN rye config --set-bool behavior.global-python=true && \
     rye pin 3.8 && \
     rye sync
 
-CMD cd libs/labelbox && rye run integration && rye sync -f --features labelbox/data && rye run unit && rye run data
\ No newline at end of file
+CMD cd libs/labelbox && rye run integration && rye sync -f --features labelbox/data && rye run unit && rye run data
diff --git a/libs/labelbox/pyproject.toml b/libs/labelbox/pyproject.toml
index 96da30e11..57d21283c 100644
--- a/libs/labelbox/pyproject.toml
+++ b/libs/labelbox/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "labelbox"
-version = "3.69.1"
+version = "3.70.0"
 description = "Labelbox Python API"
 authors = [
     { name = "Labelbox", email = "engineering@labelbox.com" }
diff --git a/libs/labelbox/src/labelbox/__init__.py b/libs/labelbox/src/labelbox/__init__.py
index c75253e20..85620f851 100644
--- a/libs/labelbox/src/labelbox/__init__.py
+++ b/libs/labelbox/src/labelbox/__init__.py
@@ -1,6 +1,6 @@
 name = "labelbox"
 
-__version__ = "3.69.1"
+__version__ = "3.70.0"
 
 from labelbox.client import Client
 from labelbox.schema.project import Project
diff --git a/libs/labelbox/src/labelbox/schema/project.py b/libs/labelbox/src/labelbox/schema/project.py
index 9a689fd61..dc634c0a6 100644
--- a/libs/labelbox/src/labelbox/schema/project.py
+++ b/libs/labelbox/src/labelbox/schema/project.py
@@ -18,6 +18,7 @@
     LabelboxError,
     ProcessingWaitTimeout,
     ResourceConflict,
+    ResourceNotFoundError
 )
 from labelbox.orm import query
 from labelbox.orm.db_object import DbObject, Deletable, Updateable, experimental
@@ -33,12 +34,14 @@
 from labelbox.schema.identifiable import DataRowIdentifier, GlobalKey, UniqueId
 from labelbox.schema.identifiables import DataRowIdentifiers, UniqueIds
 from labelbox.schema.media_type import MediaType
+from labelbox.schema.model_config import ModelConfig
 from labelbox.schema.project_model_config import ProjectModelConfig
 from labelbox.schema.queue_mode import QueueMode
 from labelbox.schema.resource_tag import ResourceTag
 from labelbox.schema.task import Task
 from labelbox.schema.task_queue import TaskQueue
 from labelbox.schema.ontology_kind import (EditorTaskType, OntologyKind)
+from labelbox.schema.project_overview import ProjectOverview
 
 if TYPE_CHECKING:
     from labelbox import BulkImportRequest
@@ -1270,6 +1273,8 @@ def add_model_config(self, model_config_id: str) -> str:
             "modelConfigId": model_config_id,
         }
         result = self.client.execute(query, params)
+        if not result:
+            raise ResourceNotFoundError(ModelConfig, params)
         return result["createProjectModelConfig"]["projectModelConfigId"]
 
     def delete_project_model_config(self, project_model_config_id: str) -> bool:
@@ -1291,6 +1296,8 @@ def delete_project_model_config(self, project_model_config_id: str) -> bool:
             "id": project_model_config_id,
         }
         result = self.client.execute(query, params)
+        if not result:
+            raise ResourceNotFoundError(ProjectModelConfig, params)
         return result["deleteProjectModelConfig"]["success"]
 
     def set_labeling_parameter_overrides(
@@ -1746,6 +1753,63 @@ def __check_data_rows_have_been_processed(
         return response["queryAllDataRowsHaveBeenProcessed"][
             "allDataRowsHaveBeenProcessed"]
 
