class_docstr: Class for sound class embedding training" 
code: def on_stage_start(self, stage, epoch=None):
      def accuracy_value(predict, target, length):
          """Computes Accuracy"""
          nbr_correct, nbr_total = sb.utils.Accuracy.Accuracy(
              predict.unsqueeze(1), target, length
          )
          acc = torch.tensor([nbr_correct / nbr_total])
          return acc
  
      @torch.no_grad()
      def compute_fidelity(theta_out, predictions):
          """Computes top-`k` fidelity of interpreter."""
          predictions = F.softmax(predictions, dim=1)
  
          pred_cl = torch.argmax(predictions, dim=1)
          k_top = torch.topk(
              theta_out, k=self.hparams.k_fidelity, dim=1
          )[1]
  
          # 1 element for each sample in batch, is 0 if pred_cl is in top k
          temp = (k_top - pred_cl.unsqueeze(1) == 0).sum(1)
  
          return temp
  
      @torch.no_grad()
      def compute_inp_fidelity(wavs, predictions):
          """Computes top-1 input fidelity of interpreter."""
          X2 = self.interpret_sample(wavs[0].unsqueeze(0)).unsqueeze(0)
          for i, wav in enumerate(wavs[1:, ...]):
              X2 = torch.cat(
                  (
                      X2,
                      self.interpret_sample(wav.unsqueeze(0)).unsqueeze(
                          0
                      ),
                  ),
                  axis=0,
              )
  
          if self.hparams.use_melspectra:
              net_input = self.modules.compute_fbank(X2.transpose(1, 2))
          else:
              net_input = torch.log1p(X2.transpose(1, 2))
  
          temp = self.hparams.embedding_model(net_input)
  
          if isinstance(temp, tuple):
              embeddings = temp[0]
          else:
              embeddings = temp
  
          if embeddings.ndim == 4:
              embeddings = embeddings.mean((-1, -2))
  
          predictions_interpret = self.hparams.classifier(
              embeddings
          ).squeeze(1)
          predictions = F.softmax(predictions, dim=1)
          predictions_interpret = F.softmax(
              predictions_interpret, dim=1
          )
  
          pred_cl = torch.argmax(predictions, dim=1)
          k_top = torch.argmax(predictions_interpret, dim=1)
  
          return (pred_cl == k_top).float()
  
      @torch.no_grad()
      def compute_faithfulness(wavs, predictions):
          X2 = self.interpret_sample(wavs[0].unsqueeze(0)).unsqueeze(0)
          for i, wav in enumerate(wavs[1:, ...]):
              X2 = torch.cat(
                  (
                      X2,
                      self.interpret_sample(wav.unsqueeze(0)).unsqueeze(
                          0
                      ),
                  ),
                  axis=0,
              )
  
          if self.hparams.use_melspectra:
              net_input = self.modules.compute_fbank(X2.transpose(1, 2))
          else:
              net_input = torch.log1p(X2.transpose(1, 2))
  
          temp = self.hparams.embedding_model(net_input)
  
          if isinstance(temp, tuple):
              embeddings = temp[0]
          else:
              embeddings = temp
  
          if embeddings.ndim == 4:
              embeddings = embeddings.mean((-1, -2))
  
          predictions_masked = self.hparams.classifier(
              embeddings
          ).squeeze(1)
  
          predictions = F.softmax(predictions, dim=1)
          predictions_masked = F.softmax(predictions_masked, dim=1)
  
          # get the prediction indices
          pred_cl = predictions.argmax(dim=1, keepdim=True)
  
          # get the corresponding output probabilities
          predictions_selected = torch.gather(
              predictions, dim=1, index=pred_cl
          )
          predictions_masked_selected = torch.gather(
              predictions_masked, dim=1, index=pred_cl
          )
  
          faithfulness = (
              predictions_selected - predictions_masked_selected
          ).squeeze(1)
  
          return faithfulness
  
      self.top_3_fidelity = MetricStats(metric=compute_fidelity)
      self.input_fidelity = MetricStats(metric=compute_inp_fidelity)
      self.faithfulness = MetricStats(metric=compute_faithfulness)
      self.acc_metric = sb.utils.metric_stats.MetricStats(
          metric=accuracy_value, n_jobs=1
      )
      return super().on_stage_start(stage, epoch)
  
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 6 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.on_stage_start(stage, epoch) and returns: super, acc, temp, unknown, faithfulness