code: def f1(items):
      """See section 4 "Evaluation Metrics" in the paper about the F1 metric used."""
      results = list(zip(*items))
      # Group the positive ("yes" = 1) golds and predictions by question.
      gold_positives, pred_positives = defaultdict(list), defaultdict(
          list
      )
      for gold, pred, question in zip(
          results[0], results[1], results[2]
      ):
          gold_positives[question].append(gold)
          pred_positives[question].append(pred)
      f1 = []
      for question in gold_positives.keys():
          gp, pp = sum(gold_positives[question]), sum(
              pred_positives[question]
          )
          tp = sum(
              np.logical_and(
                  gold_positives[question], pred_positives[question]
              )
          )
          p = tp / pp if pp > 0.0 else 1.0
          r = tp / gp if gp > 0.0 else 1.0
          if p + r > 0.0:
              f1.append(2.0 * (p * r) / (p + r))
      return np.mean(f1)
  
imports: import numpy
  from collections import defaultdict
  from lm_eval.base import rf, Task
label_desc: Use image node_id 2 for calling a global function with example usage: f1(items) and returns: np