code: def get_args():
      import argparse
  
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--vocab_size",
          default=100,
          type=int,
          help="Quantization codebook vocabulary size",
      )
      parser.add_argument(
          "--dense_model",
          default="hubert-base-ls960",
          help="Dense model to be used",
      )
      parser.add_argument(
          "--quantizer_model",
          default="kmeans",
          help="Quantizer model to be used",
      )
  
      parser.add_argument(
          "--manifest",
          required=True,
          help="Path to the dataset manifest file",
      )
      parser.add_argument(
          "--output",
          required=True,
          help="Path to the output files. Pseudo-units and duration (if requested) streams will be stored in files with .units and .durations suffixes, respectively",
      )
      parser.add_argument(
          "--deduplicate",
          action="store_true",
          help="if set, consecutive repeats of the same pseudo-unit are collapsed ('1 2 2 2 3' becomes '1 2 3')",
      )
      parser.add_argument(
          "--durations",
          action="store_true",
          help="if set, the token durations stream is produced",
      )
      parser.add_argument(
          "--f0s",
          action="store_true",
          help="if set, the F0 stream is produced",
      )
      parser.add_argument(
          "--preserve_name",
          action="store_true",
          help="If set, the transcript contains names of the audio files",
      )
      parser.add_argument(
          "--separator",
          type=str,
          default=" ",
          help="Separator between pseudo-unit tokens",
      )
  
      parser.add_argument("--distributed_port", type=int, default=58554)
  
      args = parser.parse_args()
      logger.info(f"Launched with args: {args}")
  
      return args
  
file_path: textlesslib/tools/distributed_transcribe/transcribe.py
imports: import torch.distributed
  import torch
  import pathlib
  from data_handler import ManifestDataset
  from distributed import init_distributed_context
  import logging
  from textless.data.speech_encoder import SpeechEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: get_args() and returns: args