code: def _compute_f1(self, predicted_bag, gold_bag):
      intersection = len(gold_bag.intersection(predicted_bag))
      if not predicted_bag:
          precision = 1.0
      else:
          precision = intersection / float(len(predicted_bag))
      if not gold_bag:
          recall = 1.0
      else:
          recall = intersection / float(len(gold_bag))
      f1 = (
          (2 * precision * recall) / (precision + recall)
          if not (precision == 0.0 and recall == 0.0)
          else 0.0
      )
      return f1
  
imports: import inspect
  import numpy
  import re
  import string
  import lm_eval.datasets.drop.drop
  from scipy.optimize import linear_sum_assignment
  from lm_eval.base import Task, rf
  from lm_eval.metrics import mean
label_desc: Use image node_id 18 for calling the DROP obj's underlying member method code with example usage: obj._compute_f1(predicted_bag, gold_bag) and returns: f1