docstr: compute ROC-AUC and AP score averaged across tasks
code: def _eval_rocauc(self, y_true, y_pred):
      """
      compute ROC-AUC and AP score averaged across tasks
      """
  
      rocauc_list = []
  
      for i in range(y_true.shape[1]):
          # AUC is only defined when there is at least one positive data.
          if (
              np.sum(y_true[:, i] == 1) > 0
              and np.sum(y_true[:, i] == 0) > 0
          ):
              is_labeled = y_true[:, i] == y_true[:, i]
              rocauc_list.append(
                  roc_auc_score(
                      y_true[is_labeled, i], y_pred[is_labeled, i]
                  )
              )
  
      if len(rocauc_list) == 0:
          raise RuntimeError(
              "No positively labeled data available. Cannot compute ROC-AUC."
          )
  
      return {"rocauc": sum(rocauc_list) / len(rocauc_list)}
  
file_path: ogb/ogb/nodeproppred/evaluate.py
imports: import os
  import numpy
  import pandas
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 6 for calling the Evaluator obj's underlying member method code with example usage: obj._eval_rocauc(y_true, y_pred) and returns: dict