Parameters
        ----------
        inputs : ``PackedSequence``, required.
            A batch first ``PackedSequence`` to run the stacked LSTM over.
        initial_state : Tuple[torch.Tensor, torch.Tensor], optional, (default = None)
            A tuple (state, memory) representing the initial hidden state and memory
            of the LSTM. Each tensor has shape (1, batch_size, output_dimension).

        Returns
        -------
        output_sequence : PackedSequence
            The encoded sequence of shape (batch_size, sequence_length, hidden_size)
        final_states: Tuple[torch.Tensor, torch.Tensor]
            The per-layer final (state, memory) states of the LSTM, each with shape
            (num_layers, batch_size, hidden_size).

def forward(self,  # pylint: disable=arguments-differ
                inputs: PackedSequence,
                initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None) -> \
            Tuple[Union[torch.Tensor, PackedSequence], Tuple[torch.Tensor, torch.Tensor]]:
        """
        Parameters
        ----------
        inputs : ``PackedSequence``, required.
            A batch first ``PackedSequence`` to run the stacked LSTM over.
        initial_state : Tuple[torch.Tensor, torch.Tensor], optional, (default = None)
            A tuple (state, memory) representing the initial hidden state and memory
            of the LSTM. Each tensor has shape (1, batch_size, output_dimension).

        Returns
        -------
        output_sequence : PackedSequence
            The encoded sequence of shape (batch_size, sequence_length, hidden_size)
        final_states: Tuple[torch.Tensor, torch.Tensor]
            The per-layer final (state, memory) states of the LSTM, each with shape
            (num_layers, batch_size, hidden_size).
        """
        if not initial_state:
            hidden_states = [None] * len(self.lstm_layers)
        elif initial_state[0].size()[0] != len(self.lstm_layers):
            raise ConfigurationError("Initial states were passed to forward() but the number of "
                                     "initial states does not match the number of layers.")
        else:
            hidden_states = list(zip(initial_state[0].split(1, 0),
                                     initial_state[1].split(1, 0)))

        output_sequence = inputs
        final_states = []
        for i, state in enumerate(hidden_states):
            layer = getattr(self, 'layer_{}'.format(i))
            # The state is duplicated to mirror the Pytorch API for LSTMs.
            output_sequence, final_state = layer(output_sequence, state)
            final_states.append(final_state)

        final_hidden_state, final_cell_state = tuple(torch.cat(state_list, 0) for state_list in zip(*final_states))
        return output_sequence, (final_hidden_state, final_cell_state)