class_docstr: Class for sound class embedding training" 
docstr: Computation pipeline based on a encoder + sound classifier.
  Data augmentation and environmental corruption are applied to the
  input sound.
docstr_tok: ["Computation","pipeline","based","on","a","encoder","+","sound","classifier",".","Data","augmentation","and","environmental","corruption","are","applied","to","the","input","sound","."]
code: def compute_forward(self, batch, stage):
      """Computation pipeline based on a encoder + sound classifier.
      Data augmentation and environmental corruption are applied to the
      input sound.
      """
      batch = batch.to(self.device)
      wavs, lens = batch.sig
  
      X_stft = self.modules.compute_stft(wavs)
      X_stft_power = sb.processing.features.spectral_magnitude(
          X_stft, power=self.hparams.spec_mag_power
      )
      if self.hparams.use_melspectra:
          net_input = self.modules.compute_fbank(X_stft_power)
      else:
          net_input = torch.log1p(X_stft_power)
  
      # Embeddings + sound classifier
      temp = self.hparams.embedding_model(net_input)
      if isinstance(temp, tuple):
          embeddings, f_I = temp
      else:
          embeddings, f_I = temp, temp
  
      if embeddings.ndim == 4:
          embeddings = embeddings.mean((-1, -2))
  
      predictions = self.hparams.classifier(embeddings).squeeze(1)
  
      psi_out = self.modules.psi(f_I)  # generate nmf activations
  
      if isinstance(psi_out, tuple):
          psi_out = psi_out[0]
          psi_out = psi_out.squeeze(1).permute(0, 2, 1)
  
      # cut the length of psi
      psi_out = psi_out[:, :, : X_stft_power.shape[1]]
  
      #  generate log-mag spectrogram
      reconstructed = self.hparams.nmf_decoder(psi_out).transpose(1, 2)
  
      # generate classifications from time activations
      theta_out = self.modules.theta(psi_out)
  
      if stage == sb.Stage.VALID:
          # save some samples
          if (
              self.hparams.epoch_counter.current
              % self.hparams.interpret_period
          ) == 0 and self.hparams.save_interpretations:
              wavs = wavs[0].unsqueeze(0)
  
              self.interpret_sample(wavs, batch)
              self.overlap_test(batch)
  
      return (reconstructed, psi_out), (predictions, theta_out)
  
imports: import os
  import sys
  import torch
  import torchaudio
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from esc50_prepare import prepare_esc50
  from speechbrain.utils.metric_stats import MetricStats
  from os import makedirs
  import torch.nn.functional
  from speechbrain.processing.NMF import spectral_phase
label_desc: Use image node_id 4 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: reconstructed,  psi_out,  predictions,  theta_out