code: def forward(self, words: torch.Tensor):
      words = words.transpose(1, 2)
      conv_outs = [F.relu(conv(words)) for conv in self.convs]
      mp_outs = [self.max_pool(co).squeeze(2) for co in conv_outs]
      return self.fc(torch.cat(mp_outs, 1))
  
file_path: pytext/pytext/models/representations/biseqcnn.py
imports: from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config.module_config import CNNParams
  from pytext.models.representations.representation_base import RepresentationBase
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 2 for calling the ContextualWordConvolution obj's underlying member method code with example usage: obj.forward(words) and returns: self