class_docstr: Class for sound class embedding training" 
class_docstr_tok: ["Class","for","sound","class","embedding","training","''"]
docstr: The forward pass for the classifier
docstr_tok: ["The","forward","pass","for","the","classifier"]
code: def classifier_forward(self, X_stft_logpower):
      """The forward pass for the classifier"""
      hcat = self.hparams.embedding_model(X_stft_logpower)
      embeddings = hcat.mean((-1, -2))
      predictions = self.hparams.classifier(embeddings).squeeze(1)
      class_pred = predictions.argmax(1)
  
      return hcat, embeddings, predictions, class_pred
  
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
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.classifier_forward(X_stft_logpower) and returns: hcat,  embeddings,  predictions,  class_pred