code: def forward(
      self,  # pylint: disable=arguments-differ
      inputs,
      weight,
      bias,
      state_accumulator,
      memory_accumulator,
      dropout_mask,
      lengths,
      gates,
  ):
      sequence_length, batch_size, input_size = inputs.size()
      tmp_i = inputs.new_empty(batch_size, 6 * self.hidden_size)
      tmp_h = inputs.new_empty(batch_size, 5 * self.hidden_size)
      is_training = 1 if self.train else 0
      highway_lstm_layer.highway_lstm_forward_cuda(
          input_size,  # type: ignore # pylint: disable=no-member
          self.hidden_size,
          batch_size,
          self.num_layers,
          sequence_length,
          inputs,
          lengths,
          state_accumulator,
          memory_accumulator,
          tmp_i,
          tmp_h,
          weight,
          bias,
          dropout_mask,
          gates,
          is_training,
      )
  
      self.save_for_backward(
          inputs,
          lengths,
          weight,
          bias,
          state_accumulator,
          memory_accumulator,
          dropout_mask,
          gates,
      )
  
      # The state_accumulator has shape: (num_layers, sequence_length + 1, batch_size, hidden_size)
      # so for the output, we want the last layer and all but the first timestep, which was the
      # initial state.
      output = state_accumulator[-1, 1:, :, :]
      return output, state_accumulator[:, 1:, :, :]
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/alternating_highway_lstm.py
imports: from __future__ import absolute_import
  import torch
  from torch.autograd import Function
  from torch.nn import Parameter
  from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence, pack_padded_sequence
  from allennlp.nn.initializers import block_orthogonal
  from allennlp.custom_extensions._ext import highway_lstm_layer
label_desc: Use image node_id 2 for calling the _AlternatingHighwayLSTMFunction obj's underlying member method code with example usage: obj.forward(inputs, weight, bias, state_accumulator, memory_accumulator, dropout_mask, lengths, gates) and returns: output,  state_accumulator