class_docstr: CTC-attention hybrid Encoder-Decoder model
class_docstr_tok: ["CTC-attention","hybrid","Encoder-Decoder","model"]
code: def collect_feats(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
      text: torch.Tensor,
      text_lengths: torch.Tensor,
      text_prev: torch.Tensor,
      text_prev_lengths: torch.Tensor,
      text_ctc: torch.Tensor,
      text_ctc_lengths: torch.Tensor,
      **kwargs,
  ) -> Dict[str, torch.Tensor]:
      feats, feats_lengths = self._extract_feats(speech, speech_lengths)
      return {"feats": feats, "feats_lengths": feats_lengths}
  
imports: import logging
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.cuda.amp import autocast
  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.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.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.pytorch_backend.nets_utils import pad_list, th_accuracy
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 3 for calling the ESPnetS2TModel obj's underlying member method code with example usage: obj.collect_feats(speech, speech_lengths, text, text_lengths, text_prev, text_prev_lengths, text_ctc, text_ctc_lengths) and returns: dict