class_docstr: ESPnet speech-to-speech translation model
code: def _extract_feats(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
      target: bool = False,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      if speech_lengths is not None:
          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)
          if target:
              feats, feats_lengths = self.tgt_feats_extract(
                  speech, speech_lengths
              )
          else:
              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.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.s2st.aux_attention.abs_aux_attention import AbsS2STAuxAttention
  from espnet2.s2st.losses.abs_loss import AbsS2STLoss
  from espnet2.s2st.synthesizer.abs_synthesizer import AbsSynthesizer
  from espnet2.s2st.tgt_feats_extract.abs_tgt_feats_extract import AbsTgtFeatsExtract
  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
  from espnet.nets.e2e_mt_common import ErrorCalculator
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, th_accuracy
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
label_desc: Use image node_id 6 for calling the ESPnetS2STModel obj's underlying member method code with example usage: obj._extract_feats(speech, speech_lengths, target) and returns: feats,  feats_lengths