code: def main(argv: Optional[List[str]] = None) -> None:
      """
      Handle command line arguments and gets 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 = argparse.ArgumentParser(
          description="Takes an input feature file and converts it to another "
          "format. Formats are determined automatically from file "
          "extensions.",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
      parser.add_argument(
          "infile",
          help="input feature file (ends in .arff, .csv, "
          ".jsonlines, .libsvm, .ndj, or .tsv)",
      )
      parser.add_argument(
          "outfile",
          help="output feature file (ends in .arff, .csv, "
          ".jsonlines, .libsvm, .ndj, or .tsv)",
      )
      parser.add_argument(
          "-i",
          "--id_col",
          help="Name of the column which contains the instance "
          "IDs in ARFF, CSV, or TSV files.",
          default="id",
      )
      label_group = parser.add_mutually_exclusive_group(required=False)
      label_group.add_argument(
          "-l",
          "--label_col",
          help="Name of the column which contains the "
          "class labels in ARFF, CSV, or TSV files. "
          "For ARFF files, this must be the final "
          "column to count as the label.",
          default="y",
      )
      label_group.add_argument(
          "--no_labels",
          action="store_true",
          default=False,
          help="Used to indicate that the input data has " "no labels.",
      )
      parser.add_argument(
          "-q",
          "--quiet",
          help='Suppress printing of "Loading..." messages.',
          action="store_true",
      )
      parser.add_argument(
          "--arff_regression",
          help="Create ARFF files for regression, not "
          "classification.",
          action="store_true",
      )
      parser.add_argument(
          "--arff_relation",
          help="Relation name to use for ARFF file.",
          default="skll_relation",
      )
      parser.add_argument(
          "--reuse_libsvm_map",
          help="If you want to output multiple files that use "
          "the same mapping from labels and features to "
          "numbers when writing libsvm files, you can "
          "specify an existing .libsvm file to reuse the "
          "mapping from.",
          type=argparse.FileType("rb"),
      )
      parser.add_argument(
          "--version",
          action="version",
          version=f"%(prog)s {__version__}",
      )
      args = parser.parse_args(argv)
  
      # 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"
      )
      logger = logging.getLogger(__name__)
  
      # make sure the input file extension is one we can process
      input_extension = Path(args.infile).suffix.lower()
      output_extension = Path(args.outfile).suffix.lower()
  
      if input_extension not in EXT_TO_READER:
          logger.error(
              "Input file must be in either .arff, .csv, .jsonlines, "
              ".libsvm, .ndj, or .tsv format. You specified: "
              f"{input_extension}"
          )
          sys.exit(1)
  
      # Build feature and label vectorizers from existing libsvm file if asked
      feat_map: Dict[str, int]
      label_map: Optional[Dict[str, int]]
      if args.reuse_libsvm_map and output_extension == ".libsvm":
          feat_map = {}
          label_map = {}
          for line in args.reuse_libsvm_map:
              line = UnicodeDammit(
                  line, ["utf-8", "windows-1252"]
              ).unicode_markup
              if "#" not in line:
                  logger.error(
                      "The LibSVM file you want to reuse the map from "
                      "was not created by SKLL and does not actually "
                      "contain the necessary mapping info."
                  )
                  sys.exit(1)
              comments = line.split("#")[1]
              _, label_map_str, feat_map_str = comments.split("|")
              feat_map.update(
                  _pair_to_dict_tuple(pair)
                  for pair in feat_map_str.strip().split()
              )
              label_map.update(
                  _pair_to_dict_tuple(pair)
                  for pair in label_map_str.strip().split()
              )
          feat_vectorizer = DictVectorizer()
          feat_vectorizer.fit([{name: 1} for name in feat_map])
          feat_vectorizer.vocabulary_ = feat_map
      else:
          feat_vectorizer = None
          label_map = None
  
      label_col = None if args.no_labels else args.label_col
  
      # Iterate through input file and collect the information we need
      reader = EXT_TO_READER[input_extension](
          args.infile,
          quiet=args.quiet,
          label_col=label_col,
          id_col=args.id_col,
      )
      feature_set = reader.read()
      # write out the file in the requested output format
      writer_type = EXT_TO_WRITER[output_extension]
      writer_args = {"quiet": args.quiet}
      if writer_type is CSVWriter or writer_type is TSVWriter:
          writer_args["label_col"] = label_col
          writer_args["id_col"] = args.id_col
      elif writer_type is ARFFWriter:
          writer_args["label_col"] = label_col
          writer_args["id_col"] = args.id_col
          writer_args["regression"] = args.arff_regression
          writer_args["relation"] = args.arff_relation
      elif writer_type is LibSVMWriter:
          writer_args["label_map"] = label_map
      writer = writer_type(args.outfile, feature_set, **writer_args)
      writer.write()
  
file_path: skll/skll/utils/commandline/skll_convert.py
imports: import argparse
  import logging
  import sys
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  from bs4 import UnicodeDammit
  from skll.data.dict_vectorizer import DictVectorizer
  from skll.data.readers import EXT_TO_READER
  from skll.data.writers import EXT_TO_WRITER, ARFFWriter, CSVWriter, LibSVMWriter, TSVWriter
  from skll.version import __version__
label_desc: Use image node_id 2 for calling a global function with example usage: main(argv) without return types