docstr: Takes a predicted answer and a gold answer (that are both either a string or a list of
  strings), and returns exact match and the DROP F1 metric for the prediction.  If you are
  writing a script for evaluating objects in memory (say, the output of predictions during
  validation, or while training), this is the function you want to call, after using
  :func:`answer_json_to_strings` when reading the gold answer from the released data file.
code: def get_metrics(self, predicted, gold):
      """
      Takes a predicted answer and a gold answer (that are both either a string or a list of
      strings), and returns exact match and the DROP F1 metric for the prediction.  If you are
      writing a script for evaluating objects in memory (say, the output of predictions during
      validation, or while training), this is the function you want to call, after using
      :func:`answer_json_to_strings` when reading the gold answer from the released data file.
      """
      predicted_bags = self._answer_to_bags(predicted)
      gold_bags = self._answer_to_bags(gold)
  
      if set(predicted_bags[0]) == set(gold_bags[0]) and len(
          predicted_bags[0]
      ) == len(gold_bags[0]):
          exact_match = 1.0
      else:
          exact_match = 0.0
  
      f1_per_bag = self._align_bags(predicted_bags[1], gold_bags[1])
      f1 = np.mean(f1_per_bag)
      f1 = round(f1, 2)
      return exact_match, 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 15 for calling the DROP obj's underlying member method code with example usage: obj.get_metrics(predicted, gold) and returns: exact_match,  f1