class_docstr: The SpeechBrain class to train Non-Negative Factorization with Amortized Inference
docstr: this function computes the l2-error to train the NMF model.
code: def compute_objectives(
      self, predictions, batch, stage=sb.Stage.TRAIN
  ):
      """
      this function computes the l2-error to train the NMF model.
      """
      batch = batch.to(self.device)
      wavs, lens = batch.sig
  
      X_stft = self.hparams.compute_stft(wavs)
      X_stft_power = self.hparams.compute_stft_mag(X_stft)
      target = torch.log1p(X_stft_power).permute(0, 2, 1)
  
      loss = ((target.squeeze() - predictions) ** 2).mean()
      return loss
  
file_path: speechbrain/recipes/ESC50/interpret/train_nmf.py
imports: import sys
  import torch
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from esc50_prepare import prepare_esc50
  from train_l2i import dataio_prep
label_desc: Use image node_id 2 for calling the NMFBrain obj's underlying member method code with example usage: obj.compute_objectives(predictions, batch, stage) and returns: loss