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, with shape (num_layers, batch_size, 2 * hidden_size) and
            (num_layers, batch_size, 2 * cell_size) respectively.

        Returns
        -------
        output_sequence : ``torch.FloatTensor``
            The encoded sequence of shape (num_layers, batch_size, sequence_length, hidden_size)
        final_states: ``Tuple[torch.FloatTensor, torch.FloatTensor]``
            The per-layer final (state, memory) states of the LSTM, with shape
            (num_layers, batch_size, 2 * hidden_size) and  (num_layers, batch_size, 2 * cell_size)
            respectively. The last dimension is duplicated because it contains the state/memory
            for both the forward and backward layers.

def _lstm_forward(self,
                      inputs: PackedSequence,
                      initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None) -> \
            Tuple[torch.Tensor, 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, with shape (num_layers, batch_size, 2 * hidden_size) and
            (num_layers, batch_size, 2 * cell_size) respectively.

        Returns
        -------
        output_sequence : ``torch.FloatTensor``
            The encoded sequence of shape (num_layers, batch_size, sequence_length, hidden_size)
        final_states: ``Tuple[torch.FloatTensor, torch.FloatTensor]``
            The per-layer final (state, memory) states of the LSTM, with shape
            (num_layers, batch_size, 2 * hidden_size) and  (num_layers, batch_size, 2 * cell_size)
            respectively. The last dimension is duplicated because it contains the state/memory
            for both the forward and backward layers.
        """
        if initial_state is None:
            hidden_states: List[Optional[Tuple[torch.Tensor,
                                               torch.Tensor]]] = [None] * len(self.forward_layers)
        elif initial_state[0].size()[0] != len(self.forward_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)))

        inputs, batch_lengths = pad_packed_sequence(inputs, batch_first=True)
        forward_output_sequence = inputs
        backward_output_sequence = inputs

        final_states = []
        sequence_outputs = []
        for layer_index, state in enumerate(hidden_states):
            forward_layer = getattr(self, 'forward_layer_{}'.format(layer_index))
            backward_layer = getattr(self, 'backward_layer_{}'.format(layer_index))

            forward_cache = forward_output_sequence
            backward_cache = backward_output_sequence

            if state is not None:
                forward_hidden_state, backward_hidden_state = state[0].split(self.hidden_size, 2)
                forward_memory_state, backward_memory_state = state[1].split(self.cell_size, 2)
                forward_state = (forward_hidden_state, forward_memory_state)
                backward_state = (backward_hidden_state, backward_memory_state)
            else:
                forward_state = None
                backward_state = None

            forward_output_sequence, forward_state = forward_layer(forward_output_sequence,
                                                                   batch_lengths,
                                                                   forward_state)
            backward_output_sequence, backward_state = backward_layer(backward_output_sequence,
                                                                      batch_lengths,
                                                                      backward_state)
            # Skip connections, just adding the input to the output.
            if layer_index != 0:
                forward_output_sequence += forward_cache
                backward_output_sequence += backward_cache

            sequence_outputs.append(torch.cat([forward_output_sequence,
                                               backward_output_sequence], -1))
            # Append the state tuples in a list, so that we can return
            # the final states for all the layers.
            final_states.append((torch.cat([forward_state[0], backward_state[0]], -1),
                                 torch.cat([forward_state[1], backward_state[1]], -1)))

        stacked_sequence_outputs: torch.FloatTensor = torch.stack(sequence_outputs)
        # Stack the hidden state and memory for each layer into 2 tensors of shape
        # (num_layers, batch_size, hidden_size) and (num_layers, batch_size, cell_size)
        # respectively.
        final_hidden_states, final_memory_states = zip(*final_states)
        final_state_tuple: Tuple[torch.FloatTensor,
                                 torch.FloatTensor] = (torch.cat(final_hidden_states, 0),
                                                       torch.cat(final_memory_states, 0))
        return stacked_sequence_outputs, final_state_tuple