code: def forward(self, x):
      transform_output = self.highway_transform_activation(
          self.highway_transform(x)
      )
      gate_output = self.highway_gate_activation(self.highway_gate(x))
  
      transformation_part = torch.mul(transform_output, gate_output)
      # TODO: https://github.com/pytorch/pytorch/issues/10747 makes the
      # torch.FloatTensor() expression necessary. Once that gets fixed we
      # can just write 1 - gate_output
      carry_part = torch.mul(
          (torch.FloatTensor([1.0]).type_as(gate_output) - gate_output),
          x,
      )
      return torch.add(transformation_part, carry_part)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from pytorch_translate import common_layers, rnn, utils
  from pytorch_translate.data.dictionary import TAGS
  from torch.nn.utils.rnn import pack_padded_sequence
label_desc: Use image node_id 2 for calling the HighwayLayer obj's underlying member method code with example usage: obj.forward(x) and returns: torch