(self, decoder_inputs, initial_state, loop_function, cell)
Unrolls an RNN cell for all inputs.

    This is a placeholder to call some RNN decoder. It has a similar to
    tf.seq2seq.rnn_decode interface.

    Args:
      decoder_inputs: A list of 2D Tensors* [batch_size x input_size]. In fact,
        most of existing decoders in presence of a loop_function use only the
        first element to determine batch_size and length of the list to
        determine number of steps.
      initial_state: 2D Tensor with shape [batch_size x cell.state_size].
      loop_function: function will be applied to the i-th output in order to
        generate the i+1-st input (see self.get_input).
      cell: rnn_cell.RNNCell defining the cell function and size.

    Returns:
      A tuple of the form (outputs, state), where:
        outputs: A list of character logits of the same length as
        decoder_inputs of 2D Tensors with shape [batch_size x num_characters].
        state: The state of each cell at the final time-step.
          It is a 2D Tensor of shape [batch_size x cell.state_size].
Unrolls an RNN cell for all inputs.
Unrolls an RNN cell for all inputs .
def unroll_cell(self, decoder_inputs, initial_state, loop_function, cell):
    """Unrolls an RNN cell for all inputs.

    This is a placeholder to call some RNN decoder. It has a similar to
    tf.seq2seq.rnn_decode interface.

    Args:
      decoder_inputs: A list of 2D Tensors* [batch_size x input_size]. In fact,
        most of existing decoders in presence of a loop_function use only the
        first element to determine batch_size and length of the list to
        determine number of steps.
      initial_state: 2D Tensor with shape [batch_size x cell.state_size].
      loop_function: function will be applied to the i-th output in order to
        generate the i+1-st input (see self.get_input).
      cell: rnn_cell.RNNCell defining the cell function and size.

    Returns:
      A tuple of the form (outputs, state), where:
        outputs: A list of character logits of the same length as
        decoder_inputs of 2D Tensors with shape [batch_size x num_characters].
        state: The state of each cell at the final time-step.
          It is a 2D Tensor of shape [batch_size x cell.state_size].
    """
    pass
def unroll_cell ( self , decoder_inputs , initial_state , loop_function , cell ) : pass