code: def computeLFEM(greedy, answer, args):
      answer = [x[0] for x in answer]
      count = 0
      correct = 0
      text_answers = []
      for idx, (g, ex) in enumerate(zip(greedy, answer)):
          count += 1
          text_answers.append([ex["answer"].lower()])
          try:
              lf = to_lf(g, ex["table"])
              gt = ex["sql"]
              conds = gt["conds"]
              lower_conds = []
              for c in conds:
                  lc = c
                  lc[2] = str(lc[2]).lower()
                  lower_conds.append(lc)
              gt["conds"] = lower_conds
              correct += lf == gt
          except Exception as e:
              continue
      return correct / count * 100, text_answers
  
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 2 for calling a global function with example usage: computeLFEM(greedy, answer, args) and returns: unknown,  text_answers