class_docstr: An end-to-end SLU model.
  
  The class can be used either to run only the encoder (encode()) to extract
  features or to run the entire model (decode()) to map the speech to its semantics.
  
  Example
  -------
  >>> from speechbrain.inference.SLU import EndToEndSLU
  >>> tmpdir = getfixture("tmpdir")
  >>> slu_model = EndToEndSLU.from_hparams(
  ...     source="speechbrain/slu-timers-and-such-direct-librispeech-asr",
  ...     savedir=tmpdir,
  ... )  # doctest: +SKIP
  >>> slu_model.decode_file("tests/samples/single-mic/example6.wav") # doctest: +SKIP
  "{'intent': 'SimpleMath', 'slots': {'number1': 37.67, 'number2': 75.7, 'op': ' minus '}}"
docstr: Maps the given audio file to a string representing the
  semantic dictionary for the utterance.
  
  Arguments
  ---------
  path : str
      Path to audio file to decode.
  
  Returns
  -------
  str
      The predicted semantics.
code: def decode_file(self, path, **kwargs):
      """Maps the given audio file to a string representing the
      semantic dictionary for the utterance.
  
      Arguments
      ---------
      path : str
          Path to audio file to decode.
  
      Returns
      -------
      str
          The predicted semantics.
      """
      waveform = self.load_audio(path, **kwargs)
      waveform = waveform.to(self.device)
      # Fake a batch:
      batch = waveform.unsqueeze(0)
      rel_length = torch.tensor([1.0])
      predicted_words, predicted_tokens = self.decode_batch(
          batch, rel_length
      )
      return predicted_words[0]
  
file_path: speechbrain/speechbrain/inference/SLU.py
imports: import torch
  from speechbrain.inference.interfaces import Pretrained
  from speechbrain.inference.ASR import EncoderDecoderASR
label_desc: Use image node_id 2 for calling the EndToEndSLU obj's underlying member method code with example usage: obj.decode_file(path) and returns: predicted_words