code: def process_predictions(
      args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id
  ):
      for hypo in hypos[: min(len(hypos), args.nbest)]:
          hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu())
  
          if "words" in hypo:
              hyp_words = " ".join(hypo["words"])
          else:
              hyp_words = post_process(hyp_pieces, args.post_process)
  
          if res_files is not None:
              print(
                  "{} ({}-{})".format(hyp_pieces, speaker, id),
                  file=res_files["hypo.units"],
              )
              print(
                  "{} ({}-{})".format(hyp_words, speaker, id),
                  file=res_files["hypo.words"],
              )
  
          tgt_pieces = tgt_dict.string(target_tokens)
          tgt_words = post_process(tgt_pieces, args.post_process)
  
          if res_files is not None:
              print(
                  "{} ({}-{})".format(tgt_pieces, speaker, id),
                  file=res_files["ref.units"],
              )
              print(
                  "{} ({}-{})".format(tgt_words, speaker, id),
                  file=res_files["ref.words"],
              )
  
          if not args.quiet:
              logger.info("HYPO:" + hyp_words)
              logger.info("TARGET:" + tgt_words)
              logger.info("___________________")
  
          hyp_words = hyp_words.split()
          tgt_words = tgt_words.split()
          return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
  
imports: import ast
  import logging
  import math
  import os
  import sys
  import editdistance
  import numpy
  import torch
  from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
  from fairseq.data.data_utils import post_process
  from fairseq.logging.meters import StopwatchMeter, TimeMeter
label_desc: Use image node_id 4 for calling a global function with example usage: process_predictions(args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id) and returns: editdistance,  len