code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="ASR Decoding",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
  
      # Note(kamo): Use '_' instead of '-' as separator.
      # '-' is confusing if written in yaml.
      parser.add_argument(
          "--log_level",
          type=lambda x: x.upper(),
          default="INFO",
          choices=(
              "CRITICAL",
              "ERROR",
              "WARNING",
              "INFO",
              "DEBUG",
              "NOTSET",
          ),
          help="The verbose level of logging",
      )
  
      parser.add_argument("--output_dir", type=str, required=True)
      parser.add_argument(
          "--ngpu",
          type=int,
          default=0,
          help="The number of gpus. 0 indicates CPU mode",
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed"
      )
      parser.add_argument(
          "--dtype",
          default="float32",
          choices=["float16", "float32", "float64"],
          help="Data type",
      )
      parser.add_argument(
          "--num_workers",
          type=int,
          default=1,
          help="The number of workers used for DataLoader",
      )
  
      group = parser.add_argument_group("Input data related")
      group.add_argument(
          "--data_path_and_name_and_type",
          type=str2triple_str,
          required=True,
          action="append",
      )
      group.add_argument("--key_file", type=str_or_none)
      group.add_argument(
          "--allow_variable_data_keys", type=str2bool, default=False
      )
  
      group = parser.add_argument_group(
          "The model configuration related"
      )
      group.add_argument(
          "--asr_train_config",
          type=str,
          help="ASR training configuration",
      )
      group.add_argument(
          "--asr_model_file",
          type=str,
          help="ASR model parameter file",
      )
      group.add_argument(
          "--lm_train_config",
          type=str,
          help="LM training configuration",
      )
      group.add_argument(
          "--lm_file",
          type=str,
          help="LM parameter file",
      )
      group.add_argument(
          "--word_lm_train_config",
          type=str,
          help="Word LM training configuration",
      )
      group.add_argument(
          "--word_lm_file",
          type=str,
          help="Word LM parameter file",
      )
      group.add_argument(
          "--ngram_file",
          type=str,
          help="N-gram parameter file",
      )
      group.add_argument(
          "--model_tag",
          type=str,
          help="Pretrained model tag. If specify this option, *_train_config and "
          "*_file will be overwritten",
      )
      group.add_argument(
          "--enh_s2t_task",
          type=str2bool,
          default=False,
          help="Whether we are using an enhancement and ASR joint model",
      )
      group.add_argument(
          "--multi_asr",
          type=str2bool,
          default=False,
          help="Whether we are using a monolithic multi-speaker ASR model "
          "(This flag should be False if a speech separation model is used before ASR)",
      )
      group = parser.add_argument_group("Quantization related")
      group.add_argument(
          "--quantize_asr_model",
          type=str2bool,
          default=False,
          help="Apply dynamic quantization to ASR model.",
      )
      group.add_argument(
          "--quantize_lm",
          type=str2bool,
          default=False,
          help="Apply dynamic quantization to LM.",
      )
      group.add_argument(
          "--quantize_modules",
          type=str,
          nargs="*",
          default=["Linear"],
          help="""List of modules to be dynamically quantized.
          E.g.: --quantize_modules=[Linear,LSTM,GRU].
          Each specified module should be an attribute of 'torch.nn', e.g.:
          torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
      )
      group.add_argument(
          "--quantize_dtype",
          type=str,
          default="qint8",
          choices=["float16", "qint8"],
          help="Dtype for dynamic quantization.",
      )
  
      group = parser.add_argument_group("Beam-search related")
      group.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="The batch size for inference",
      )
      group.add_argument(
          "--nbest",
          type=int,
          default=1,
          help="Output N-best hypotheses",
      )
      group.add_argument(
          "--beam_size", type=int, default=20, help="Beam size"
      )
      group.add_argument(
          "--penalty", type=float, default=0.0, help="Insertion penalty"
      )
      group.add_argument(
          "--maxlenratio",
          type=float,
          default=0.0,
          help="Input length ratio to obtain max output length. "
          "If maxlenratio=0.0 (default), it uses a end-detect "
          "function "
          "to automatically find maximum hypothesis lengths."
          "If maxlenratio<0.0, its absolute value is interpreted"
          "as a constant max output length",
      )
      group.add_argument(
          "--minlenratio",
          type=float,
          default=0.0,
          help="Input length ratio to obtain min output length",
      )
      group.add_argument(
          "--ctc_weight",
          type=float,
          default=0.5,
          help="CTC weight in joint decoding",
      )
      group.add_argument(
          "--lm_weight", type=float, default=1.0, help="RNNLM weight"
      )
      group.add_argument(
          "--ngram_weight", type=float, default=0.9, help="ngram weight"
      )
      group.add_argument("--streaming", type=str2bool, default=False)
      group.add_argument(
          "--hugging_face_decoder", type=str2bool, default=False
      )
      group.add_argument(
          "--hugging_face_decoder_conf",
          type=NestedDictAction,
          default=dict(),
          help="Custom kwargs for the HF .generate()",
      )
  
      group.add_argument(
          "--transducer_conf",
          default=None,
          help="The keyword arguments for transducer beam search.",
      )
  
      group = parser.add_argument_group("Text converter related")
      group.add_argument(
          "--token_type",
          type=str_or_none,
          default=None,
          choices=["char", "bpe", None],
          help="The token type for ASR model. "
          "If not given, refers from the training args",
      )
      group.add_argument(
          "--bpemodel",
          type=str_or_none,
          default=None,
          help="The model path of sentencepiece. "
          "If not given, refers from the training args",
      )
      group.add_argument(
          "--time_sync",
          type=str2bool,
          default=False,
          help="Time synchronous beam search.",
      )
      group.add_argument(
          "--lang_prompt_token",
          type=str,
          default=None,
          help="Prompt token for mulitlingual prompting",
      )
      group.add_argument(
          "--nlp_prompt_token",
          type=str,
          default=None,
          help="Prompt token for natural language phrases as prompting",
      )
      group.add_argument(
          "--prompt_token_file",
          type=str,
          default=None,
          help="Prompt token file",
      )
      group.add_argument(
          "--normalize_length",
          type=str2bool,
          default=False,
          help="If true, best hypothesis is selected by length-normalized scores",
      )
      return parser
  
imports: import argparse
  import logging
  import sys
  from distutils.version import LooseVersion
  from itertools import groupby
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.decoder.hugging_face_transformers_decoder import get_hugging_face_model_lm_head, get_hugging_face_model_network
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet2.asr.transducer.beam_search_transducer import BeamSearchTransducer
  from espnet2.asr.transducer.beam_search_transducer import ExtendedHypothesis
  from espnet2.asr.transducer.beam_search_transducer import Hypothesis
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.asr import ASRTask
  from espnet2.tasks.enh_s2t import EnhS2TTask
  from espnet2.tasks.lm import LMTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.hugging_face_token_id_converter import HuggingFaceTokenIDConverter
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.text.whisper_token_id_converter import OpenAIWhisperTokenIDConverter
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.utils import config_argparse
  from espnet2.utils.nested_dict_action import NestedDictAction
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.batch_beam_search_online_sim import BatchBeamSearchOnlineSim
  from espnet.nets.beam_search import BeamSearch, Hypothesis
  from espnet.nets.beam_search_timesync import BeamSearchTimeSync
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.nets.scorers.length_bonus import LengthBonus
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 2 for calling a global function with example usage: get_parser() and returns: parser