code: def test(model, data, mask):
      model.eval()
  
      pred = model(data)[mask].max(1)[1]
      acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
      return acc
  
imports: import os
  import random
  import numpy
  from tqdm import tqdm
  import pickle
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Planetoid
  import torch_geometric.transforms
  from torch_geometric.nn import GCNConv, GATConv, SAGEConv
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: test(model, data, mask) and returns: acc