code: def expected_output_format(self):
      desc = "==== Expected output format of Evaluator for {}\n".format(
          self.name
      )
      if "hits@" in self.eval_metric:
          desc += (
              "{" + "hits@{}': hits@{}".format(self.K, self.K) + "}\n"
          )
          desc += "- hits@{} (float): Hits@{} score\n".format(
              self.K, self.K
          )
      elif self.eval_metric == "mrr":
          desc += (
              "{"
              + "'hits@1_list': hits@1_list, 'hits@3_list': hits@3_list, \n'hits@10_list': hits@10_list, 'mrr_list': mrr_list}\n"
          )
          desc += "- mrr_list (list of float): list of scores for calculating MRR \n"
          desc += "- hits@1_list (list of float): list of scores for calculating Hits@1 \n"
          desc += "- hits@3_list (list of float): list of scores to calculating Hits@3\n"
          desc += "- hits@10_list (list of float): list of scores to calculating Hits@10\n"
          desc += "Note: i-th element corresponds to the prediction score for the i-th edge.\n"
          desc += "Note: To obtain the final score, you need to concatenate the lists of scores and take average over the concatenated list."
      elif self.eval_metric == "rocauc":
          desc += "{'rocauc': rocauc\n"
          desc += "- rocauc (float): ROC-AUC score\n"
      else:
          raise ValueError(
              "Undefined eval metric %s" % (self.eval_metric)
          )
  
      return desc
  
file_path: ogb/ogb/linkproppred/evaluate.py
imports: import os
  import numpy
  import pandas
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 5 for calling the Evaluator obj's underlying member method code with example usage: obj.expected_output_format() and returns: desc