class_docstr: Class for sound class embedding training" 
docstr: computation steps to get the interpretation spectrogram
docstr_tok: ["computation","steps","to","get","the","interpretation","spectrogram"]
code: def interpret_computation_steps(self, wavs):
      """computation steps to get the interpretation spectrogram"""
      # compute stft and logmel, and phase
      X_stft = self.modules.compute_stft(wavs)
      X_stft_phase = spectral_phase(X_stft)
      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)
  
      # get the classifier embeddings
      temp = self.hparams.embedding_model(net_input)
  
      if isinstance(
          temp, tuple
      ):  # if embeddings are not used for interpretation
          embeddings, f_I = temp
      else:
          embeddings, f_I = temp, temp
  
      # get the nmf activations
      psi_out = self.modules.psi(f_I)
  
      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 in case necessary
      psi_out = psi_out[:, :, : X_stft_power.shape[1]]
  
      # get the classifier output
      if embeddings.ndim == 4:
          embeddings = embeddings.mean((-1, -2))
  
      predictions = self.hparams.classifier(embeddings).squeeze(1)
      pred_cl = torch.argmax(predictions, dim=1)[0].item()
  
      nmf_dictionary = self.hparams.nmf_decoder.return_W()
  
      # computes time activations per component
      # FROM NOW ON WE FOLLOW THE PAPER'S NOTATION
      psi_out = psi_out.squeeze()
      z = self.modules.theta.hard_att(psi_out).squeeze()
      theta_c_w = self.modules.theta.classifier[0].weight[pred_cl]
  
      # some might be negative, relevance of component
      r_c_x = theta_c_w * z / torch.abs(theta_c_w * z).max()
      # define selected components by thresholding
      L = (
          torch.arange(r_c_x.shape[0])
          .to(r_c_x.device)[r_c_x > self.hparams.relevance_th]
          .tolist()
      )
  
      # get the log power spectra, this is needed as NMF is trained on log-power spectra
      X_stft_power_log = (
          torch.log(X_stft_power + 1).transpose(1, 2).squeeze(0)
      )
  
      X_withselected = nmf_dictionary[:, L] @ psi_out[L, :]
      Xhat = nmf_dictionary @ psi_out
  
      X_stft_power_log = X_stft_power_log[..., : Xhat.shape[1]]
  
      # need the eps for the denominator
      eps = 1e-10
      X_int = (X_withselected / (Xhat + eps)) * X_stft_power_log
  
      # get back to the standard stft
      X_int = torch.exp(X_int) - 1
      return X_int, X_stft_phase, pred_cl
  
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 1 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.interpret_computation_steps(wavs) and returns: X_int,  X_stft_phase,  pred_cl