+    def get_overview(self) -> ProjectOverview:
+        """ Return the number of data rows per task queue, and issues of a project
+            Equivalent of the Overview tab of a project
+
+                Args:
+                    with_issues: (optional) boolean to include issues in the overview
+                Returns:
+                    Object Project_Overview
+                """
+
+        query = """query ProjectGetOverviewPyApi($projectId: ID!) {
+            project(where: { id: $projectId }) {      
+            workstreamStateCounts {
+                state
+                count
+            }
+            taskQueues {
+                queueType
+                name
+                dataRowCount
+            }
+            issues {
+                totalCount
+            }
+            completedDataRowCount
+            }
+        }
+        """
+
+        # Must use experimental to access "issues"
+        result = self.client.execute(query, {"projectId": self.uid},
+                                     experimental=True)["project"]
+
+        overview = {
+            utils.snake_case(st["state"]): st["count"]
+            for st in result.get("workstreamStateCounts")
+            if st["state"] != "NotInTaskQueue"
+        }
+
+        review_queues = {
+            tq["name"]: tq.get("dataRowCount")
+            for tq in result.get("taskQueues")
+            if tq.get("queueType") == "MANUAL_REVIEW_QUEUE"
+        }
+
+        # Store the total number of data rows in review
+        review_queues["all"] = overview.get("in_review")
+        overview["in_review"] = review_queues
+
+        overview["issues"] = result.get("issues", {}).get("totalCount")
+
+        # Rename keys
+        overview["to_label"] = overview.pop("unlabeled")
+        overview["all_in_data_rows"] = overview.pop("all")
+
+        return ProjectOverview(**overview)
+
 
 class ProjectMember(DbObject):
     user = Relationship.ToOne("User", cache=True)
diff --git a/libs/labelbox/src/labelbox/schema/project_overview.py b/libs/labelbox/src/labelbox/schema/project_overview.py
new file mode 100644
index 000000000..0b517615e
--- /dev/null
+++ b/libs/labelbox/src/labelbox/schema/project_overview.py
@@ -0,0 +1,38 @@
+from typing import Dict
+from labelbox.pydantic_compat import BaseModel
+
+from typing import Dict
+
+class ProjectOverview(BaseModel):
+    """
+    Class that represents a project summary as displayed in the UI, in Annotate, 
+    under the "Overview" tab of a particular project.
+
+    All attributes represent the number of data rows in the corresponding state.
+    The `in_review` attribute is a dictionary where the keys are the queue names
+    and the values are the number of data rows in that queue.
+
+    Attributes:
+        Representing existing fields from the Overview tag (UI names in parentheses):
+
+        to_label (int): The number of data rows that are yet to be labeled (To Label).
+        in_review (Dict[str, int]): A dictionary where the keys are the queue names .
+            and the values are the number of data rows in that queue. (In Review)
+        in_rework (int): The number of data rows that are in the Rework queue (In Rework).
+        skipped (int): The number of data rows that have been skipped (Skipped).
+        done (int): The number of data rows that have been marked as Done (Done).
+        issues (int): The number of data rows with associated issues (Issues).
+        
+        Additional values:
+
+        labeled (int): The number of data rows that have been labeled.
+        all_in_data_rows (int): The total number of data rows in the project.
+    """
+    to_label: int   
+    in_review: Dict[str, int]
+    in_rework: int
+    skipped: int
+    done: int
+    issues: int
+    labeled: int
+    all_in_data_rows: int
\ No newline at end of file
diff --git a/libs/labelbox/tests/integration/test_project_model_config.py b/libs/labelbox/tests/integration/test_project_model_config.py
index e4adf90a2..2979406de 100644
--- a/libs/labelbox/tests/integration/test_project_model_config.py
+++ b/libs/labelbox/tests/integration/test_project_model_config.py
@@ -1,4 +1,5 @@
 import pytest
+from labelbox.exceptions import ResourceNotFoundError
 
