code: def main():
      parser = argparse.ArgumentParser(description="")
      parser.add_argument(
          "--en2fr", required=True, help="path to en2fr model"
      )
      parser.add_argument(
          "--fr2en",
          required=True,
          help="path to fr2en mixture of experts model",
      )
      parser.add_argument(
          "--user-dir",
          help="path to fairseq examples/translation_moe/src directory",
      )
      parser.add_argument(
          "--num-experts",
          type=int,
          default=10,
          help="(keep at 10 unless using a different model)",
      )
      parser.add_argument(
          "files",
          nargs="*",
          default=["-"],
          help='input files to paraphrase; "-" for stdin',
      )
      args = parser.parse_args()
  
      if args.user_dir is None:
          args.user_dir = os.path.join(
              os.path.dirname(
                  os.path.dirname(os.path.abspath(__file__))
              ),  # examples/
              "translation_moe",
              "src",
          )
          if os.path.exists(args.user_dir):
              logging.info("found user_dir:" + args.user_dir)
          else:
              raise RuntimeError(
                  "cannot find fairseq examples/translation_moe/src "
                  "(tried looking here: {})".format(args.user_dir)
              )
  
      logging.info("loading en2fr model from:" + args.en2fr)
      en2fr = TransformerModel.from_pretrained(
          model_name_or_path=args.en2fr,
          tokenizer="moses",
          bpe="sentencepiece",
      ).eval()
  
      logging.info("loading fr2en model from:" + args.fr2en)
      fr2en = TransformerModel.from_pretrained(
          model_name_or_path=args.fr2en,
          tokenizer="moses",
          bpe="sentencepiece",
          user_dir=args.user_dir,
          task="translation_moe",
      ).eval()
  
      def gen_paraphrases(en):
          fr = en2fr.translate(en)
          return [
              fr2en.translate(fr, inference_step_args={"expert": i})
              for i in range(args.num_experts)
          ]
  
      logging.info("Type the input sentence and press return:")
      for line in fileinput.input(args.files):
          line = line.strip()
          if len(line) == 0:
              continue
          for paraphrase in gen_paraphrases(line):
              print(paraphrase)
  
imports: import argparse
  import fileinput
  import logging
  import os
  import sys
  from fairseq.models.transformer import TransformerModel
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: unknown