code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="Frontend 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)
      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(
          "--fs",
          type=humanfriendly_or_none,
          default=8000,
          help="Sampling rate",
      )
      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("Output data related")
  
      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.add_argument(
          "--model_tag",
          type=str,
          help="Pretrained model tag. If specify this option, train_config and "
          "model_file will be overwritten",
      )
      group.add_argument(
          "--inference_config",
          type=str_or_none,
          default=None,
          help="Optional configuration file for overwriting enh model attributes "
          "during inference",
      )
      group.add_argument(
          "--enh_s2t_task",
          type=str2bool,
          default=False,
          help="enhancement and asr joint model",
      )
  
      group = parser.add_argument_group("Data loading related")
      group.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="The batch size for inference",
      )
      group = parser.add_argument_group("SeparateSpeech related")
      group.add_argument(
          "--ref_channel",
          type=int,
          default=None,
          help="If not None, this will overwrite the ref_channel defined in the "
          "separator module (for multi-channel speech processing)",
      )
  
      return parser
  
imports: import argparse
  import logging
  import math
  import sys
  from itertools import chain
  from pathlib import Path
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import humanfriendly
  import numpy
  import torch
  import torch_complex
  import yaml
  from typeguard import check_argument_types
  from espnet2.bin.enh_inference import build_model_from_args_and_file, get_train_config, recursive_dict_update
  from espnet2.fileio.sound_scp import SoundScpWriter
  from espnet2.tasks.enh import EnhancementTask
  from espnet2.tasks.enh_s2t import EnhS2TTask
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 3 for calling a global function with example usage: get_parser() and returns: parser