code: def test(data):
      model.eval()
      out = model(
          data.x, data.edge_index, data.edge_label_index
      ).sigmoid()
      return roc_auc_score(
          data.edge_label.cpu().numpy(), out.cpu().numpy()
      )
  
file_path: pytorch_geometric/examples/explain/gnn_explainer_link_pred.py
imports: import os.path
  import torch
  import torch.nn.functional
  from sklearn.metrics import roc_auc_score
  import torch_geometric.transforms
  from torch_geometric.datasets import Planetoid
  from torch_geometric.explain import Explainer, GNNExplainer, ModelConfig
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 2 for calling a global function with example usage: test(data) and returns: roc_auc_score