code: def test(loader):
      model.eval()
      correct = 0
  
      for data in loader:
          data = data.to(device)
          pred = model(data.x, data.adj, data.mask)[0].max(dim=1)[1]
          correct += int(pred.eq(data.y.view(-1)).sum())
      return correct / len(loader.dataset)
  
file_path: pytorch_geometric/examples/proteins_diff_pool.py
imports: import os.path
  import time
  from math import ceil
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DenseDataLoader
  from torch_geometric.nn import DenseSAGEConv, dense_diff_pool
label_desc: Use image node_id 2 for calling a global function with example usage: test(loader) and returns: unknown