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Runs dynamic decoding with a decoder.

Calls initialize() once and step() repeatedly on the decoder object.

decoder A tfa.seq2seq.Decoder or tfa.seq2seq.BaseDecoder instance.
output_time_major Python boolean. Default: False (batch major). If True, outputs are returned as time major tensors (this mode is faster). Otherwise, outputs are returned as batch major tensors (this adds extra time to the computation).
impute_finished Python boolean. If True, then states for batch entries which are marked as finished get copied through and the corresponding outputs get zeroed out. This causes some slowdown at each time step, but ensures that the final state and outputs have the correct values and that backprop ignores time steps that were marked as finished.
maximum_iterations A strictly positive int32 scalar, the maximum allowed number of decoding steps. Default is None (decode until the decoder is fully done).
parallel_iterations Argument passed to tf.while_loop.
swap_memory Argument passed to tf.while_loop.
training Python boolean. Indicates whether the layer should behave in training mode or in inference mode. Only relevant when dropout or recurrent_dropout is used.
scope Optional name scope to use.
enable_tflite_convertible Python boolean. If True, then the variables of TensorArray become of 1-D static shape. Also zero pads in the output tensor will be discarded. Default: False.
**kwargs dict, other keyword arguments for dynamic_decode. It might contain arguments for BaseDecoder to initialize, which takes all tensor inputs during call().

(final_outputs, final_state, final_sequence_lengths).

ValueError if maximum_iterations is provided but is not a scalar.