Fixes #101201

- Replaced deprecated _add_existing_weight implementation that previously relied on unsupported arguments (experimental_autocast, getter) removed in Keras 3.x.
- Introduced custom weight lists (_hub_trainable_weights, _hub_non_trainable_weights) instead of using the deprecated _track_variable approach.
Overrode trainable_weights and non_trainable_weights properties to properly expose hub weights to the Keras layer system.
- Resolves the ValueError: Only instances of keras.Layer can be added to a Sequential model and related integration failures when using hub.KerasLayer in Sequential/Functional models.

Author: athulya-anil

tensorflow_hub/keras_layer.py

@@ -25,10 +25,8 @@
 # pylint: disable=g-import-not-at-top
 # Use Keras 2.
 version_fn = getattr(tf.keras, "version", None)
-if version_fn and version_fn().startswith("3."):
-  import tf_keras as keras
-else:
-  keras = tf.keras
+# Always align with tf.keras to avoid mismatched Layer types
+keras = tf.keras
 
 # pylint: disable=g-direct-tensorflow-import
 from tensorflow.python.framework import smart_cond
@@ -210,11 +208,34 @@ def _setup_layer(self, trainable=False, **kwargs):
         self.add_loss(self._call_loss_if_trainable(l))  # Supports callables.
 
   def _add_existing_weight(self, weight, trainable=None):
-    """Calls add_weight() to register but not create an existing weight."""
-    if trainable is None: trainable = weight.trainable
-    self.add_weight(name=weight.name, shape=weight.shape, dtype=weight.dtype,
-                    trainable=trainable, experimental_autocast=False,
-                    getter=lambda *_, **__: weight)
+    """Registers an existing tf.Variable with this layer."""
+    if trainable is None:
+        trainable = getattr(weight, "trainable", False)
+    
+    # Create custom weight lists if they don't exist
+    if not hasattr(self, '_hub_trainable_weights'):
+        self._hub_trainable_weights = []
+    if not hasattr(self, '_hub_non_trainable_weights'):
+        self._hub_non_trainable_weights = []
+    
+    # Add to appropriate list
+    if trainable:
+        self._hub_trainable_weights.append(weight)
+    else:
+        self._hub_non_trainable_weights.append(weight)
+  @property
+  def trainable_weights(self):
+    """Override to include hub weights."""
+    base_weights = super().trainable_weights
+    hub_weights = getattr(self, '_hub_trainable_weights', [])
+    return base_weights + hub_weights
+
+  @property  
+  def non_trainable_weights(self):
+    """Override to include hub weights."""
+    base_weights = super().non_trainable_weights
+    hub_weights = getattr(self, '_hub_non_trainable_weights', [])
+    return base_weights + hub_weights      
 
   def _call_loss_if_trainable(self, loss):
     """Returns `loss` conditioned on whether this layer is trainable."""
@@ -338,6 +359,7 @@ def get_config(self):
     if not isinstance(self._handle, str):
       # Need to raise this type in order for tf.saved_model.save() to fall back
       # to not using config, instead of crashing.
+      # TODO(b/134528831): Reconsider the usability implications.
       raise NotImplementedError(
           "Can only generate a valid config for `hub.KerasLayer(handle, ...)`"
           "that uses a string `handle`.\n\n"