code: def _parse_args():
      parser = argparse.ArgumentParser(
          description=__doc__,
          formatter_class=argparse.RawTextHelpFormatter,
      )
      parser.add_argument(
          "--librispeech_path",
          type=str,
          help="folder where LibriSpeech is stored",
          default="./librispeech",
      )
      parser.add_argument(
          "--split",
          type=str,
          help="LibriSpeech dataset split",
          choices=[
              "dev-clean",
              "dev-other",
              "test-clean",
              "test-other",
          ],
          default="test-other",
      )
      parser.add_argument(
          "--model",
          type=str,
          default="./cpu_jit.pt",
          help="pretrained ASR model using CTC loss",
      )
      parser.add_argument(
          "--bpe-model",
          type=str,
          default="./bpe.model",
          help="bpe file for pretrained ASR model",
      )
      parser.add_argument(
          "--nbest",
          type=int,
          default=10,
          help="number of best hypotheses to return",
      )
      parser.add_argument(
          "--beam-size",
          type=int,
          default=10,
          help="beam size for determining number of hypotheses to store",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=4,
          help="batch size for decoding",
      )
      parser.add_argument(
          "--blank-skip-threshold",
          type=float,
          default=0.95,
          help="skip frames where prob_blank > 0.95, https://ieeexplore.ieee.org/document/7736093",
      )
      parser.add_argument(
          "--debug",
          action="store_true",
          help="whether to use debug level for logging",
      )
      # cpu decoder specific parameters
      parser.add_argument(
          "--using-cpu-decoder",
          action="store_true",
          help="whether to use flashlight cpu ctc decoder",
      )
      parser.add_argument(
          "--beam-threshold",
          type=int,
          default=50,
          help="beam threshold for pruning hypotheses",
      )
      parser.add_argument(
          "--beam-size-token",
          type=int,
          default=None,
          help="number of tokens to consider at each beam search step",
      )
      return parser.parse_args()
  
file_path: audio/examples/asr/librispeech_cuda_ctc_decoder/inference.py
imports: import argparse
  import logging
  import time
  import sentencepiece
  import torch
  import torchaudio
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from torchaudio.models.decoder import ctc_decoder, cuda_ctc_decoder
label_desc: Use image node_id 3 for calling a global function with example usage: _parse_args() and returns: parser