class_docstr: CTC-attention hybrid Encoder-Decoder model
class_docstr_tok: ["CTC-attention","hybrid","Encoder-Decoder","model"]
code: def __init__(
      self,
      vocab_size: int,
      token_list: Union[Tuple[str, ...], List[str]],
      frontend: Optional[AbsFrontend],
      specaug: Optional[AbsSpecAug],
      normalize: Optional[AbsNormalize],
      preencoder: Optional[AbsPreEncoder],
      encoder: AbsEncoder,
      postencoder: Optional[AbsPostEncoder],
      decoder: Optional[AbsDecoder],
      ctc: CTC,
      ctc_weight: float = 0.5,
      interctc_weight: float = 0.0,
      ignore_id: int = -1,
      lsm_weight: float = 0.0,
      length_normalized_loss: bool = False,
      report_cer: bool = True,
      report_wer: bool = True,
      sym_space: str = "<space>",
      sym_blank: str = "<blank>",
      sym_sos: str = "<sos>",
      sym_eos: str = "<eos>",
      sym_sop: str = "<sop>",  # start of prev
      sym_na: str = "<na>",  # not available
      extract_feats_in_collect_stats: bool = True,
  ):
      assert check_argument_types()
      assert 0.0 <= ctc_weight <= 1.0, ctc_weight
      assert 0.0 <= interctc_weight < 1.0, interctc_weight
  
      super().__init__()
  
      self.blank_id = token_list.index(sym_blank)
      self.sos = token_list.index(sym_sos)
      self.eos = token_list.index(sym_eos)
      self.sop = token_list.index(sym_sop)
      self.na = token_list.index(sym_na)
      self.vocab_size = vocab_size
      self.ignore_id = ignore_id
      self.ctc_weight = ctc_weight
      self.interctc_weight = interctc_weight
      self.token_list = token_list.copy()
  
      self.frontend = frontend
      self.specaug = specaug
      self.normalize = normalize
      self.preencoder = preencoder
      self.postencoder = postencoder
      self.encoder = encoder
  
      if not hasattr(self.encoder, "interctc_use_conditioning"):
          self.encoder.interctc_use_conditioning = False
      if self.encoder.interctc_use_conditioning:
          self.encoder.conditioning_layer = torch.nn.Linear(
              vocab_size, self.encoder.output_size()
          )
  
      self.error_calculator = None
  
      if ctc_weight < 1.0:
          assert (
              decoder is not None
          ), "decoder should not be None when attention is used"
      else:
          decoder = None
          logging.warning("Set decoder to none as ctc_weight==1.0")
  
      self.decoder = decoder
  
      self.criterion_att = LabelSmoothingLoss(
          size=vocab_size,
          padding_idx=ignore_id,
          smoothing=lsm_weight,
          normalize_length=length_normalized_loss,
      )
  
      if report_cer or report_wer:
          self.error_calculator = ErrorCalculator(
              token_list, sym_space, sym_blank, report_cer, report_wer
          )
  
      if ctc_weight == 0.0:
          self.ctc = None
      else:
          self.ctc = ctc
  
      self.extract_feats_in_collect_stats = (
          extract_feats_in_collect_stats
      )
  
      self.is_encoder_whisper = "Whisper" in type(self.encoder).__name__
  
      if self.is_encoder_whisper:
          assert (
              self.frontend is None
          ), "frontend should be None when using full Whisper model"
  
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 1 to create a new ESPnetS2TModel object from inherited base classes: AbsESPnetModel with example: obj = ESPnetS2TModel(vocab_size, token_list, frontend, specaug, normalize, preencoder, encoder, postencoder, decoder, ctc, ctc_weight, interctc_weight, ignore_id, lsm_weight, length_normalized_loss, report_cer, report_wer, sym_space, sym_blank, sym_sos, sym_eos, sym_sop, sym_na, extract_feats_in_collect_stats)