code: def _parse_args(args):
      parser = argparse.ArgumentParser(
          description=__doc__,
      )
      parser.add_argument(
          "--debug",
          action="store_true",
          help="Enable debug behavior. Each epoch will end with just one batch.",
      )
      group = parser.add_argument_group("Model Options")
      group.add_argument(
          "--num-speakers",
          required=True,
          type=int,
          help="The number of speakers.",
      )
      group = parser.add_argument_group("Dataset Options")
      group.add_argument(
          "--sample-rate",
          required=True,
          type=int,
          help="Sample rate of audio files in the given dataset.",
      )
      group.add_argument(
          "--dataset",
          default="wsj0mix",
          choices=["wsj0mix"],
          help='Dataset type. (default: "wsj0mix")',
      )
      group.add_argument(
          "--dataset-dir",
          required=True,
          type=pathlib.Path,
          help=(
              "Directory where dataset is found. "
              'If the dataset type is "wsj9mix", then this is the directory where '
              '"cv", "tt" and "tr" subdirectories are found.'
          ),
      )
      group = parser.add_argument_group("Save Options")
      group.add_argument(
          "--save-dir",
          required=True,
          type=pathlib.Path,
          help=(
              "Directory where the checkpoints and logs are saved. "
              "Though, only the worker 0 saves checkpoint data, "
              "all the worker processes must have access to the directory."
          ),
      )
      group = parser.add_argument_group("Dataloader Options")
      group.add_argument(
          "--batch-size",
          type=int,
          help="Batch size. (default: 16 // world_size)",
      )
      group = parser.add_argument_group("Training Options")
      group.add_argument(
          "--epochs",
          metavar="NUM_EPOCHS",
          default=100,
          type=int,
          help="The number of epochs to train. (default: 100)",
      )
      group.add_argument(
          "--learning-rate",
          default=1e-3,
          type=float,
          help="Initial learning rate. (default: 1e-3)",
      )
      group.add_argument(
          "--grad-clip",
          metavar="CLIP_VALUE",
          default=5.0,
          type=float,
          help="Gradient clip value (l2 norm). (default: 5.0)",
      )
      group.add_argument(
          "--resume",
          metavar="CHECKPOINT_PATH",
          help="Previous checkpoint file from which the training is resumed.",
      )
  
      args = parser.parse_args(args)
  
      # Delaing the default value initialization until parse_args is done because
      # if `--help` is given, distributed training is not enabled.
      if args.batch_size is None:
          args.batch_size = 16 // torch.distributed.get_world_size()
  
      return args
  
file_path: audio/examples/source_separation/conv_tasnet/train.py
imports: import argparse
  import pathlib
  import time
  import conv_tasnet
  import torch
  import torchaudio
  import torchaudio.models
  from utils import dist_utils
  from utils.dataset import utils
label_desc: Use image node_id 1 for calling a global function with example usage: _parse_args(args) and returns: args