code: def evaluate(y_pred, y_true, idx, evaluator):
      return evaluator.eval(
          {"y_true": y_true[idx], "y_pred": y_pred[idx]}
      )["acc"]
  
file_path: dgl/examples/pytorch/correct_and_smooth/main.py
imports: import argparse
  import copy
  import os
  import dgl
  import torch
  import torch.nn.functional
  import torch.optim
  from model import CorrectAndSmooth, MLP, MLPLinear
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(y_pred, y_true, idx, evaluator) and returns: evaluator