code: def test_gnn_explainer_attentive_fp(check_explanation):
      model = AttentiveFP(
          3, 16, 1, edge_dim=5, num_layers=2, num_timesteps=2
      )
  
      explainer = Explainer(
          model=model,
          algorithm=GNNExplainer(epochs=2),
          explanation_type="model",
          node_mask_type="object",
          edge_mask_type="object",
          model_config=dict(
              mode="binary_classification",
              task_level="node",
              return_type="raw",
          ),
      )
  
      explanation = explainer(
          x, edge_index, edge_attr=edge_attr, batch=batch
      )
  
      assert explainer.algorithm.node_mask is None
      assert explainer.algorithm.edge_mask is None
  
      check_explanation(explanation, MaskType.object, MaskType.object)
  
file_path: pytorch_geometric/test/explain/algorithm/test_gnn_explainer.py
imports: import pytest
  import torch
  from torch_geometric.explain import Explainer, GNNExplainer
  from torch_geometric.explain.config import ExplanationType, MaskType, ModelConfig, ModelMode, ModelReturnType, ModelTaskLevel
  from torch_geometric.nn import AttentiveFP, ChebConv, GCNConv, global_add_pool
label_desc: Use image node_id 5 for calling a global function with example usage: test_gnn_explainer_attentive_fp(check_explanation) without return types