ValueError: Unknown layer when loading H5 models with custom components

Keras utilizes the load_model API to reconstruct model architecture and weights from persisted files. When using the HDF5 (.h5) format, the serialization process stores the configuration of layers, but it does not embed the actual Python code for custom-defined layers.

A challenge arises when a model containing non-standard layers is shared across different environments or scripts where the original class definitions are not present in the global namespace. Without a mechanism to map the serialized layer name back to the implementation, the loading process fails.

Given the requirement for the custom_objects dictionary during restoration, what is the most scalable way to manage these mappings for complex models with numerous custom layers? Is there a documented method to automate this registration process to avoid manual dictionary construction for every load_model call?