class_docstr: An RNN Decoder that is based on a Keras layer.
  
  Concepts used by this interface:
  - `inputs`: (structure of) Tensors and TensorArrays that is passed as input
    to the RNN cell composing the decoder, at each time step.
  - `state`: (structure of) Tensors and TensorArrays that is passed to the
    RNN cell instance as the state.
  - `memory`: tensor that is usually the full output of the encoder, which
    will be used for the attention wrapper for the RNN cell.
  - `finished`: boolean tensor telling whether each sequence in the batch is
    finished.
  - `training`: boolean whether it should behave in training mode or in
    inference mode.
  - `outputs`: instance of `tfa.seq2seq.BasicDecoderOutput`. Result of the decoding, at
    each time step.
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docstr: Called before any decoding iterations.
  
  This methods must compute initial input values and initial state.
  
  Args:
    inputs: (structure of) tensors that contains the input for the
      decoder. In the normal case, it's a tensor with shape
      [batch, timestep, embedding].
    initial_state: (structure of) tensors that contains the initial state
      for the RNN cell.
    **kwargs: Other arguments that are passed in from layer.call()
      method. It could contains item like input `sequence_length`, or
      masking for input.
  
  Returns:
    `(finished, initial_inputs, initial_state)`: initial values of
    'finished' flags, inputs and state.
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code: def initialize(self, inputs, initial_state=None, **kwargs):
      """Called before any decoding iterations.
  
      This methods must compute initial input values and initial state.
  
      Args:
        inputs: (structure of) tensors that contains the input for the
          decoder. In the normal case, it's a tensor with shape
          [batch, timestep, embedding].
        initial_state: (structure of) tensors that contains the initial state
          for the RNN cell.
        **kwargs: Other arguments that are passed in from layer.call()
          method. It could contains item like input `sequence_length`, or
          masking for input.
  
      Returns:
        `(finished, initial_inputs, initial_state)`: initial values of
        'finished' flags, inputs and state.
      """
      raise NotImplementedError
  
imports: import abc
  import tensorflow
  from tensorflow_addons.utils.types import TensorLike
  from typeguard import typechecked
  from typing import Any, Optional, Tuple, Union
  from tensorflow.python.ops import control_flow_util
label_desc: Use image node_id 6 for calling the BaseDecoder obj's underlying member method code with example usage: obj.initialize(inputs, initial_state) without return types