code: def _parse_args():
      parser = argparse.ArgumentParser(
          description=__doc__,
          formatter_class=argparse.RawTextHelpFormatter,
      )
      parser.add_argument(
          "--librispeech-path",
          type=str,
          help="Folder where LibriSpeech dataset is stored.",
      )
      parser.add_argument(
          "--split",
          type=str,
          choices=[
              "dev-clean",
              "dev-other",
              "test-clean",
              "test-other",
          ],
          help="LibriSpeech dataset split. (Default: 'test-clean')",
          default="test-clean",
      )
      parser.add_argument(
          "--checkpoint",
          type=str,
          help="The checkpoint path of fine-tuned HuBERTPretrainModel.",
      )
      parser.add_argument(
          "--use-lm",
          action="store_true",
          help="Whether to use language model for decoding.",
      )
      parser.add_argument(
          "--nbest",
          type=int,
          default=1,
          help="Number of best hypotheses to return.",
      )
      parser.add_argument(
          "--beam-size",
          type=int,
          default=1500,
          help="Beam size for determining number of hypotheses to store. (Default: 1500)",
      )
      parser.add_argument(
          "--beam-size-token",
          type=int,
          default=29,
          help="Number of tokens to consider at each beam search step. (Default: 29)",
      )
      parser.add_argument(
          "--beam-threshold",
          type=int,
          default=100,
          help="Beam threshold for pruning hypotheses. (Default: 100)",
      )
      parser.add_argument(
          "--lm-weight",
          type=float,
          default=2.46,
          help="Languge model weight in decoding. (Default: 2.46)",
      )
      parser.add_argument(
          "--word-score",
          type=float,
          default=-0.59,
          help="Word insertion score in decoding. (Default: -0.59)",
      )
      parser.add_argument(
          "--unk-score",
          type=float,
          default=float("-inf"),
          help="Unknown word insertion score. (Default: -inf)",
      )
      parser.add_argument(
          "--sil-score",
          type=float,
          default=0,
          help="Silence insertion score. (Default: 0)",
      )
      parser.add_argument(
          "--use-gpu",
          action="store_true",
          help="Whether to use GPU for decoding.",
      )
      parser.add_argument(
          "--debug",
          action="store_true",
          help="Whether to use debug level for logging.",
      )
      return parser.parse_args()
  
file_path: audio/examples/hubert/evaluate.py
imports: import argparse
  import logging
  from typing import Dict, List
  import torch
  import torch.nn.functional
  import torchaudio
  from torchaudio.models.decoder import ctc_decoder, CTCDecoder, download_pretrained_files
  from utils import _get_id2label
label_desc: Use image node_id 5 for calling a global function with example usage: _parse_args() and returns: parser