class_docstr: Target Speaker Extraction Frontend model
class_docstr_tok: ["Target","Speaker","Extraction","Frontend","model"]
code: def collect_feats(
      self,
      speech_mix: torch.Tensor,
      speech_mix_lengths: torch.Tensor,
      **kwargs,
  ) -> Dict[str, torch.Tensor]:
      # for data-parallel
      speech_mix = speech_mix[:, : speech_mix_lengths.max()]
  
      feats, feats_lengths = speech_mix, speech_mix_lengths
      return {"feats": feats, "feats_lengths": feats_lengths}
  
imports: import contextlib
  from typing import Dict, List, Optional, OrderedDict, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.enh.decoder.abs_decoder import AbsDecoder
  from espnet2.enh.encoder.abs_encoder import AbsEncoder
  from espnet2.enh.extractor.abs_extractor import AbsExtractor
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainLoss
  from espnet2.enh.loss.criterions.time_domain import TimeDomainLoss
  from espnet2.enh.loss.wrappers.abs_wrapper import AbsLossWrapper
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
label_desc: Use image node_id 7 for calling the ESPnetExtractionModel obj's underlying member method code with example usage: obj.collect_feats(speech_mix, speech_mix_lengths) and returns: dict