class_docstr: An RNN Decoder abstract interface object.
  
  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.
  - `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: Describes whether the Decoder keeps track of finished states.
  
  Most decoders will emit a true/false `finished` value independently
  at each time step.  In this case, the `tfa.seq2seq.dynamic_decode` function keeps
  track of which batch entries are already finished, and performs a
  logical OR to insert new batches to the finished set.
  
  Some decoders, however, shuffle batches / beams between time steps and
  `tfa.seq2seq.dynamic_decode` will mix up the finished state across these entries
  because it does not track the reshuffle across time steps. In this
  case, it is up to the decoder to declare that it will keep track of its
  own finished state by setting this property to `True`.
  
  Returns:
    Python bool.
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code: def tracks_own_finished(self):
      """Describes whether the Decoder keeps track of finished states.
  
      Most decoders will emit a true/false `finished` value independently
      at each time step.  In this case, the `tfa.seq2seq.dynamic_decode` function keeps
      track of which batch entries are already finished, and performs a
      logical OR to insert new batches to the finished set.
  
      Some decoders, however, shuffle batches / beams between time steps and
      `tfa.seq2seq.dynamic_decode` will mix up the finished state across these entries
      because it does not track the reshuffle across time steps. In this
      case, it is up to the decoder to declare that it will keep track of its
      own finished state by setting this property to `True`.
  
      Returns:
        Python bool.
      """
      return False
  
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 7 for calling the Decoder obj's underlying member method code with example usage: obj.tracks_own_finished() and returns: False