code: def _parse_and_check_input(self, input_dict):
      if self.eval_metric == "rocauc" or self.eval_metric == "acc":
          if not "y_true" in input_dict:
              raise RuntimeError("Missing key of y_true")
          if not "y_pred" in input_dict:
              raise RuntimeError("Missing key of y_pred")
  
          y_true, y_pred = input_dict["y_true"], input_dict["y_pred"]
  
          """
                  y_true: numpy ndarray or torch tensor of shape (num_nodes num_tasks)
                  y_pred: numpy ndarray or torch tensor of shape (num_nodes num_tasks)
              """
  
          # converting to torch.Tensor to numpy on cpu
          if torch is not None and isinstance(y_true, torch.Tensor):
              y_true = y_true.detach().cpu().numpy()
  
          if torch is not None and isinstance(y_pred, torch.Tensor):
              y_pred = y_pred.detach().cpu().numpy()
  
          ## check type
          if not (
              isinstance(y_true, np.ndarray)
              and isinstance(y_true, np.ndarray)
          ):
              raise RuntimeError(
                  "Arguments to Evaluator need to be either numpy ndarray or torch tensor"
              )
  
          if not y_true.shape == y_pred.shape:
              raise RuntimeError(
                  "Shape of y_true and y_pred must be the same"
              )
  
          if not y_true.ndim == 2:
              raise RuntimeError(
                  "y_true and y_pred must to 2-dim arrray, {}-dim array given".format(
                      y_true.ndim
                  )
              )
  
          if not y_true.shape[1] == self.num_tasks:
              raise RuntimeError(
                  "Number of tasks for {} should be {} but {} given".format(
                      self.name, self.num_tasks, y_true.shape[1]
                  )
              )
  
          return y_true, y_pred
  
      else:
          raise ValueError(
              "Undefined eval metric %s " % (self.eval_metric)
          )
  
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 2 for calling the Evaluator obj's underlying member method code with example usage: obj._parse_and_check_input(input_dict) and returns: y_true,  y_pred