 def test_add_single_model_config(configured_project, model_config):
     project_model_config_id = configured_project.add_model_config(model_config.uid)
@@ -23,3 +24,7 @@ def test_add_multiple_model_config(client, rand_gen, configured_project, model_c
 def test_delete_project_model_config(configured_project, model_config):
     assert configured_project.delete_project_model_config(configured_project.add_model_config(model_config.uid))
     assert not len(configured_project.project_model_configs())
+
+def test_delete_nonexistant_project_model_config(configured_project):
+    with pytest.raises(ResourceNotFoundError):
+        configured_project.delete_project_model_config("nonexistant_project_model_config")
diff --git a/libs/labelbox/tests/integration/test_task_queue.py b/libs/labelbox/tests/integration/test_task_queue.py
index 0d25dfa39..1bd721b51 100644
--- a/libs/labelbox/tests/integration/test_task_queue.py
+++ b/libs/labelbox/tests/integration/test_task_queue.py
@@ -1,5 +1,4 @@
 import time
-
 from labelbox import Project
 from labelbox.schema.identifiables import GlobalKeys, UniqueIds
 
@@ -12,6 +11,19 @@ def test_get_task_queue(project: Project):
     assert review_queue
 
 
+def test_get_overview(project: Project):
+    po = project.get_overview()
+
+    assert isinstance(po.to_label, int)
+    assert isinstance(po.in_review, dict)
+    assert isinstance(po.in_rework, int)
+    assert isinstance(po.skipped, int)
+    assert isinstance(po.done, int)
+    assert isinstance(po.issues, int)
+    assert isinstance(po.labeled, int)
+    assert isinstance(po.all_in_data_rows, int)
+
+
 def _validate_moved(project, queue_name, data_row_count):
     timeout_seconds = 30
     sleep_time = 2
diff --git a/requirements-dev.lock b/requirements-dev.lock
index c2b2a6b2b..ad5e7da0c 100644
--- a/requirements-dev.lock
+++ b/requirements-dev.lock
@@ -10,7 +10,7 @@
 -e file:libs/labelbox
 alabaster==0.7.13
     # via sphinx
-annotated-types==0.6.0
+annotated-types==0.7.0
     # via pydantic
 appnope==0.1.4
     # via ipython
@@ -19,7 +19,7 @@ asttokens==2.4.1
 attrs==23.2.0
     # via jsonschema
     # via referencing
-babel==2.14.0
+babel==2.15.0
     # via sphinx
 backcall==0.2.0
     # via ipython
@@ -51,9 +51,9 @@ defusedxml==0.7.1
 docutils==0.20.1
     # via sphinx
     # via sphinx-rtd-theme
-exceptiongroup==1.2.0
+exceptiongroup==1.2.1
     # via pytest
-execnet==2.0.2
+execnet==2.1.1
     # via pytest-xdist
 executing==2.0.1
     # via stack-data
@@ -67,20 +67,20 @@ gitpython==3.1.43
     # via databooks
 google-api-core==2.17.1
     # via labelbox
-google-auth==2.28.2
+google-auth==2.29.0
     # via google-api-core
 googleapis-common-protos==1.63.0
     # via google-api-core
-idna==3.6
+idna==3.7
     # via requests
 imagesize==1.4.1
     # via sphinx
-importlib-metadata==7.0.2
+importlib-metadata==7.1.0
     # via jupyter-client
     # via nbconvert
     # via sphinx
     # via yapf
-importlib-resources==6.3.1
+importlib-resources==6.4.0
     # via jsonschema
     # via jsonschema-specifications
 iniconfig==2.0.0
@@ -92,11 +92,11 @@ jedi==0.19.1
 jinja2==3.1.3
     # via nbconvert
     # via sphinx
-jsonschema==4.21.1
+jsonschema==4.22.0
     # via nbformat
 jsonschema-specifications==2023.12.1
     # via jsonschema
