code: def main(argv: Optional[List[str]] = None) -> None:
      """
      Handle command line arguments and get things started.
  
      Parameters
      ----------
      argv : Optional[List[str]], default=None
          List of arguments, as if specified on the command-line.
          If ``None``, ``sys.argv[1:]`` is used instead.
      """
      # Get command line arguments
      parser = ArgumentParser(
          description="Runs the scikit-learn experiments "
          "in a given config file. If Grid Map "
          "is installed, jobs will automatically"
          " be created and run on a "
          "DRMAA-compatible cluster.",
          formatter_class=ArgumentDefaultsHelpFormatter,
          conflict_handler="resolve",
      )
      parser.add_argument(
          "config_file",
          help="Configuration file describing the task to run.",
          nargs="+",
      )
      parser.add_argument(
          "-a",
          "--ablation",
          help="Runs an ablation study where repeated "
          "experiments are conducted where the specified "
          "number of features in each featureset in the "
          "configuration file are held out.",
          type=int,
          default=0,
          metavar="NUM_FEATURES",
      )
      parser.add_argument(
          "-A",
          "--ablation_all",
          help="Runs an ablation study where repeated "
          "experiments are conducted with all combinations "
          "of features in each featureset in the "
          "configuration file. Overrides --ablation "
          "setting.",
          action="store_true",
      )
      parser.add_argument(
          "-k",
          "--keep_models",
          help="If trained models already exists, re-use them "
          "instead of overwriting them.",
          action="store_true",
      )
      parser.add_argument(
          "-l",
          "--local",
          help="Do not use the Grid Engine for running jobs and"
          " just run everything sequentially on the local "
          "machine. ",
          action="store_true",
      )
      parser.add_argument(
          "-m",
          "--machines",
          help="comma-separated list of machines to add to the "
          "gridmap whitelist (if not specified, all "
          "available machines are used). Note that full "
          "names must be specified, e.g., "
          '"nlp.research.ets.org"',
          default=None,
      )
      parser.add_argument(
          "-q",
          "--queue",
          help="Use this queue for gridmap.",
          default="all.q",
      )
      parser.add_argument(
          "-r",
          "--resume",
          help="If result files already exist for an experiment"
          " do not overwrite them. This is very useful "
          "when doing a large ablation experiment and part"
          " of it crashes.",
          action="store_true",
      )
      parser.add_argument(
          "-v",
          "--verbose",
          help="Include debug information in the logging output.",
          default=False,
          action="store_true",
      )
      parser.add_argument(
          "--version",
          action="version",
          version=f"%(prog)s {__version__}",
      )
      args = parser.parse_args(argv)
  
      # Default logging level is INFO unless we are being verbose
      log_level = logging.DEBUG if args.verbose else logging.INFO
  
      # Make warnings from built-in warnings module get formatted more nicely
      logging.captureWarnings(True)
      logging.basicConfig(
          format="%(asctime)s - %(name)s - %(levelname)s - "
          "%(message)s",
          level=log_level,
      )
  
      machines = None
      if args.machines:
          machines = args.machines.split(",")
  
      ablation = args.ablation
      if args.ablation_all:
          ablation = None
  
      # Run each config file sequentially
      for config_file in args.config_file:
          run_configuration(
              config_file,
              local=args.local,
              overwrite=not args.keep_models,
              queue=args.queue,
              hosts=machines,
              ablation=ablation,
              resume=args.resume,
              log_level=log_level,
          )
  
imports: import logging
  from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
  from typing import List, Optional
  from skll.experiments import run_configuration
  from skll.version import __version__
label_desc: Use image node_id 1 for calling a global function with example usage: main(argv) without return types