docstr: Computes the loss given the predicted and targeted outputs.
  Arguments
  ---------
  predictions : torch.Tensor
      The model generated spectrograms and other metrics from `compute_forward`.
  batch : PaddedBatch
      This batch object contains all the relevant tensors for computation.
  stage : sb.Stage
      One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST.
  Returns
  -------
  loss : torch.Tensor
      A one-element tensor used for backpropagating the gradient.
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code: def compute_objectives(self, predictions, batch, stage):
      """Computes the loss given the predicted and targeted outputs.
      Arguments
      ---------
      predictions : torch.Tensor
          The model generated spectrograms and other metrics from `compute_forward`.
      batch : PaddedBatch
          This batch object contains all the relevant tensors for computation.
      stage : sb.Stage
          One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST.
      Returns
      -------
      loss : torch.Tensor
          A one-element tensor used for backpropagating the gradient.
      """
      (p_seq, wavs, transcripts) = predictions
      tokens_eos, tokens_eos_lens = batch.code_eos
      ids = batch.id
  
      # speech translation loss
      loss = self.hparams.seq_cost(
          p_seq, tokens_eos, length=tokens_eos_lens
      )
  
      if stage != sb.Stage.TRAIN:
          if (
              stage == sb.Stage.TEST
              or self.hparams.epoch_counter.current
              % self.hparams.evaluation_interval
              == 0
          ):
              # compute the accuracy of the one-step-forward prediction
              self.acc_metric.append(p_seq, tokens_eos, tokens_eos_lens)
  
              tgt_wavs, _ = batch.tgt_sig
              tgt_transcripts = batch.tgt_text
  
              # Save last batch
              wavs = [wav.cpu() for wav in wavs]
              tgt_wavs = [wav.cpu() for wav in tgt_wavs]
              self.last_batch = [
                  ids,
                  (wavs, transcripts),
                  (tgt_transcripts, tgt_wavs),
              ]
  
      return loss
  
imports: import sys
  import torch
  import logging
  import pathlib
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from speechbrain.inference.vocoders import UnitHIFIGAN
  from speechbrain.inference.ASR import EncoderDecoderASR
  import tqdm
  import torchaudio
  import numpy
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 2 for calling the S2UT obj's underlying member method code with example usage: obj.compute_objectives(predictions, batch, stage) and returns: loss