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.
class_docstr_tok: ["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","."]
code: def __init__(self, config: Config, embed_dim: int) -> None:
      super().__init__(config)
  
      self.fwd_bwd_ctxt_len = config.fwd_bwd_context_len
      self.surr_ctxt_len = config.surrounding_context_len
      self.ctxt_pad_len = max(self.fwd_bwd_ctxt_len, self.surr_ctxt_len)
  
      out_channels = config.cnn.kernel_num
      kernel_sizes = config.cnn.kernel_sizes
      self.backward_conv = ContextualWordConvolution(
          embed_dim, out_channels, kernel_sizes
      )
      self.forward_conv = ContextualWordConvolution(
          embed_dim, out_channels, kernel_sizes
      )
      self.surround_conv = ContextualWordConvolution(
          embed_dim, out_channels, kernel_sizes
      )
  
      self.ctxt_pad = nn.ConstantPad1d(
          (self.ctxt_pad_len, self.ctxt_pad_len), 0
      )
  
      self.representation_dim = 3 * len(kernel_sizes) * out_channels
      log_class_usage(__class__)
  
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 1 to create a new BSeqCNNRepresentation object from inherited base classes: RepresentationBase with example: obj = BSeqCNNRepresentation(config, embed_dim)