code: def build_model(cls, args: argparse.Namespace) -> ESPnetSLUModel:
      assert check_argument_types()
      if isinstance(args.token_list, str):
          with open(args.token_list, encoding="utf-8") as f:
              token_list = [line.rstrip() for line in f]
  
          # Overwriting token_list to keep it as "portable".
          args.token_list = list(token_list)
      elif isinstance(args.token_list, (tuple, list)):
          token_list = list(args.token_list)
      else:
          raise RuntimeError("token_list must be str or list")
      if "transcript_token_list" in args:
          if args.transcript_token_list is not None:
              if isinstance(args.transcript_token_list, str):
                  with open(
                      args.transcript_token_list, encoding="utf-8"
                  ) as f:
                      transcript_token_list = [
                          line.rstrip() for line in f
                      ]
  
                  # Overwriting token_list to keep it as "portable".
                  args.transcript_token_list = list(
                      transcript_token_list
                  )
              elif isinstance(args.token_list, (tuple, list)):
                  transcript_token_list = list(
                      args.transcript_token_list
                  )
              else:
                  raise RuntimeError(
                      " Transcript token_list must be str or list"
                  )
      vocab_size = len(token_list)
      logging.info(f"Vocabulary size: {vocab_size }")
  
      # 1. frontend
      if args.input_size is None:
          # Extract features in the model
          frontend_class = frontend_choices.get_class(args.frontend)
          frontend = frontend_class(**args.frontend_conf)
          input_size = frontend.output_size()
      else:
          # Give features from data-loader
          args.frontend = None
          args.frontend_conf = {}
          frontend = None
          input_size = args.input_size
  
      # 2. Data augmentation for spectrogram
      if args.specaug is not None:
          specaug_class = specaug_choices.get_class(args.specaug)
          specaug = specaug_class(**args.specaug_conf)
      else:
          specaug = None
  
      # 3. Normalization layer
      if args.normalize is not None:
          normalize_class = normalize_choices.get_class(args.normalize)
          normalize = normalize_class(**args.normalize_conf)
      else:
          normalize = None
  
      # 4. Pre-encoder input block
      # NOTE(kan-bayashi): Use getattr to keep the compatibility
      if getattr(args, "preencoder", None) is not None:
          preencoder_class = preencoder_choices.get_class(
              args.preencoder
          )
          preencoder = preencoder_class(**args.preencoder_conf)
          input_size = preencoder.output_size()
      else:
          preencoder = None
  
      # 4. Encoder
      encoder_class = encoder_choices.get_class(args.encoder)
      encoder = encoder_class(
          input_size=input_size, **args.encoder_conf
      )
  
      # 5. Post-encoder block
      # NOTE(kan-bayashi): Use getattr to keep the compatibility
      encoder_output_size = encoder.output_size()
      if getattr(args, "postencoder", None) is not None:
          postencoder_class = postencoder_choices.get_class(
              args.postencoder
          )
          postencoder = postencoder_class(
              input_size=encoder_output_size, **args.postencoder_conf
          )
          encoder_output_size = postencoder.output_size()
      else:
          postencoder = None
  
      if getattr(args, "deliberationencoder", None) is not None:
          deliberationencoder_class = (
              deliberationencoder_choices.get_class(
                  args.deliberationencoder
              )
          )
          deliberationencoder = deliberationencoder_class(
              input_size=encoder_output_size,
              **args.deliberationencoder_conf,
          )
          encoder_output_size = deliberationencoder.output_size()
      else:
          deliberationencoder = None
  
      if getattr(args, "postdecoder", None) is not None:
          postdecoder_class = postdecoder_choices.get_class(
              args.postdecoder
          )
          postdecoder = postdecoder_class(**args.postdecoder_conf)
          encoder_output_size = encoder_output_size
      else:
          postdecoder = None
  
      # 5. Decoder
      decoder_class = decoder_choices.get_class(args.decoder)
  
      if args.decoder == "transducer":
          decoder = decoder_class(
              vocab_size,
              embed_pad=0,
              **args.decoder_conf,
          )
  
          joint_network = JointNetwork(
              vocab_size,
              encoder.output_size(),
              decoder.dunits,
              **args.joint_net_conf,
          )
      else:
          decoder = decoder_class(
              vocab_size=vocab_size,
              encoder_output_size=encoder_output_size,
              **args.decoder_conf,
          )
  
          joint_network = None
  
      # 6. CTC
      ctc = CTC(
          odim=vocab_size,
          encoder_output_size=encoder_output_size,
          **args.ctc_conf,
      )
  
