def forward(self,  # pylint: disable=arguments-differ
                inputs: torch.FloatTensor,
                batch_lengths: List[int],
                initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
        """
        Parameters
        ----------
        inputs : ``torch.FloatTensor``, required.
            A tensor of shape (batch_size, num_timesteps, input_size)
            to apply the LSTM over.
        batch_lengths : ``List[int]``, required.
            A list of length batch_size containing the lengths of the sequences in batch.
        initial_state : ``Tuple[torch.Tensor, torch.Tensor]``, optional, (default = None)
            A tuple (state, memory) representing the initial hidden state and memory
            of the LSTM. The ``state`` has shape (1, batch_size, hidden_size) and the
            ``memory`` has shape (1, batch_size, cell_size).

        Returns
        -------
        output_accumulator : ``torch.FloatTensor``
            The outputs of the LSTM for each timestep. A tensor of shape
            (batch_size, max_timesteps, hidden_size) where for a given batch
            element, all outputs past the sequence length for that batch are
            zero tensors.
        final_state : ``Tuple[``torch.FloatTensor, torch.FloatTensor]``
            A tuple (state, memory) representing the initial hidden state and memory
            of the LSTM. The ``state`` has shape (1, batch_size, hidden_size) and the
            ``memory`` has shape (1, batch_size, cell_size).
        """
        batch_size = inputs.size()[0]
        total_timesteps = inputs.size()[1]

        output_accumulator = inputs.new_zeros(batch_size, total_timesteps, self.hidden_size)

        if initial_state is None:
            full_batch_previous_memory = inputs.new_zeros(batch_size, self.cell_size)
            full_batch_previous_state = inputs.new_zeros(batch_size, self.hidden_size)
        else:
            full_batch_previous_state = initial_state[0].squeeze(0)
            full_batch_previous_memory = initial_state[1].squeeze(0)

        current_length_index = batch_size - 1 if self.go_forward else 0
        if self.recurrent_dropout_probability > 0.0 and self.training:
            dropout_mask = get_dropout_mask(self.recurrent_dropout_probability,
                                            full_batch_previous_state)
        else:
            dropout_mask = None

        for timestep in range(total_timesteps):
            # The index depends on which end we start.
            index = timestep if self.go_forward else total_timesteps - timestep - 1

            # What we are doing here is finding the index into the batch dimension
            # which we need to use for this timestep, because the sequences have
            # variable length, so once the index is greater than the length of this
            # particular batch sequence, we no longer need to do the computation for
            # this sequence. The key thing to recognise here is that the batch inputs
            # must be _ordered_ by length from longest (first in batch) to shortest
            # (last) so initially, we are going forwards with every sequence and as we
            # pass the index at which the shortest elements of the batch finish,
            # we stop picking them up for the computation.
            if self.go_forward:
                while batch_lengths[current_length_index] <= index:
                    current_length_index -= 1
            # If we're going backwards, we are _picking up_ more indices.
            else:
                # First conditional: Are we already at the maximum number of elements in the batch?
                # Second conditional: Does the next shortest sequence beyond the current batch
                # index require computation use this timestep?
                while current_length_index < (len(batch_lengths) - 1) and \
                                batch_lengths[current_length_index + 1] > index:
                    current_length_index += 1

            # Actually get the slices of the batch which we
            # need for the computation at this timestep.
            # shape (batch_size, cell_size)
            previous_memory = full_batch_previous_memory[0: current_length_index + 1].clone()
            # Shape (batch_size, hidden_size)
            previous_state = full_batch_previous_state[0: current_length_index + 1].clone()
            # Shape (batch_size, input_size)
            timestep_input = inputs[0: current_length_index + 1, index]

            # Do the projections for all the gates all at once.
            # Both have shape (batch_size, 4 * cell_size)
            projected_input = self.input_linearity(timestep_input)
            projected_state = self.state_linearity(previous_state)

            # Main LSTM equations using relevant chunks of the big linear
            # projections of the hidden state and inputs.
            input_gate = torch.sigmoid(projected_input[:, (0 * self.cell_size):(1 * self.cell_size)] +
                                       projected_state[:, (0 * self.cell_size):(1 * self.cell_size)])
            forget_gate = torch.sigmoid(projected_input[:, (1 * self.cell_size):(2 * self.cell_size)] +
                                        projected_state[:, (1 * self.cell_size):(2 * self.cell_size)])
            memory_init = torch.tanh(projected_input[:, (2 * self.cell_size):(3 * self.cell_size)] +
                                     projected_state[:, (2 * self.cell_size):(3 * self.cell_size)])
            output_gate = torch.sigmoid(projected_input[:, (3 * self.cell_size):(4 * self.cell_size)] +
                                        projected_state[:, (3 * self.cell_size):(4 * self.cell_size)])
            memory = input_gate * memory_init + forget_gate * previous_memory

            # Here is the non-standard part of this LSTM cell; first, we clip the
            # memory cell, then we project the output of the timestep to a smaller size
            # and again clip it.

            if self.memory_cell_clip_value:
                # pylint: disable=invalid-unary-operand-type
                memory = torch.clamp(memory, -self.memory_cell_clip_value, self.memory_cell_clip_value)

            # shape (current_length_index, cell_size)
            pre_projection_timestep_output = output_gate * torch.tanh(memory)

            # shape (current_length_index, hidden_size)
            timestep_output = self.state_projection(pre_projection_timestep_output)
            if self.state_projection_clip_value:
                # pylint: disable=invalid-unary-operand-type
                timestep_output = torch.clamp(timestep_output,
                                              -self.state_projection_clip_value,
                                              self.state_projection_clip_value)

            # Only do dropout if the dropout prob is > 0.0 and we are in training mode.
            if dropout_mask is not None:
                timestep_output = timestep_output * dropout_mask[0: current_length_index + 1]

            # We've been doing computation with less than the full batch, so here we create a new
            # variable for the the whole batch at this timestep and insert the result for the
            # relevant elements of the batch into it.
            full_batch_previous_memory = full_batch_previous_memory.clone()
            full_batch_previous_state = full_batch_previous_state.clone()
            full_batch_previous_memory[0:current_length_index + 1] = memory
            full_batch_previous_state[0:current_length_index + 1] = timestep_output
            output_accumulator[0:current_length_index + 1, index] = timestep_output

        # Mimic the pytorch API by returning state in the following shape:
        # (num_layers * num_directions, batch_size, ...). As this
        # LSTM cell cannot be stacked, the first dimension here is just 1.
        final_state = (full_batch_previous_state.unsqueeze(0),
                       full_batch_previous_memory.unsqueeze(0))

        return output_accumulator, final_state