class_docstr: This class implements the interface for the PIQ posthoc interpreter for an audio classifier.
  
  Example
  -------
  >>> from speechbrain.inference.interpretability import PIQAudioInterpreter
  >>> tmpdir = getfixture("tmpdir")
  >>> interpreter = PIQAudioInterpreter.from_hparams(
  ...     source="speechbrain/PIQ-ESC50",
  ...     savedir=tmpdir,
  ... )
  >>> signal = torch.randn(1, 16000)
  >>> interpretation, _ = interpreter.interpret_batch(signal)
class_docstr_tok: ["This","class","implements","the","interface","for","the","PIQ","posthoc","interpreter","for","an","audio","classifier",".","Example","--","--","--","-",">",">",">","from","speechbrain.inference.interpretability","import","PIQAudioInterpreter",">",">",">","tmpdir","=","getfixture","(","``","tmpdir","''",")",">",">",">","interpreter","=","PIQAudioInterpreter.from_hparams","(","...","source=","''","speechbrain\/PIQ-ESC50","''",",","...","savedir=tmpdir",",","...",")",">",">",">","signal","=","torch.randn","(","1",",","16000",")",">",">",">","interpretation",",","_","=","interpreter.interpret_batch","(","signal",")"]
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.mods.embedding_model(X_stft_logpower)
      embeddings = hcat.mean((-1, -2))
      predictions = self.mods.classifier(embeddings).squeeze(1)
      class_pred = predictions.argmax(1)
      return hcat, embeddings, predictions, class_pred
  
file_path: speechbrain/speechbrain/inference/interpretability.py
imports: import torch
  import torchaudio
  import torch.nn.functional
  import speechbrain
  from speechbrain.utils.fetching import fetch
  from speechbrain.utils.data_utils import split_path
  from speechbrain.processing.NMF import spectral_phase
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 3 for calling the PIQAudioInterpreter obj's underlying member method code with example usage: obj.classifier_forward(X_stft_logpower) and returns: hcat,  embeddings,  predictions,  class_pred