code: def test():
      model.eval()
  
      propagation_mask = train_mask
      out = model(data.x, data.y, data.edge_index, propagation_mask)
      pred = out[val_mask].argmax(dim=-1)
      val_acc = int((pred == data.y[val_mask]).sum()) / pred.size(0)
  
      propagation_mask = train_mask | val_mask
      out = model(data.x, data.y, data.edge_index, propagation_mask)
      pred = out[test_mask].argmax(dim=-1)
      test_acc = int((pred == data.y[test_mask]).sum()) / pred.size(0)
  
      return val_acc, test_acc
  
file_path: pytorch_geometric/examples/unimp_arxiv.py
imports: import os.path
  import torch
  import torch.nn.functional
  from ogb.nodeproppred import PygNodePropPredDataset
  import torch_geometric.transforms
  from torch_geometric.nn import MaskLabel, TransformerConv
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: val_acc,  test_acc