code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      # number of tokens is variable
      if not self.embedded_tokens:
          # x of shape [batch_size, number of tokens]
          input = self.embedding(x)
          input = input.permute(0, 2, 1)
      else:
          # x of shape [batch_size, number of tokens, embedding_size]
          input = x.permute(0, 2, 1)
  
      # input of [batch size, embedding size, number of tokens]
      outputs = []
      for i in range(len(self.kernel_sizes_cnn)):
          # convolutional input should be of shape [batch_size, embedding_size, number of tokens]
          output = getattr(self, "conv_" + str(i))(input)
          output = getattr(self, "bn_" + str(i))(output)
          output = getattr(self, "relu_" + str(i))(output)
          output = getattr(self, "pool_" + str(i))(output)
          output = output.squeeze(-1)
          # output of shape [batch_size, out]
          outputs.append(output)
  
      output = torch.cat(outputs, dim=-1)
      output = self.dropout(output)
      output = self.dense(output)
      output = self.relu_dense(output)
      output = self.dropout(output)
      output = self.final_dense(output)
      return output
  
imports: from typing import List, Union, Optional
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ShallowAndWideCnn obj's underlying member method code with example usage: obj.forward(x) and returns: output