code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="ST 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.add_argument(
          "--sim_chunk_length",
          type=int,
          default=0,
          help="The length of one chunk, to which speech will be "
          "divided for evalution of streaming processing.",
      )
  
      group = parser.add_argument_group(
          "The model configuration related"
      )
      group.add_argument("--st_train_config", type=str, required=True)
      group.add_argument("--st_model_file", type=str, required=True)
      group.add_argument("--lm_train_config", type=str)
      group.add_argument("--lm_file", type=str)
      group.add_argument("--word_lm_train_config", type=str)
      group.add_argument("--word_lm_file", type=str)
  
      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",
      )
      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=0.0, help="CTC weight"
      )
      group.add_argument(
          "--lm_weight", type=float, default=1.0, help="RNNLM weight"
      )
      group.add_argument(
          "--disable_repetition_detection", type=str2bool, default=False
      )
  
      group.add_argument(
          "--encoded_feat_length_limit",
          type=int,
          default=0,
          help="Limit the lengths of the encoded feature"
          "to input to the decoder.",
      )
      group.add_argument(
          "--decoder_text_length_limit",
          type=int,
          default=0,
          help="Limit the lengths of the text"
          "to input to the decoder.",
      )
  
      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 ST 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(
          "--incremental_decode",
          type=str2bool,
          default=False,
          help="Time synchronous beam search.",
      )
      group.add_argument(
          "--blank_penalty",
          type=float,
          default=1.0,
          help="Time synchronous beam search.",
      )
      group.add_argument(
          "--hold_n",
          type=int,
          default=0,
          help="Time synchronous beam search.",
      )
      group.add_argument(
          "--transducer_conf",
          default=None,
          help="The keyword arguments for transducer beam search.",
      )
      group.add_argument(
          "--hugging_face_decoder", type=str2bool, default=False
      )
      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 math
  import sys
  from pathlib import Path
  from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder
  from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder
  from espnet2.asr.frontend.s3prl import S3prlFrontend
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  from espnet2.tasks.st import STTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  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_online import BatchBeamSearchOnline
  from espnet.nets.beam_search import Hypothesis
  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