      # 7. Build model
      try:
          model_class = model_choices.get_class(args.model)
      except AttributeError:
          model_class = model_choices.get_class("espnet")
      if "transcript_token_list" in args:
          if args.transcript_token_list is not None:
              args.model_conf[
                  "transcript_token_list"
              ] = transcript_token_list
              args.model_conf["two_pass"] = args.two_pass
              args.model_conf[
                  "pre_postencoder_norm"
              ] = args.pre_postencoder_norm
      model = model_class(
          vocab_size=vocab_size,
          frontend=frontend,
          specaug=specaug,
          normalize=normalize,
          preencoder=preencoder,
          encoder=encoder,
          postencoder=postencoder,
          deliberationencoder=deliberationencoder,
          decoder=decoder,
          postdecoder=postdecoder,
          ctc=ctc,
          joint_network=joint_network,
          token_list=token_list,
          **args.model_conf,
      )
  
      # FIXME(kamo): Should be done in model?
      # 8. Initialize
      if args.init is not None:
          initialize(model, args.init)
  
      assert check_return_type(model)
      return model
  
imports: import argparse
  import logging
  from typing import Callable, Dict, Optional, Tuple
  import numpy
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.decoder.mlm_decoder import MLMDecoder
  from espnet2.asr.decoder.rnn_decoder import RNNDecoder
  from espnet2.asr.decoder.transducer_decoder import TransducerDecoder
  from espnet2.asr.decoder.transformer_decoder import DynamicConvolution2DTransformerDecoder, DynamicConvolutionTransformerDecoder, LightweightConvolution2DTransformerDecoder, LightweightConvolutionTransformerDecoder, TransformerDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.encoder.branchformer_encoder import BranchformerEncoder
  from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
  from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder
  from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder
  from espnet2.asr.encoder.hubert_encoder import FairseqHubertEncoder, FairseqHubertPretrainEncoder
  from espnet2.asr.encoder.longformer_encoder import LongformerEncoder
  from espnet2.asr.encoder.rnn_encoder import RNNEncoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder
  from espnet2.asr.encoder.wav2vec2_encoder import FairSeqWav2Vec2Encoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.frontend.default import DefaultFrontend
  from espnet2.asr.frontend.fused import FusedFrontends
  from espnet2.asr.frontend.s3prl import S3prlFrontend
  from espnet2.asr.frontend.windowing import SlidingWindow
  from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
  from espnet2.asr.postencoder.hugging_face_transformers_postencoder import HuggingFaceTransformersPostEncoder
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.preencoder.linear import LinearProjection
  from espnet2.asr.preencoder.sinc import LightweightSincConvs
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.asr.specaug.specaug import SpecAug
  from espnet2.asr_transducer.joint_network import JointNetwork
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.layers.global_mvn import GlobalMVN
  from espnet2.layers.utterance_mvn import UtteranceMVN
  from espnet2.slu.espnet_model import ESPnetSLUModel
  from espnet2.slu.postdecoder.abs_postdecoder import AbsPostDecoder
  from espnet2.slu.postdecoder.hugging_face_transformers_postdecoder import HuggingFaceTransformersPostDecoder
  from espnet2.slu.postencoder.conformer_postencoder import ConformerPostEncoder
  from espnet2.slu.postencoder.transformer_postencoder import TransformerPostEncoder
  from espnet2.tasks.asr import ASRTask
  from espnet2.text.phoneme_tokenizer import g2p_choices
  from espnet2.torch_utils.initialize import initialize
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet2.train.class_choices import ClassChoices
  from espnet2.train.preprocessor import SLUPreprocessor
  from espnet2.train.trainer import Trainer
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet2.utils.nested_dict_action import NestedDictAction
  from espnet2.utils.types import float_or_none, int_or_none, str2bool, str_or_none
label_desc: Use image node_id 5 for calling the SLUTask obj's underlying member method code with example usage: obj.build_model(cls, args) and returns: model