code: def test():
      model.eval()
      pred = model(data.x, data.edge_index).argmax(dim=-1)
  
      train_correct = int((pred[train_idx] == data.y[train_idx]).sum())
      train_acc = train_correct / train_idx.size(0)
  
      test_correct = int((pred[test_idx] == data.y[test_idx]).sum())
      test_acc = test_correct / test_idx.size(0)
  
      return train_acc, test_acc
  
file_path: pytorch_geometric/examples/explain/gnn_explainer_ba_shapes.py
imports: import torch
  import torch.nn.functional
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import ExplainerDataset
  from torch_geometric.datasets.graph_generator import BAGraph
  from torch_geometric.explain import Explainer, GNNExplainer
  from torch_geometric.nn import GCN
  from torch_geometric.utils import k_hop_subgraph
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: train_acc,  test_acc