code: def match_target_hypo(args, target_outfile, hypo_outfile):
      """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
      if len(args.weight1) == 1:
          res = score_target_hypo(
              args,
              args.weight1[0],
              args.weight2[0],
              args.weight3[0],
              args.lenpen[0],
              target_outfile,
              hypo_outfile,
              True,
              args.normalize,
          )
          rerank_scores = [res]
      else:
          print("launching pool")
          with Pool(32) as p:
              rerank_scores = p.starmap(
                  score_target_hypo,
                  [
                      (
                          args,
                          args.weight1[i],
                          args.weight2[i],
                          args.weight3[i],
                          args.lenpen[i],
                          target_outfile,
                          hypo_outfile,
                          False,
                          args.normalize,
                      )
                      for i in range(len(args.weight1))
                  ],
              )
  
      if len(rerank_scores) > 1:
          best_index = np.argmax(rerank_scores)
          best_score = rerank_scores[best_index]
          print("best score", best_score)
          print("best lenpen", args.lenpen[best_index])
          print("best weight1", args.weight1[best_index])
          print("best weight2", args.weight2[best_index])
          print("best weight3", args.weight3[best_index])
          return (
              args.lenpen[best_index],
              args.weight1[best_index],
              args.weight2[best_index],
              args.weight3[best_index],
              best_score,
          )
  
      else:
          return (
              args.lenpen[0],
              args.weight1[0],
              args.weight2[0],
              args.weight3[0],
              rerank_scores[0],
          )
  
file_path: fairseq/examples/noisychannel/rerank.py
imports: import math
  from multiprocessing import Pool
  import numpy
  from fairseq import options
  from fairseq.data import dictionary
  from fairseq.scoring import bleu
  from examples.noisychannel import rerank_generate, rerank_options, rerank_score_bw, rerank_score_lm, rerank_utils
label_desc: Use image node_id 2 for calling a global function with example usage: match_target_hypo(args, target_outfile, hypo_outfile) and returns: args,  args,  args,  args,  best_score, args,  args,  args,  args,  rerank_scores