code: def test():
      accs = []
      model.eval()
      pred = model(data.x, data.edge_index).argmax(dim=-1)
      for _, mask in data("train_mask", "val_mask", "test_mask"):
          mask = mask[:, 0]  # Use the first set of the five masks.
          accs.append(
              int((pred[mask] == data.y[mask]).sum()) / int(mask.sum())
          )
      return accs
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import LINKXDataset
  from torch_geometric.nn import LINKX
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: accs