-jupyter-client==8.6.1
+jupyter-client==8.6.2
     # via nbclient
 jupyter-core==5.7.2
     # via jupyter-client
@@ -115,14 +115,14 @@ matplotlib-inline==0.1.7
     # via ipython
 mistune==3.0.2
     # via nbconvert
-mypy==1.9.0
+mypy==1.10.0
 mypy-extensions==1.0.0
     # via black
     # via mypy
 nbclient==0.10.0
     # via nbconvert
-nbconvert==7.16.2
-nbformat==5.10.3
+nbconvert==7.16.4
+nbformat==5.10.4
     # via nbclient
     # via nbconvert
 numpy==1.24.4
@@ -151,7 +151,7 @@ platformdirs==4.2.0
     # via black
     # via jupyter-core
     # via yapf
-pluggy==1.4.0
+pluggy==1.5.0
     # via pytest
 prompt-toolkit==3.0.43
     # via ipython
@@ -165,28 +165,28 @@ pure-eval==0.2.2
 pyasn1==0.5.1
     # via pyasn1-modules
     # via rsa
-pyasn1-modules==0.3.0
+pyasn1-modules==0.4.0
     # via google-auth
 pydantic==2.6.4
     # via databooks
     # via labelbox
-pydantic-core==2.16.3
+pydantic-core==2.18.2
     # via pydantic
 pygments==2.17.2
     # via ipython
     # via nbconvert
     # via rich
     # via sphinx
-pytest==8.1.1
+pytest==8.2.1
     # via pytest-cov
     # via pytest-rerunfailures
     # via pytest-snapshot
     # via pytest-xdist
-pytest-cases==3.8.4
-pytest-cov==4.1.0
+pytest-cases==3.8.5
+pytest-cov==5.0.0
 pytest-rerunfailures==14.0
 pytest-snapshot==0.9.0
-pytest-xdist==3.5.0
+pytest-xdist==3.6.1
 python-dateutil==2.8.2
     # via jupyter-client
     # via labelbox
@@ -196,12 +196,12 @@ pytz==2024.1
     # via pandas
 pyzmq==25.1.2
     # via jupyter-client
-referencing==0.34.0
+referencing==0.35.1
     # via jsonschema
     # via jsonschema-specifications
-regex==2023.12.25
+regex==2024.5.15
     # via toml-cli
-requests==2.31.0
+requests==2.32.2
     # via google-api-core
     # via labelbox
     # via sphinx
@@ -212,6 +212,8 @@ rpds-py==0.18.0
     # via referencing
 rsa==4.9
     # via google-auth
+shellingham==1.5.4
+    # via typer
 six==1.16.0
     # via asttokens
     # via bleach
@@ -245,7 +247,7 @@ stack-data==0.6.3
     # via ipython
 strenum==0.4.15
     # via labelbox
-tinycss2==1.2.1
+tinycss2==1.3.0
     # via nbconvert
 tokenize-rt==5.2.0
     # via black
@@ -257,11 +259,11 @@ tomli==2.0.1
     # via mypy
     # via pytest
     # via yapf
-tomlkit==0.12.4
+tomlkit==0.12.5
     # via toml-cli
 tornado==6.4
     # via jupyter-client
-tqdm==4.66.2
+tqdm==4.66.4
     # via labelbox
 traitlets==5.14.2
     # via ipython
@@ -274,11 +276,11 @@ traitlets==5.14.2
 typer==0.9.0
     # via databooks
     # via toml-cli
-types-pillow==10.2.0.20240311
+types-pillow==10.2.0.20240520
 types-python-dateutil==2.9.0.20240316
-types-requests==2.31.0.20240311
-types-tqdm==4.66.0.20240106
-typing-extensions==4.10.0
+types-requests==2.32.0.20240523
+types-tqdm==4.66.0.20240417
+typing-extensions==4.11.0
     # via annotated-types
     # via black
     # via databooks
@@ -297,6 +299,6 @@ webencodings==0.5.1
     # via bleach
     # via tinycss2
 yapf==0.40.2
-zipp==3.18.1
+zipp==3.18.2
     # via importlib-metadata
     # via importlib-resources
diff --git a/requirements.lock b/requirements.lock
index 3745857dc..770c69cd7 100644
--- a/requirements.lock
+++ b/requirements.lock
@@ -6,13 +6,14 @@
 #   features: []
 #   all-features: false
 #   with-sources: false
+#   generate-hashes: false
 
