class_docstr: TorchAudio Hubert Pretrain model
class_docstr_tok: ["TorchAudio","Hubert","Pretrain","model"]
code: def _extract_feats(
      self, speech: torch.Tensor, speech_lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      assert speech_lengths.dim() == 1, speech_lengths.shape
  
      # for data-parallel
      speech = speech[:, : speech_lengths.max()]
  
      if self.frontend is not None:
          # Frontend
          #  e.g. STFT and Feature extract
          #       data_loader may send time-domain signal in this case
          # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
          feats, feats_lengths = self.frontend(speech, speech_lengths)
      else:
          # No frontend and no feature extract
          feats, feats_lengths = speech, speech_lengths
      return feats, feats_lengths
  
imports: import logging
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.hubert.hubert_loss import HubertPretrainLoss
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.e2e_asr_common import ErrorCalculator
label_desc: Use image node_id 5 for calling the TorchAudioHubertPretrainModel obj's underlying member method code with example usage: obj._extract_feats(speech, speech_lengths) and returns: feats,  feats_lengths