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: Runs full decoding - note: no gradients through decoding
code: def forward(self, wavs, wav_lens):
      """Runs full decoding - note: no gradients through decoding"""
      return self.decode_batch(wavs, wav_lens)
  
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 5 for calling the EndToEndSLU obj's underlying member method code with example usage: obj.forward(wavs, wav_lens) and returns: self