Keras load_model failure with custom object serialization

Custom Layer Serialization in TensorFlow Keras

When utilizing tf.keras.models.load_model to restore a model containing custom layers or loss functions, TensorFlow requires a custom_objects dictionary to map serialized names back to their Python implementations. While this mechanism handles basic custom classes, consistency issues arise when models are transferred between different Keras versions or environments where the class definitions have evolved.

The primary constraint is the reliance on manual mapping to resolve serialization gaps, which can lead to runtime errors if the internal configuration of the custom object has changed but the class name remains identical.

• Version: TensorFlow 2.x
• Feature: SavedModel serialization

Does TensorFlow provide a mechanism to automate the mapping of custom objects without manually defining the dictionary for every load operation? How does the SavedModel format handle versioning for custom layer configurations to prevent incompatibility during restoration?