code: def test():
      model.eval()
      accs = []
      for loader in [train_loader, val_loader, test_loader]:
          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
              accs.append(acc)
      return accs
  
imports: import math
  import copy
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Planetoid
  import torch_geometric.transforms
  from torch_geometric.nn import SplineConv
  from torch_geometric.nn import GCNConv
  import sys
  import networkx
  import pdb
  import deepsnap
  from converter import *
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: accs