code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="S2T 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(
          "--s2t_train_config",
          type=str,
          help="S2T training configuration",
      )
      group.add_argument(
          "--s2t_model_file",
          type=str,
          help="S2T model 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_s2t_model",
          type=str2bool,
          default=False,
          help="Apply dynamic quantization to S2T model.",
      )
      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 = parser.add_argument_group("Text converter related")
      group.add_argument(
          "--token_type",
          type=str_or_none,
          default=None,
          choices=["char", "bpe", "word", None],
          help="The token type for S2T 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(
          "--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 pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  import torch.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.s2t import S2TTask
  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.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.nets.scorer_interface import BatchScorerInterface
  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