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 += ["src_token_list", "token_list"]
  
      group.add_argument(
          "--token_list",
          type=str_or_none,
          default=None,
          help="A text mapping int-id to token (for target language)",
      )
      group.add_argument(
          "--src_token_list",
          type=str_or_none,
          default=None,
          help="A text mapping int-id to token (for source language)",
      )
      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"],
          help="The target text will be tokenized "
          "in the specified level token",
      )
      group.add_argument(
          "--src_token_type",
          type=str,
          default="bpe",
          choices=["bpe", "char", "word", "phn"],
          help="The source 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 (for target language)",
      )
      group.add_argument(
          "--src_bpemodel",
          type=str_or_none,
          default=None,
          help="The model file of sentencepiece (for source language)",
      )
      parser.add_argument(
          "--non_linguistic_symbols",
          type=str_or_none,
          help="non_linguistic_symbols file path",
      )
      parser.add_argument(
          "--cleaner",
          type=str_or_none,
          choices=[None, "tacotron", "jaconv", "vietnamese"],
          default=None,
          help="Apply text cleaning",
      )
      parser.add_argument(
          "--g2p",
          type=str_or_none,
          choices=g2p_choices,
          default=None,
          help="Specify g2p method if --token_type=phn",
      )
      parser.add_argument(
          "--tokenizer_encode_conf",
          type=dict,
          default=None,
          help="Tokenization encoder conf, "
          "e.g. BPE dropout: enable_sampling=True, alpha=0.1, nbest_size=-1",
      )
      parser.add_argument(
          "--src_tokenizer_encode_conf",
          type=dict,
          default=None,
          help="Src tokenization encoder conf, "
          "e.g. BPE dropout: enable_sampling=True, alpha=0.1, nbest_size=-1",
      )
  
      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.rnn_decoder import RNNDecoder
  from espnet2.asr.decoder.transformer_decoder import DynamicConvolution2DTransformerDecoder, DynamicConvolutionTransformerDecoder, LightweightConvolution2DTransformerDecoder, LightweightConvolutionTransformerDecoder, TransformerDecoder
  from espnet2.asr.discrete_asr_espnet_model import ESPnetDiscreteASRModel
  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_transformer_encoder import ContextualBlockTransformerEncoder
  from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
  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.frontend.abs_frontend import AbsFrontend
  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.mt.espnet_model import ESPnetMTModel
  from espnet2.mt.frontend.embedding import Embedding
  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 MutliTokenizerCommonPreprocessor
  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 int_or_none, str2bool, str_or_none
label_desc: Use image node_id 1 for calling the MTTask obj's underlying member method code with example usage: obj.add_task_arguments(cls, parser) without return types