code: def build_model(cls, args: argparse.Namespace) -> ESPnetASVSpoofModel:
      assert check_argument_types()
  
      # 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
      )
      encoder_output_size = encoder.output_size()
  
      # 5. Decoder
      decoder_class = decoder_choices.get_class(args.decoder)
  
      decoder = decoder_class(
          encoder_output_size=encoder_output_size,
          **args.decoder_conf,
      )
  
      # 6. Loss definition
      losses = {}
      if getattr(args, "losses", None) is not None:
          # This check is for the compatibility when load models
          # that packed by older version
          for ctr in args.losses:
              if "softmax" in ctr["name"]:
                  loss = losses_choices.get_class(ctr["name"])(
                      enc_dim=encoder_output_size, **ctr["conf"]
                  )
              else:
                  loss = losses_choices.get_class(ctr["name"])(
                      **ctr["conf"]
                  )
              losses[ctr["name"]] = loss
  
      # 7. Build model
      model = ESPnetASVSpoofModel(
          frontend=frontend,
          specaug=specaug,
          normalize=normalize,
          preencoder=preencoder,
          encoder=encoder,
          decoder=decoder,
          losses=losses,
      )
  
      # 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, Collection, Dict, List, Optional, Tuple
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  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.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.asvspoof.decoder.abs_decoder import AbsDecoder
  from espnet2.asvspoof.decoder.linear_decoder import LinearDecoder
  from espnet2.asvspoof.espnet_model import ESPnetASVSpoofModel
  from espnet2.asvspoof.loss.abs_loss import AbsASVSpoofLoss
  from espnet2.asvspoof.loss.am_softmax_loss import ASVSpoofAMSoftmaxLoss
  from espnet2.asvspoof.loss.binary_loss import ASVSpoofBinaryLoss
  from espnet2.asvspoof.loss.oc_softmax_loss import ASVSpoofOCSoftmaxLoss
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.layers.global_mvn import GlobalMVN
  from espnet2.layers.utterance_mvn import UtteranceMVN
  from espnet2.tasks.abs_task import AbsTask
  from espnet2.torch_utils.initialize import initialize
  from espnet2.train.class_choices import ClassChoices
  from espnet2.train.collate_fn import CommonCollateFn
  from espnet2.train.preprocessor import CommonPreprocessor
  from espnet2.train.trainer import Trainer
  from espnet2.utils.nested_dict_action import NestedDictAction
  from espnet2.utils.types import int_or_none, str2bool, str_or_none
label_desc: Use image node_id 6 for calling the ASVSpoofTask obj's underlying member method code with example usage: obj.build_model(cls, args) and returns: model