'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].'

@abc.abstractmethod
def unroll_cell(self, decoder_inputs, initial_state, loop_function, cell):

    pass
