class_docstr: ESPnet speech-to-speech translation model
code: def collect_feats(
      self,
      src_speech: torch.Tensor,
      src_speech_lengths: torch.Tensor,
      tgt_speech: torch.Tensor,
      tgt_speech_lengths: torch.Tensor,
      **kwargs,
  ) -> Dict[str, torch.Tensor]:
      if self.extract_feats_in_collect_stats:
          src_feats, src_feats_lengths = self._extract_feats(
              src_speech, src_speech_lengths
          )
          return_dict = {
              "src_feats": src_feats,
              "src_feats_lengths": src_feats_lengths,
          }
  
          if self.tgt_feats_extract is not None:
              tgt_feats, tgt_feats_lengths = self._extract_feats(
                  tgt_speech, tgt_speech_lengths, target=True
              )
              return_dict.update(
                  tgt_feats=tgt_feats,
                  tgt_feats_lengths=tgt_feats_lengths,
              )
      else:
          # Generate dummy stats if extract_feats_in_collect_stats is False
          logging.warning(
              "Generating dummy stats for feats and feats_lengths, "
              "because encoder_conf.extract_feats_in_collect_stats is "
              f"{self.extract_feats_in_collect_stats}"
          )
          return_dict = {
              "src_feats": src_speech,
              "tgt_feats": tgt_speech,
              "src_feats_lengths": src_speech_lengths,
              "tgt_feats_lengths": tgt_speech_lengths,
          }
  
      return return_dict
  
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 4 for calling the ESPnetS2STModel obj's underlying member method code with example usage: obj.collect_feats(src_speech, src_speech_lengths, tgt_speech, tgt_speech_lengths) and returns: return_dict