code: def add_task_arguments(cls, parser: argparse.ArgumentParser):
      group = parser.add_argument_group(description="Task related")
  
      # NOTE(kamo): add_arguments(..., required=True) can't be used
      # to provide --print_config mode. Instead of it, do as
      required = parser.get_default("required")
      required += ["token_list"]
  
      group.add_argument(
          "--token_list",
          type=str_or_none,
          default=None,
          help="A text mapping int-id to token",
      )
      group.add_argument(
          "--init",
          type=lambda x: str_or_none(x.lower()),
          default=None,
          help="The initialization method",
          choices=[
              "chainer",
              "xavier_uniform",
              "xavier_normal",
              "kaiming_uniform",
              "kaiming_normal",
              None,
          ],
      )
  
      group.add_argument(
          "--input_size",
          type=int_or_none,
          default=None,
          help="The number of input dimension of the feature",
      )
  
      group.add_argument(
          "--ctc_conf",
          action=NestedDictAction,
          default=get_default_kwargs(CTC),
          help="The keyword arguments for CTC class.",
      )
  
      group = parser.add_argument_group(
          description="Preprocess related"
      )
      group.add_argument(
          "--use_preprocessor",
          type=str2bool,
          default=True,
          help="Apply preprocessing to data or not",
      )
      group.add_argument(
          "--token_type",
          type=str,
          default="bpe",
          choices=[
              "bpe",
              "char",
              "word",
              "phn",
              "hugging_face",
              "whisper_en",
              "whisper_multilingual",
          ],
          help="The text will be tokenized in the specified level token",
      )
      group.add_argument(
          "--bpemodel",
          type=str_or_none,
          default=None,
          help="The model file of sentencepiece",
      )
      parser.add_argument(
          "--non_linguistic_symbols",
          type=str_or_none,
          help="non_linguistic_symbols file path",
      )
      group.add_argument(
          "--cleaner",
          type=str_or_none,
          choices=[
              None,
              "tacotron",
              "jaconv",
              "vietnamese",
              "whisper_en",
              "whisper_basic",
          ],
          default=None,
          help="Apply text cleaning",
      )
      group.add_argument(
          "--g2p",
          type=str_or_none,
          choices=g2p_choices,
          default=None,
          help="Specify g2p method if --token_type=phn",
      )
      group.add_argument(
          "--speech_volume_normalize",
          type=float_or_none,
          default=None,
          help="Scale the maximum amplitude to the given value.",
      )
      group.add_argument(
          "--rir_scp",
          type=str_or_none,
          default=None,
          help="The file path of rir scp file.",
      )
      group.add_argument(
          "--rir_apply_prob",
          type=float,
          default=1.0,
          help="THe probability for applying RIR convolution.",
      )
      group.add_argument(
          "--noise_scp",
          type=str_or_none,
          default=None,
          help="The file path of noise scp file.",
      )
      group.add_argument(
          "--noise_apply_prob",
          type=float,
          default=1.0,
          help="The probability applying Noise adding.",
      )
      group.add_argument(
          "--noise_db_range",
          type=str,
          default="13_15",
          help="The range of noise decibel level.",
      )
      group.add_argument(
          "--short_noise_thres",
          type=float,
          default=0.5,
          help="If len(noise) / len(speech) is smaller than this threshold during "
          "dynamic mixing, a warning will be displayed.",
      )
  
      for class_choices in cls.class_choices_list:
          # Append --<name> and --<name>_conf.
          # e.g. --encoder and --encoder_conf
          class_choices.add_arguments(group)
  
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.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.decoder.hugging_face_transformers_decoder import HuggingFaceTransformersDecoder
  from espnet2.asr.decoder.mlm_decoder import MLMDecoder
  from espnet2.asr.decoder.rnn_decoder import RNNDecoder
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet2.asr.decoder.transformer_decoder import DynamicConvolution2DTransformerDecoder, DynamicConvolutionTransformerDecoder, LightweightConvolution2DTransformerDecoder, LightweightConvolutionTransformerDecoder, TransformerDecoder
  from espnet2.asr.decoder.whisper_decoder import OpenAIWhisperDecoder
  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.e_branchformer_encoder import EBranchformerEncoder
  from espnet2.asr.encoder.hubert_encoder import FairseqHubertEncoder, FairseqHubertPretrainEncoder, TorchAudioHuBERTPretrainEncoder
  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.transformer_encoder_multispkr import TransformerEncoder
  from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder
  from espnet2.asr.encoder.wav2vec2_encoder import FairSeqWav2Vec2Encoder
  from espnet2.asr.encoder.whisper_encoder import OpenAIWhisperEncoder
  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.whisper import WhisperFrontend
  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.layers.abs_normalize import AbsNormalize
  from espnet2.layers.global_mvn import GlobalMVN
  from espnet2.layers.utterance_mvn import UtteranceMVN
  from espnet2.s2t.espnet_model import ESPnetS2TModel
  from espnet2.tasks.abs_task import AbsTask
  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.collate_fn import CommonCollateFn
  from espnet2.train.preprocessor import AbsPreprocessor, S2TPreprocessor
  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 1 for calling the S2TTask obj's underlying member method code with example usage: obj.add_task_arguments(cls, parser) without return types