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: AbsDecoder,
      ctc: CTC,
      joint_network: Optional[torch.nn.Module],
      postdecoder: Optional[AbsPostDecoder] = None,
      deliberationencoder: Optional[AbsPostEncoder] = None,
      transcript_token_list: Union[Tuple[str, ...], List[str]] = None,
      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>",
      extract_feats_in_collect_stats: bool = True,
      two_pass: bool = False,
      pre_postencoder_norm: bool = False,
  ):
      assert check_argument_types()
      assert 0.0 <= ctc_weight <= 1.0, ctc_weight
      assert 0.0 <= interctc_weight < 1.0, interctc_weight
  
      AbsESPnetModel.__init__(self)
      # note that eos is the same as sos (equivalent ID)
      self.blank_id = 0
      self.sos = vocab_size - 1
      self.eos = vocab_size - 1
      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()
      if transcript_token_list is not None:
          self.transcript_token_list = transcript_token_list.copy()
      self.two_pass = two_pass
      self.pre_postencoder_norm = pre_postencoder_norm
      self.frontend = frontend
      self.specaug = specaug
      self.normalize = normalize
      self.preencoder = preencoder
      self.postencoder = postencoder
      self.postdecoder = postdecoder
      self.encoder = encoder
      if self.postdecoder is not None:
          if (
              self.encoder._output_size
              != self.postdecoder.output_size_dim
          ):
              self.uniform_linear = torch.nn.Linear(
                  self.encoder._output_size,
                  self.postdecoder.output_size_dim,
              )
  
      self.deliberationencoder = deliberationencoder
      # we set self.decoder = None in the CTC mode since
      # self.decoder parameters were never used and PyTorch complained
      # and threw an Exception in the multi-GPU experiment.
      # thanks Jeff Farris for pointing out the issue.
      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.use_transducer_decoder = joint_network is not None
  
      self.error_calculator = None
  
      if self.use_transducer_decoder:
          from warprnnt_pytorch import RNNTLoss
  
          self.decoder = decoder
          self.joint_network = joint_network
  
          self.criterion_transducer = RNNTLoss(
              blank=self.blank_id,
              fastemit_lambda=0.0,
          )
  
          if report_cer or report_wer:
              self.error_calculator_trans = ErrorCalculatorTransducer(
                  decoder,
                  joint_network,
                  token_list,
                  sym_space,
                  sym_blank,
                  report_cer=report_cer,
                  report_wer=report_wer,
              )
          else:
              self.error_calculator_trans = None
  
              if self.ctc_weight != 0:
                  self.error_calculator = ErrorCalculator(
                      token_list,
                      sym_space,
                      sym_blank,
                      report_cer,
                      report_wer,
                  )
      else:
          # we set self.decoder = None in the CTC mode since
          # self.decoder parameters were never used and PyTorch complained
          # and threw an Exception in the multi-GPU experiment.
          # thanks Jeff Farris for pointing out the issue.
          if ctc_weight == 1.0:
              self.decoder = None
          else:
              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
      )
  
imports: 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.espnet_model import ESPnetASRModel
  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.asr.transducer.error_calculator import ErrorCalculatorTransducer
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.slu.postdecoder.abs_postdecoder import AbsPostDecoder
  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.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 1 to create a new ESPnetSLUModel object from inherited base classes: ESPnetASRModel with example: obj = ESPnetSLUModel(vocab_size, token_list, frontend, specaug, normalize, preencoder, encoder, postencoder, decoder, ctc, joint_network, postdecoder, deliberationencoder, transcript_token_list, ctc_weight, interctc_weight, ignore_id, lsm_weight, length_normalized_loss, report_cer, report_wer, sym_space, sym_blank, extract_feats_in_collect_stats, two_pass, pre_postencoder_norm)