docstr: Take a single document and the LM results and evaluates, returning a
  dict where keys are the names of submetrics and values are the values of
  the metric for that one document
  
  :param doc:
      The document as returned from training_docs, validation_docs, or test_docs.
  :param results:
      The results of the requests created in construct_requests.
code: def process_results(self, doc, results):
      """Take a single document and the LM results and evaluates, returning a
      dict where keys are the names of submetrics and values are the values of
      the metric for that one document
  
      :param doc:
          The document as returned from training_docs, validation_docs, or test_docs.
      :param results:
          The results of the requests created in construct_requests.
      """
      preds, golds = results, doc["answers"]
      max_em = 0
      max_f1 = 0
      for gold_answer in golds:
          exact_match, f1_score = self.get_metrics(preds, gold_answer)
          if gold_answer[0].strip():
              max_em = max(max_em, exact_match)
              max_f1 = max(max_f1, f1_score)
      return {"em": max_em, "f1": max_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 14 for calling the DROP obj's underlying member method code with example usage: obj.process_results(doc, results) and returns: dict