code: def get_answers(cls, qa):
      def _flatten_validated_answers(validated_answers):
          """Flattens a dict of lists of validated answers.
          {"number": ['1', '8'], ...}
          -> [{"number": ['1'], ...}, {"number": ['8'], ...}]
          """
          valid_answers = []
          for i in range(len(validated_answers["number"])):
              valid_answers.append(
                  {
                      "number": validated_answers["number"][i],
                      "date": validated_answers["date"][i],
                      "spans": validated_answers["spans"][i],
                  }
              )
          return valid_answers
  
      answers = []
      answers_set = set()
      candidates = [qa["answer"]] + _flatten_validated_answers(
          qa["validated_answers"]
      )
      for candidate in candidates:
          answer = cls.parse_answer(candidate)
          if answer in answers_set:
              continue
          answers_set.add(answer)
          answers.append(answer)
      return answers
  
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 7 for calling the DROP obj's underlying member method code with example usage: obj.get_answers(cls, qa) and returns: answers, valid_answers