code: def _compute(
      self,
      references,
      prediction_scores,
      average="macro",
      sample_weight=None,
      max_fpr=None,
      multi_class="raise",
      labels=None,
  ):
      return {
          "roc_auc": roc_auc_score(
              references,
              prediction_scores,
              average=average,
              sample_weight=sample_weight,
              max_fpr=max_fpr,
              multi_class=multi_class,
              labels=labels,
          )
      }
  
imports: from sklearn.metrics import roc_auc_score
  import datasets
label_desc: Use image node_id 2 for calling the ROCAUC obj's underlying member method code with example usage: obj._compute(references, prediction_scores, average, sample_weight, max_fpr, multi_class, labels) and returns: dict