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You would need to implement a julia function that perform keras.src.saving.pickle_utils.deserialize_model_from_bytecode on UInt8[0x50, 0x4b, 0x03, 0x04, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00 … 0xb2, 0x00, 0x00, 0x00, 0xc6, 0xa5, 0xee, 0x02, 0x00, 0x00] and register that to the unpickler (TorchPickler().mt["keras.src.saving.pickle_utils.deserialize_model_from_bytecode"] = julia_impl_deserialize_model_from_bytecode). Though I'm not sure what exactly keras.src.saving.pickle_utils.deserialize_model_from_bytecodedo.
The bytes seem to be raw bytes of a zipfile, which contains the configurations and weights stored in either H5 or Npz. The load_model function would directly construct the keras model from the configurations and weights.
I'm guessing this means some keras support needs to be added.
If so, can you provide guidance. I'm happy to PR
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