code: def computeF1(outputs, targets):
      return (
          sum(
              [
                  metric_max_over_ground_truths(f1_score, o, t)
                  for o, t in zip(outputs, targets)
              ]
          )
          / len(outputs)
          * 100
      )
  
imports: from subprocess import Popen, PIPE, CalledProcessError
  import json
  from text.torchtext.datasets.generic import Query
  import logging
  import os
  import re
  import string
  import numpy
  import collections
  from multiprocessing import Pool, cpu_count
  from contextlib import closing
  from pyrouge import Rouge155
  from sacrebleu import corpus_bleu
label_desc: Use image node_id 10 for calling a global function with example usage: computeF1(outputs, targets) and returns: unknown