 -e file:libs/labelbox
 alabaster==0.7.13
     # via sphinx
-annotated-types==0.6.0
+annotated-types==0.7.0
     # via pydantic
-babel==2.14.0
+babel==2.15.0
     # via sphinx
 cachetools==5.3.3
     # via google-auth
@@ -25,43 +26,46 @@ docutils==0.20.1
     # via sphinx-rtd-theme
 geojson==3.1.0
     # via labelbox
-google-api-core==2.17.1
+google-api-core==2.19.0
     # via labelbox
-google-auth==2.28.2
+google-auth==2.29.0
     # via google-api-core
 googleapis-common-protos==1.63.0
     # via google-api-core
-idna==3.6
+idna==3.7
     # via requests
 imagesize==1.4.1
     # via sphinx
 importlib-metadata==7.1.0
     # via sphinx
-jinja2==3.1.3
+jinja2==3.1.4
     # via sphinx
 markupsafe==2.1.5
     # via jinja2
 packaging==24.0
     # via sphinx
+proto-plus==1.23.0
+    # via google-api-core
 protobuf==4.25.3
     # via google-api-core
     # via googleapis-common-protos
-pyasn1==0.5.1
+    # via proto-plus
+pyasn1==0.6.0
     # via pyasn1-modules
     # via rsa
-pyasn1-modules==0.3.0
+pyasn1-modules==0.4.0
     # via google-auth
-pydantic==2.6.4
+pydantic==2.7.1
     # via labelbox
-pydantic-core==2.16.3
+pydantic-core==2.18.2
     # via pydantic
-pygments==2.17.2
+pygments==2.18.0
     # via sphinx
 python-dateutil==2.8.2
     # via labelbox
 pytz==2024.1
     # via babel
-requests==2.31.0
+requests==2.32.2
     # via google-api-core
     # via labelbox
     # via sphinx
@@ -92,13 +96,13 @@ sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
 strenum==0.4.15
     # via labelbox
-tqdm==4.66.2
+tqdm==4.66.4
     # via labelbox
-typing-extensions==4.10.0
+typing-extensions==4.11.0
     # via annotated-types
     # via pydantic
     # via pydantic-core
 urllib3==2.2.1
     # via requests
-zipp==3.18.1
+zipp==3.18.2
     # via importlib-metadata

From 9a0e086080801d659b6ba64aedafe998b6ea4afc Mon Sep 17 00:00:00 2001
From: Gabefire <33893811+Gabefire@users.noreply.github.com>
Date: Thu, 23 May 2024 17:58:20 -0500
Subject: [PATCH 79/80] added generate readme

---
 examples/scripts/generate_readme.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/examples/scripts/generate_readme.py b/examples/scripts/generate_readme.py
index 1c3ff283b..80939acfc 100644
--- a/examples/scripts/generate_readme.py
+++ b/examples/scripts/generate_readme.py
@@ -105,18 +105,14 @@ def make_links_dict(links: str):
         defaultdict[list]: returns dict that is in pandas dataFrame format
     """
     link_dict = defaultdict(list)
-    extra_links = []
     for section in ORDER:
         link_dict[section] = []
     for link in links:
         if link.split("/")[-1] in IGNORE:
             continue
-        if link.split("/")[0] == "extras":
-            extra_links.append(link)
         else:
             split_link = link.split("/")[0]
             link_dict[split_link].append(link)
-    link_dict["Extras"] = extra_links
     return link_dict
 
 

From 5337e09431620b15e91f451908a898b785ba7861 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Thu, 23 May 2024 23:00:06 +0000
Subject: [PATCH 80/80] :memo: README updated

---
 examples/README.md | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/examples/README.md b/examples/README.md
index 2627388c4..6e8faebec 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -331,22 +331,3 @@
     </tr>
   </tbody>
 </table>
-
-<h2>Extras</h2>
-
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: left;">
-      <th style="min-width: 400px;">Notebook</th>
-      <th>Github</th>
-      <th>Google Colab</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td>Classification Confusion Matrix</td>
-      <td><a href="https://github.com/Labelbox/labelbox-python/tree/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="Open In Github"></a></td>
-      <td><a href="https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/extras/classification_confusion_matrix.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td>
-    </tr>
-  </tbody>
-</table>