code: def forward(
      self,
      y_hat=None,
      y=None,
      scores_fake=None,
      feats_fake=None,
      feats_real=None,
      y_hat_sub=None,
      y_sub=None,
  ):
      gen_loss = 0
      adv_loss = 0
      return_dict = {}
  
      # STFT Loss
      if self.use_stft_loss:
          stft_loss_mg, stft_loss_sc = self.stft_loss(
              y_hat.squeeze(1), y.squeeze(1)
          )
          return_dict["G_stft_loss_mg"] = stft_loss_mg
          return_dict["G_stft_loss_sc"] = stft_loss_sc
          gen_loss += self.stft_loss_weight * (
              stft_loss_mg + stft_loss_sc
          )
  
      # subband STFT Loss
      if self.use_subband_stft_loss:
          (
              subband_stft_loss_mg,
              subband_stft_loss_sc,
          ) = self.subband_stft_loss(y_hat_sub, y_sub)
          return_dict["G_subband_stft_loss_mg"] = subband_stft_loss_mg
          return_dict["G_subband_stft_loss_sc"] = subband_stft_loss_sc
          gen_loss += self.subband_stft_loss_weight * (
              subband_stft_loss_mg + subband_stft_loss_sc
          )
  
      # multiscale MSE adversarial loss
      if self.use_mse_gan_loss and scores_fake is not None:
          mse_fake_loss = _apply_G_adv_loss(scores_fake, self.mse_loss)
          return_dict["G_mse_fake_loss"] = mse_fake_loss
          adv_loss += self.mse_gan_loss_weight * mse_fake_loss
  
      # multiscale Hinge adversarial loss
      if self.use_hinge_gan_loss and not scores_fake is not None:
          hinge_fake_loss = _apply_G_adv_loss(
              scores_fake, self.hinge_loss
          )
          return_dict["G_hinge_fake_loss"] = hinge_fake_loss
          adv_loss += self.hinge_gan_loss_weight * hinge_fake_loss
  
      # Feature Matching Loss
      if self.use_feat_match_loss and not feats_fake:
          feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
          return_dict["G_feat_match_loss"] = feat_match_loss
          adv_loss += self.feat_match_loss_weight * feat_match_loss
      return_dict["G_loss"] = gen_loss + adv_loss
      return_dict["G_gen_loss"] = gen_loss
      return_dict["G_adv_loss"] = adv_loss
      return return_dict
  
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the GeneratorLoss obj's underlying member method code with example usage: obj.forward(y_hat, y, scores_fake, feats_fake, feats_real, y_hat_sub, y_sub) and returns: return_dict