code: def get_parser():
      """Get argument parser."""
      parser = config_argparse.ArgumentParser(
          description="S2ST inference",
          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,
          help="The path of output directory",
      )
      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",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="The batch size for inference",
      )
  
      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(
          "--train_config",
          type=str,
          help="Training configuration file",
      )
      group.add_argument(
          "--model_file",
          type=str,
          help="Model parameter file",
      )
  
      group = parser.add_argument_group("Decoding related")
      group.add_argument(
          "--maxlenratio",
          type=float,
          default=10.0,
          help="Maximum length ratio in decoding",
      )
      group.add_argument(
          "--minlenratio",
          type=float,
          default=0.0,
          help="Minimum length ratio in decoding",
      )
      group.add_argument(
          "--st_subtask_maxlenratio",
          type=float,
          default=1.5,
          help="Maximum length ratio in decoding",
      )
      group.add_argument(
          "--st_subtask_minlenratio",
          type=float,
          default=0.1,
          help="Minimum length ratio in decoding",
      )
  
      group = parser.add_argument_group(
          "Spectrogram-based generation related"
      )
      group.add_argument(
          "--threshold",
          type=float,
          default=0.5,
          help="Threshold value in decoding",
      )
      group.add_argument(
          "--use_att_constraint",
          type=str2bool,
          default=False,
          help="Whether to use attention constraint",
      )
      group.add_argument(
          "--backward_window",
          type=int,
          default=1,
          help="Backward window value in attention constraint",
      )
      group.add_argument(
          "--forward_window",
          type=int,
          default=3,
          help="Forward window value in attention constraint",
      )
      group.add_argument(
          "--use_teacher_forcing",
          type=str2bool,
          default=False,
          help="Whether to use teacher forcing",
      )
      group.add_argument(
          "--always_fix_seed",
          type=str2bool,
          default=False,
          help="Whether to always fix seed",
      )
  
      group = parser.add_argument_group(
          "Beam-search (discrete unit/multi-pass) related"
      )
      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(
          "--st_subtask_nbest",
          type=int,
          default=1,
          help="Output N-best hypotheses for st subtask",
      )
      group.add_argument(
          "--st_subtask_beam_size",
          type=int,
          default=5,
          help="Beam size for st subtask",
      )
      group.add_argument(
          "--st_subtask_penalty",
          type=float,
          default=0.0,
          help="Insertion penalty for st subtask",
      )
  
      group = parser.add_argument_group("Vocoder related")
      group.add_argument(
          "--vocoder_config",
          type=str_or_none,
          help="Vocoder configuration file",
      )
      group.add_argument(
          "--vocoder_file",
          type=str_or_none,
          help="Vocoder parameter file",
      )
      group.add_argument(
          "--vocoder_tag",
          type=str,
          help="Pretrained vocoder tag. If specify this option, vocoder_config and "
          "vocoder_file will be overwritten",
      )
  
      group = parser.add_argument_group("Text converter related")
      group.add_argument(
          "--st_subtask_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(
          "--st_subtask_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 shutil
  import sys
  import time
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import soundfile
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.fileio.npy_scp import NpyScpWriter
  from espnet2.tasks.s2st import S2STTask
  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 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.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