Document behavior of `save` versus `save_weights`

[mhucka]

In issue #56, there is a request for improving the docs:

Would like to +1 this, and ask that it is made clear(er) in the documentation that although save won't work, save_weights will.

This is because the error given when calling save doesn't indicate that this is the problem; in my case, I got

File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 98, in encode_structure

    return self._map_structure(nested_structure, self._get_encoders())

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 82, in _map_structure

    return do(pyobj, recursion_fn)

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 237, in do_encode

    pair.value.CopyFrom(encode_fn(named_tuple_value._asdict()[key]))

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 82, in _map_structure

    return do(pyobj, recursion_fn)

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 206, in do_encode

    encoded_dict.dict_value.fields[key].CopyFrom(encode_fn(value))

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 83, in _map_structure

    raise NotEncodableError(

tensorflow.python.saved_model.nested_structure_coder.NotEncodableError: No encoder for object [<tensorflow.python.keras.initializers.initializers_v2.RandomUniform object at 0x7f241a041880>] of type [<class 'tensorflow.python.keras.initializers.initializers_v2.RandomUniform'>].

Only when explicitly calling the model's get_config() method did I get

File "learn_params.py", line 194, in <module>

    print(model.get_config())

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 598, in get_config

    return copy.deepcopy(get_network_config(self))

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 1278, in get_network_config

    layer_config = serialize_layer_fn(layer)

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 250, in serialize_keras_object

    raise e

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 245, in serialize_keras_object

    config = instance.get_config()

  File "(virtual environment)/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 676, in get_config

    raise NotImplementedError('Layer %s has arguments in `__init__` and '

NotImplementedError: Layer ControlledPQC has arguments in `__init__` and therefore must override `get_config`.

Originally posted by @mikeevmm in #56

[Aniketsy]

@mhucka I’m planning to add get_config() and from_config() to ControlledPQC so it can be properly serialized/deserialized with model.save(). For non-trivial arguments (like Cirq circuits and operators), I’ll handle them with appropriate serialization or placeholders, and document any limitations. Does this sound like the right approach?
Any corrections or suggestions would be greatly appreciated. Thanks!