(self, time, inputs, state)
Called per step of decoding (but only once for dynamic decoding).

    Args:
      time: Scalar `int32` tensor. Current step number.
      inputs: RNNCell input (possibly nested tuple of) tensor[s] for this time
        step.
      state: RNNCell state (possibly nested tuple of) tensor[s] from previous
        time step.

    Returns:
      `(outputs, next_state, next_inputs, finished)`: `outputs` is an object
      containing the decoder output, `next_state` is a (structure of) state
      tensors and TensorArrays, `next_inputs` is the tensor that should be used
      as input for the next step, `finished` is a boolean tensor telling whether
      the sequence is complete, for each sequence in the batch.
Called per step of decoding (but only once for dynamic decoding).
Called per step of decoding ( but only once for dynamic decoding ) .
def step(self, time, inputs, state):
    """Called per step of decoding (but only once for dynamic decoding).

    Args:
      time: Scalar `int32` tensor. Current step number.
      inputs: RNNCell input (possibly nested tuple of) tensor[s] for this time
        step.
      state: RNNCell state (possibly nested tuple of) tensor[s] from previous
        time step.

    Returns:
      `(outputs, next_state, next_inputs, finished)`: `outputs` is an object
      containing the decoder output, `next_state` is a (structure of) state
      tensors and TensorArrays, `next_inputs` is the tensor that should be used
      as input for the next step, `finished` is a boolean tensor telling whether
      the sequence is complete, for each sequence in the batch.
    """
    raise NotImplementedError
def step ( self , time , inputs , state ) : raise NotImplementedError