code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "output_pt",
          default=None,
          help="Where to write the converted PT file",
      )
      parser.add_argument(
          "input_vec", default=None, help="Unconverted vectors file"
      )
      parser.add_argument(
          "max_vocab",
          type=int,
          default=-1,
          nargs="?",
          help="How many vectors to convert.  -1 means convert them all",
      )
      args = parser.parse_args()
  
      if os.path.exists(args.output_pt):
          print(
              "Not overwriting existing pretrain file in %s"
              % args.output_pt
          )
  
      if args.input_vec.endswith(".csv"):
          pt = pretrain.Pretrain(
              args.output_pt,
              max_vocab=args.max_vocab,
              csv_filename=args.input_vec,
          )
      else:
          pt = pretrain.Pretrain(
              args.output_pt, args.input_vec, max_vocab=args.max_vocab
          )
      print("Pretrain is of size {}".format(len(pt.vocab)))
  
imports: import argparse
  import os
  import sys
  from stanza.models.common import pretrain
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types