docstr: The forward function, generates synthesized waveforms,
  calculates the scores and the features of the discriminator
  for synthesized waveforms and real waveforms.
  
  Arguments
  ---------
  batch : torch.Tensor or tensors
      An element from the dataloader, including inputs for processing.
  stage : Stage
      The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
docstr_tok: ["The","forward","function",",","generates","synthesized","waveforms",",","calculates","the","scores","and","the","features","of","the","discriminator","for","synthesized","waveforms","and","real","waveforms",".","Arguments","--","--","--","--","-","batch",":","torch.Tensor","or","tensors","An","element","from","the","dataloader",",","including","inputs","for","processing",".","stage",":","Stage","The","stage","of","the","experiment",":","Stage.TRAIN",",","Stage.VALID",",","Stage.TEST"]
code: def compute_forward(self, batch, stage):
      """The forward function, generates synthesized waveforms,
      calculates the scores and the features of the discriminator
      for synthesized waveforms and real waveforms.
  
      Arguments
      ---------
      batch : torch.Tensor or tensors
          An element from the dataloader, including inputs for processing.
      stage : Stage
          The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
  
      """
      batch = batch.to(self.device)
  
      x, _ = batch.code
      y, _ = batch.sig
  
      # generate sythesized waveforms
      y_g_hat, (log_dur_pred, log_dur) = self.modules.generator(x)
      y_g_hat = y_g_hat[:, :, : y.size(2)]
  
      # get scores and features from discriminator for real and synthesized waveforms
      scores_fake, feats_fake = self.modules.discriminator(
          y_g_hat.detach()
      )
      scores_real, feats_real = self.modules.discriminator(y)
  
      return (
          y_g_hat,
          scores_fake,
          feats_fake,
          scores_real,
          feats_real,
          log_dur_pred,
          log_dur,
      )
  
imports: import sys
  import copy
  import random
  import pathlib
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from speechbrain.utils.data_utils import scalarize
  import torch
  import torchaudio
  import numpy
label_desc: Use image node_id 1 for calling the HifiGanBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: y_g_hat,  scores_fake,  feats_fake,  scores_real,  feats_real,  log_dur_pred,  log_dur