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 _preprocess_inputs(self, inputs: torch.Tensor) -> torch.Tensor:
      # Transpose to make sentence dimension as last dimension of tensor,
      # so that we can add padding to the sentences. (N, W, D) -> (N, D, W)
      inputs = inputs.transpose(1, 2)
  
      # We need to pad when there isn't enough backward and forward context.
      # Pad sents_emb with max context length so that on-demand padding is not needed
      # to take care of boundary cases, e.g., backward context for the first word.
      inputs = self.ctxt_pad(inputs.unsqueeze(1)).squeeze(1)
  
      # Transpose the tensor back to (N, W, D)
      return inputs.transpose(1, 2)
  
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 3 for calling the BSeqCNNRepresentation obj's underlying member method code with example usage: obj._preprocess_inputs(inputs) and returns: inputs