code: def test(model, loader, args):
      model.eval()
  
      correct = 0
      for data in loader:
          data = data.to(args.device)
          output = model(data)
          pred = output.max(dim=1)[1]
          correct += pred.eq(data.y).sum().item()
      return correct / len(loader.dataset)
  
imports: import os
  import random
  import numpy
  from tqdm import tqdm
  import torch
  import torch.nn.functional
  from torch.nn import Sequential, Linear, ReLU
  import torch_geometric
  from torch_geometric.datasets import TUDataset
  from torch_geometric.nn import GINConv, global_add_pool, GraphConv, TopKPooling
  from torch_geometric.nn import global_mean_pool, global_max_pool
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: test(model, loader, args) and returns: unknown