code: def _eval_acc(self, y_true, y_pred):
      acc_list = []
  
      for i in range(y_true.shape[1]):
          is_labeled = y_true[:, i] == y_true[:, i]
          correct = y_true[is_labeled, i] == y_pred[is_labeled, i]
          acc_list.append(float(np.sum(correct)) / len(correct))
  
      return {"acc": sum(acc_list) / len(acc_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 7 for calling the Evaluator obj's underlying member method code with example usage: obj._eval_acc(y_true, y_pred) and returns: dict