class_docstr: This class is an implementation of the paper https://arxiv.org/pdf/1606.07783.
  It is a bidirectional CNN model that captures context like RNNs do.
  
  The module expects that input mini-batch is already padded.
  
  TODO: Current implementation has a single layer conv-maxpool operation.
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code: def forward(self, inputs: torch.Tensor, *args) -> torch.Tensor:
      inputs = self._preprocess_inputs(inputs)
      word_reps = []
      for i in range(
          self.ctxt_pad_len, inputs.size()[1] - self.ctxt_pad_len
      ):
          word_rep = self._word_forward(inputs, i).unsqueeze(1)
          word_reps.append(word_rep)
  
      sent_rep = torch.cat(word_reps, dim=1)
  
      return sent_rep
  
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 BSeqCNNRepresentation obj's underlying member method code with example usage: obj.forward(inputs) and returns: sent_rep