code: def forward(self, x, padding_mask):
      x = x.transpose(1, 2)  # BTC -> BCT
      x = self.net(x)
      x = x.transpose(1, 2)
      x_sz = x.size(1)
      if (
          padding_mask is not None
          and padding_mask.any()
          and padding_mask.dim() > 1
      ):
          padding_mask = padding_mask[:, : x.size(1)]
          x[padding_mask] = float("-inf") if self.max_pool else 0
          x_sz = x_sz - padding_mask.sum(dim=-1)
      x = x.squeeze(-1)
      if self.max_pool:
          x, _ = x.max(dim=-1)
      else:
          x = x.sum(dim=-1)
          x = x / x_sz
      return x
  
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 Discriminator obj's underlying member method code with example usage: obj.forward(x, padding_mask) and returns: x