code: def multiprocess_evaluation(
      metric, predict, target, lengths=None, n_jobs=8
  ):
      """Runs metric evaluation if parallel over multiple jobs."""
      if lengths is not None:
          lengths = (lengths * predict.size(1)).round().int().cpu()
          predict = [
              p[:length].cpu() for p, length in zip(predict, lengths)
          ]
          target = [
              t[:length].cpu() for t, length in zip(target, lengths)
          ]
  
      while True:
          try:
              scores = Parallel(n_jobs=n_jobs, timeout=30)(
                  delayed(metric)(p, t) for p, t in zip(predict, target)
              )
              break
          except Exception as e:
              print(e)
              print("Evaluation timeout...... (will try again)")
  
      return scores
  
imports: import torch
  from joblib import Parallel, delayed
  from speechbrain.utils.data_utils import undo_padding
  from speechbrain.utils.edit_distance import wer_summary, wer_details_for_batch
  from speechbrain.dataio.dataio import merge_char, split_word, extract_concepts_values
  from speechbrain.dataio.wer import print_wer_summary, print_alignments
label_desc: Use image node_id 1 for calling a global function with example usage: multiprocess_evaluation(metric, predict, target, lengths, n_jobs) and returns: scores