code: def forward(self, dense_x, tokens, dense_padding_mask):
      result = {}
  
      if self.batch_norm:
          dense_x = self.bn_padded_data(dense_x, dense_padding_mask)
      if self.residual:
          inter_x = self.in_proj(self.dropout(dense_x))
          dense_x = dense_x + inter_x
          result["inter_x"] = inter_x
  
      dense_x = self.dropout(dense_x)
  
      dense_x = self.proj(dense_x)
      if self.stride > 1:
          dense_padding_mask = dense_padding_mask[:, :: self.stride]
  
      if dense_padding_mask.size(1) != dense_x.size(1):
          new_padding = dense_padding_mask.new_zeros(dense_x.shape[:-1])
          diff = new_padding.size(1) - dense_padding_mask.size(1)
  
          if diff > 0:
              new_padding[:, diff:] = dense_padding_mask
          else:
              assert diff < 0
              new_padding = dense_padding_mask[:, :diff]
  
          dense_padding_mask = new_padding
  
      token_x = None
      if tokens is not None:
          token_x = dense_x.new_zeros(tokens.numel(), self.output_dim)
          token_x.scatter_(1, tokens.view(-1, 1).long(), 1)
          token_x = token_x.view(tokens.shape + (self.output_dim,))
  
      result["dense_x"] = dense_x
      result["token_x"] = token_x
      result["dense_padding_mask"] = dense_padding_mask
  
      return result
  
imports: from dataclasses import dataclass
  from enum import Enum, auto
  import math
  import numpy
  from typing import Tuple, List, Optional, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import autograd
  from fairseq import checkpoint_utils, utils
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.modules import SamePad, TransposeLast
label_desc: Use image node_id 2 for calling the Generator obj's underlying member method code with example usage: obj.forward(dense_x, tokens, dense_padding_mask) and returns: result