code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      # x = (batch_size, dim, timesteps)
      # outputs: (batch_size, dim, timesteps) = f(x) + x
      out = x
      timesteps = x.size(2)
      for k, convolution in enumerate(self._convolutions):
          if k == 0 and self.dropout > 0:
              # apply dropout to the input
              out = torch.nn.functional.dropout(
                  out, self.dropout, self.training
              )
  
          conv_out = convolution(out)
  
          # remove the padding indices
          # x is padded by convolution width - 1 in each direction
          dims_to_remove = conv_out.size(2) - timesteps
          if dims_to_remove > 0:
              if self._direction == "forward":
                  # remove from the end of the sequence
                  conv_out = conv_out.narrow(2, 0, timesteps)
              else:
                  # remove from the beginning of the sequence
                  conv_out = conv_out.narrow(
                      2, dims_to_remove, timesteps
                  )
  
          out = torch.nn.functional.glu(conv_out, dim=1)
  
      # see Convolutional Sequence to Sequence Learning
      return (out + x) * math.sqrt(0.5)
  
file_path: allennlp/allennlp/modules/seq2seq_encoders/gated_cnn_encoder.py
imports: from typing import Sequence, List
  import math
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.modules.seq2seq_encoders.seq2seq_encoder import Seq2SeqEncoder
label_desc: Use image node_id 2 for calling the ResidualBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown