code: def test(loader, model, device="cuda"):
      model.eval()
  
      for batch in loader:
          batch.to(device)
          logits = model(batch)
          pred = logits[batch.node_label_index].max(1)[1]
          acc = pred.eq(batch.node_label).sum().item()
          total = batch.node_label_index.shape[0]
          acc /= total
      return acc
  
imports: import torch
  import argparse
  import numpy
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.graph import Graph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 4 for calling a global function with example usage: test(loader, model, device) and returns: acc