class_docstr: `DeepCNNRepresentation` implements CNN representation layer
  preceded by a dropout layer. CNN representation layer is based on the encoder
  in the architecture proposed by Gehring et. al. in Convolutional Sequence to
  Sequence Learning.
  
  Args:
      config (Config): Configuration object of type DeepCNNRepresentation.Config.
      embed_dim (int): The number of expected features in the input.
class_docstr_tok: ["`","DeepCNNRepresentation","`","implements","CNN","representation","layer","preceded","by","a","dropout","layer",".","CNN","representation","layer","is","based","on","the","encoder","in","the","architecture","proposed","by","Gehring","et",".","al",".","in","Convolutional","Sequence","to","Sequence","Learning",".","Args",":","config","(","Config",")",":","Configuration","object","of","type","DeepCNNRepresentation.Config",".","embed_dim","(","int",")",":","The","number","of","expected","features","in","the","input","."]
code: def forward(self, inputs: torch.Tensor, *args) -> torch.Tensor:
      inputs = self.dropout(inputs)
      # bsz * seq_len * embed_dim -> bsz * embed_dim * seq_len
      words = inputs.permute(0, 2, 1)
      convs_keys = self.convs.keys()
      projections_keys = self.projections.keys()
      # Extra verbosity is due to jit.script.
      for k in convs_keys:
          conv = self.convs[k]
          if k not in projections_keys:
              residual = words
          else:
              proj = self.projections[k]
              tranposed = words.permute(0, 2, 1)
              residual = proj(tranposed).permute(0, 2, 1)
          words = conv(words)
          words = self.activation(words)
          words = (words + residual) * math.sqrt(0.5)
      words = words.permute(0, 2, 1)
      return pool(self.pooling_type, words)
  
imports: import math
  import torch
  import torch.nn
  from pytext.config.module_config import Activation, CNNParams, PoolingType
  from pytext.models.representations.representation_base import RepresentationBase
  from pytext.optimizer import get_activation
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 2 for calling the DeepCNNRepresentation obj's underlying member method code with example usage: obj.forward(inputs) and returns: pool