code: def test():
      model.eval()
      model.set_aggr("mean")
  
      out = model(data.x.to(device), data.edge_index.to(device))
      pred = out.argmax(dim=-1)
      correct = pred.eq(data.y.to(device))
  
      accs = []
      for _, mask in data("train_mask", "val_mask", "test_mask"):
          accs.append(correct[mask].sum().item() / mask.sum().item())
      return accs
  
file_path: pytorch_geometric/examples/graph_saint.py
imports: import argparse
  import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Flickr
  from torch_geometric.loader import GraphSAINTRandomWalkSampler
  from torch_geometric.nn import GraphConv
  from torch_geometric.typing import WITH_TORCH_SPARSE
  from torch_geometric.utils import degree
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: accs