code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="LM Decoding (conditional generation)",
          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(
          "--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 = parser.add_argument_group("Quantization related")
      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="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(
          "--maxlen",
          type=int,
          default=100,
          help="Maximum output length",
      )
      group.add_argument(
          "--minlen",
          type=int,
          default=1,
          help="Minimum output length",
      )
      group.add_argument(
          "--ngram_weight", type=float, default=0.9, help="ngram weight"
      )
  
      group = parser.add_argument_group("Text converter related")
      group.add_argument(
          "--token_type",
          type=str_or_none,
          default=None,
          choices=["char", "word", "bpe", None],
          help="Token type for LM. If not given, refers from the train args",
      )
      group.add_argument(
          "--bpemodel",
          type=str_or_none,
          default=None,
          help="Model path for sentencepiece. If not given, refers from the train args",
      )
  
      return parser
  
imports: import argparse
  import logging
  import sys
  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.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  from espnet2.text.build_tokenizer import build_tokenizer
  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.types import str2bool, str2triple_str, str_or_none
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.beam_search import BeamSearch, Hypothesis
  from espnet.nets.scorer_interface import BatchScorerInterface
  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