def METHOD_NAME(
    method,
    data,
    node_mask_type,
    edge_mask_type,
    task_level,
    index,
):
    if node_mask_type is None and edge_mask_type is None:
        return
    batch = torch.tensor([0, 0, 1, 1])
    edge_label_index = torch.tensor([[0, 1, 2], [2, 3, 1]])
    model_config = ModelConfig(
        mode='binary_classification',
        task_level=task_level,
        return_type='probs',
    )
    explainer = Explainer(
        GCN(model_config),
        algorithm=CaptumExplainer(method),
        explanation_type='model',
        edge_mask_type=edge_mask_type,
        node_mask_type=node_mask_type,
        model_config=model_config,
    )
    explanation = explainer(
        data.x,
        data.edge_index,
        index=index,
        batch=batch,
        edge_label_index=edge_label_index,
    )
    check_explanation(explanation, node_mask_type, edge_mask_type)