code: def __init__(
      self, in_channels: int, out_channels: int, kernel_sizes: List[int]
  ):
      super().__init__()
      self.max_pool = nn.AdaptiveMaxPool1d(1)
      self.convs = nn.ModuleList(
          [
              nn.Conv1d(in_channels, out_channels, k, padding=k - 1)
              for k in kernel_sizes
          ]
      )
      token_rep_size = len(kernel_sizes) * out_channels
      self.fc = nn.Linear(token_rep_size, token_rep_size)
      log_class_usage
  
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 ContextualWordConvolution object from inherited base classes: nn with example: obj = ContextualWordConvolution(in_channels, out_channels, kernel_sizes)