code: def forward(
      self,
      features,
      padding_mask,
      random_label=None,
      dense_x_only=False,
      segment=True,
      aux_target=None,
  ):
      if segment:
          features, padding_mask = self.segmenter.pre_segment(
              features, padding_mask
          )
  
      orig_size = (
          features.size(0) * features.size(1) - padding_mask.sum()
      )
  
      gen_result = self.generator(features, random_label, padding_mask)
  
      orig_dense_x, token_x = (
          gen_result["dense_x"],
          gen_result["token_x"],
      )
      orig_dense_padding_mask = gen_result["dense_padding_mask"]
  
      if segment:
          dense_x, dense_padding_mask = self.segmenter.logit_segment(
              orig_dense_x, orig_dense_padding_mask
          )
      else:
          dense_x = orig_dense_x
          dense_padding_mask = orig_dense_padding_mask
  
      dense_logits = dense_x
      prob_perplexity = None
      code_perplexity = None
  
      if not (self.no_softmax and dense_x_only):
          dense_x, code_perplexity, prob_perplexity = self.normalize(
              dense_logits
          )
  
      if dense_x_only or self.discriminator is None:
          return {
              "logits": dense_x,
              "padding_mask": dense_padding_mask,
          }
  
      token_padding_mask = random_label == self.pad
  
      dense_y = self.discriminator(dense_x, dense_padding_mask)
      token_y = self.discriminator(token_x, token_padding_mask)
  
      sample_size = features.size(0)
  
      d_step = self.discrim_step(self.update_num)
  
      fake_smooth = self.smoothing
      real_smooth = self.smoothing
      if self.smoothing_one_sided:
          fake_smooth = 0
  
      zero_loss = None
      smoothness_loss = None
      code_pen = None
      mmi_loss = None
  
      if d_step:
          loss_dense = F.binary_cross_entropy_with_logits(
              dense_y,
              dense_y.new_ones(dense_y.shape) - fake_smooth,
              reduction="sum",
          )
          loss_token = F.binary_cross_entropy_with_logits(
              token_y,
              token_y.new_zeros(token_y.shape) + real_smooth,
              reduction="sum",
          )
          if self.training and self.gradient_penalty > 0:
              grad_pen = self.calc_gradient_penalty(token_x, dense_x)
              grad_pen = grad_pen.sum() * self.gradient_penalty
          else:
              grad_pen = None
      else:
          grad_pen = None
          loss_token = None
          loss_dense = F.binary_cross_entropy_with_logits(
              dense_y,
              dense_y.new_zeros(dense_y.shape) + fake_smooth,
              reduction="sum",
          )
          num_vars = dense_x.size(-1)
          if prob_perplexity is not None:
              code_pen = (num_vars - prob_perplexity) / num_vars
              code_pen = code_pen * sample_size * self.code_penalty
  
          if self.smoothness_weight > 0:
              smoothness_loss = F.mse_loss(
                  dense_logits[:, :-1],
                  dense_logits[:, 1:],
                  reduction="none",
              )
              smoothness_loss[dense_padding_mask[:, 1:]] = 0
              smoothness_loss = (
                  smoothness_loss.mean()
                  * sample_size
                  * self.smoothness_weight
              )
  
          if (self.mmi_weight > 0) and (aux_target is not None):
              inter_x = self.decoder(gen_result["inter_x"])
              if self.target_downsample_rate > 1:
                  aux_target = aux_target[
                      :, :: self.target_downsample_rate
                  ]
              max_t_len = min(aux_target.shape[1], inter_x.shape[1])
              mmi_loss = F.cross_entropy(
                  inter_x[:, :max_t_len].transpose(1, 2),
                  aux_target[:, :max_t_len],
                  ignore_index=-1,
                  reduction="none",
              )
              mmi_loss = (
                  mmi_loss.mean() * mmi_loss.shape[0] * self.mmi_weight
              )
  
      result = {
          "losses": {
              "grad_pen": grad_pen,
              "code_pen": code_pen,
              "smoothness": smoothness_loss,
              "mmi": mmi_loss,
          },
          "temp": self.curr_temp,
          "code_ppl": code_perplexity,
          "prob_ppl": prob_perplexity,
          "d_steps": int(d_step),
          "sample_size": sample_size,
      }
  
      suff = "_d" if d_step else "_g"
      result["losses"]["dense" + suff] = loss_dense
      result["losses"]["token" + suff] = loss_token
  
      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 10 for calling the Wav2vec_U obj's underlying member method code with example usage: obj.forward(features, padding_mask, random_label, dense_x_only, segment, aux_target) and returns: result, dict