code: def forward(self, source, padding_mask, label=None, **kwargs):
      if (
          self.cfg.source_mixup >= 0
          and self.training
          and self.cfg.mixup_prob > 0
      ):
          with torch.no_grad():
              mixed_source = source
              mix_mask = None
              if self.cfg.mixup_prob < 1:
                  mix_mask = (
                      torch.empty(
                          (source.size(0),), device=source.device
                      )
                      .bernoulli_(self.cfg.mixup_prob)
                      .bool()
                  )
                  mixed_source = source[mix_mask]
  
              r = (
                  torch.FloatTensor(
                      1 if self.cfg.same_mixup else mixed_source.size(0)
                  )
                  .uniform_(max(1e-6, self.cfg.source_mixup), 1)
                  .to(dtype=source.dtype, device=source.device)
              )
  
              mixup_perm = torch.randperm(source.size(0))
              s2 = source[mixup_perm]
  
              if self.cfg.gain_mode == "none":
                  p = r.unsqueeze(-1)
                  if mix_mask is not None:
                      s2 = s2[mix_mask]
              else:
                  if self.cfg.gain_mode == "naive_rms":
                      G1 = source.pow(2).mean(dim=-1).sqrt()
                  else:
                      G1, _ = self.compute_gain_torch(
                          source, mode=self.cfg.gain_mode
                      ).max(-1)
                      G1 = G1.to(dtype=source.dtype)
  
                  G2 = G1[mixup_perm]
  
                  if mix_mask is not None:
                      G1 = G1[mix_mask]
                      G2 = G2[mix_mask]
                      s2 = s2[mix_mask]
  
                  p = 1 / (1 + 10 ** ((G1 - G2) / 20) * (1 - r) / r)
                  p = p.unsqueeze(-1)
  
              mixed = (p * mixed_source) + (1 - p) * s2
  
              if mix_mask is None:
                  source = mixed / torch.sqrt(p**2 + (1 - p) ** 2)
              else:
                  source[mix_mask] = mixed / torch.sqrt(
                      p**2 + (1 - p) ** 2
                  )
  
              if label is not None and self.cfg.label_mixup:
                  r = r.unsqueeze(-1)
                  if mix_mask is None:
                      label = label * r + (1 - r) * label[mixup_perm]
                  else:
                      label[mix_mask] = (
                          label[mix_mask] * r
                          + (1 - r) * label[mixup_perm][mix_mask]
                      )
  
      d2v_args = {
          "source": source,
          "padding_mask": padding_mask,
          "mask": self.apply_mask and self.training,
      }
  
      ft = self.freeze_finetune_updates <= self.num_updates
  
      with torch.no_grad() if not ft else contextlib.ExitStack():
          res = self.d2v_model.extract_features(**d2v_args)
  
          x = res["x"]
          padding_mask = res["padding_mask"]
          if padding_mask is not None:
              x[padding_mask] = 0
  
      x = self.final_dropout(x)
  
      if self.training or (
          self.cfg.eval_prediction_mode is None
          or self.cfg.eval_prediction_mode == ""
      ):
          prediction_mode = self.cfg.prediction_mode
      else:
          prediction_mode = self.cfg.eval_prediction_mode
  
      if prediction_mode == "average_before":
          x = x.mean(dim=1)
  
      if (
          prediction_mode != "summary_mha"
          and prediction_mode != "summary_proj"
          and prediction_mode != "cls"
      ):
          x = self.proj(x)
  
      logits = True
      if prediction_mode == "lin_softmax":
          x = F.logsigmoid(x.float())
          x = torch.logsumexp(x + x, dim=1) - torch.logsumexp(x, dim=1)
          x = x.clamp(max=0)
          x = x - torch.log(-(torch.expm1(x)))
      elif prediction_mode == "extremized_odds":
          x = x.float().sum(dim=1)
          x = x * self.cfg.extreme_factor
      elif prediction_mode == "average_before":
          x = x.float()
      elif prediction_mode == "average":
          x = x.float().mean(dim=1)
      elif prediction_mode == "average_sigmoid":
          x = torch.sigmoid(x.float())
          x = x.mean(dim=1)
          logits = False
      elif prediction_mode == "max":
          x, _ = x.float().max(dim=1)
      elif prediction_mode == "max_sigmoid":
          x = torch.sigmoid(x.float())
          x, _ = x.float().max(dim=1)
          logits = False
      elif prediction_mode == "proj_avg_proj":
          x = x.mean(dim=1)
          x = self.proj2(x)
      elif (
          prediction_mode == "summary_mha"
          or prediction_mode == "summary_proj"
      ):
          x = self.d2v_model.summary(
              x, padding_mask, proj=prediction_mode == "summary_proj"
          )
          x = x.type_as(source)
          x = self.proj(x)
      elif prediction_mode == "cls":
          x = x[:, 0]
          x = self.proj(x)
      else:
          raise Exception(f"unknown prediction mode {prediction_mode}")
  
      if label is None:
          return torch.sigmoid(x) if logits else x
  
      x = torch.nan_to_num(x)
  
      if logits:
          loss = F.binary_cross_entropy_with_logits(
              x, label.float(), reduction="none"
          )
      else:
          loss = F.binary_cross_entropy(
              x, label.float(), reduction="none"
          )
  
      result = {
          "losses": {
              "main": loss,
          },
          "sample_size": label.sum(),
      }
  
      if not self.training:
          result["_predictions"] = torch.sigmoid(x) if logits else x
          result["_targets"] = label
  
      return result
  
imports: import contextlib
  import logging
  import re
  from dataclasses import dataclass, field
  from typing import Any, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from omegaconf import II, MISSING, open_dict
  from fairseq import checkpoint_utils, tasks
  from fairseq.dataclass import FairseqDataclass
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES
  from fairseq.modules import TransposeLast
  from fairseq.tasks import FairseqTask
label_desc: Use image node_id 8 for calling the AudioClassificationModel obj's underlying member method code with example usage: obj.forward(source, padding_mask, label) and returns: result, unknown