class_docstr: Encoder-Decoder model
code: def collect_feats(
      self,
      text: torch.Tensor,
      text_lengths: torch.Tensor,
      src_text: torch.Tensor,
      src_text_lengths: torch.Tensor,
      **kwargs,
  ) -> Dict[str, torch.Tensor]:
      if self.extract_feats_in_collect_stats:
          feats, feats_lengths = self._extract_feats(
              src_text, src_text_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}"
          )
          feats, feats_lengths = src_text, src_text_lengths
      return {"feats": feats, "feats_lengths": 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.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  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.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.e2e_mt_common import ErrorCalculator
  from espnet.nets.pytorch_backend.nets_utils import th_accuracy
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 3 for calling the ESPnetMTModel obj's underlying member method code with example usage: obj.collect_feats(text, text_lengths, src_text, src_text_lengths) and returns: dict