diff --git a/scgnn/scgnn.py b/scgnn/scgnn.py
index a0f07ef..b7f2cd5 100644
--- a/scgnn/scgnn.py
+++ b/scgnn/scgnn.py
@@ -159,55 +159,8 @@ class SCGNN(ExplainerAlgorithm):
             score_map = score_map + val * feat
 
         score_map = F.relu(score_map)
-
         if self.score_map_norm and score_map.min() != score_map.max():
             score_map = (score_map - score_map.min()).div(
                 score_map.max() - score_map.min()
             )
         return score_map
-
-
-if __name__ == "__main__":
-    from torch_geometric.datasets import TUDataset
-
-    dataset = TUDataset(root="/tmp/ENZYMES", name="ENZYMES")
-    data = dataset[0]
-
-    import torch.nn.functional as F
-    from torch_geometric.nn import GCNConv, global_mean_pool
-
-    # model = torch.nn.ModuleDict(
-    # {
-    # "conv1": GCNConv(dataset.num_node_features, 64),
-    # "conv2": GCNConv(64, dataset.num_classes),
-    # "gmp": global_mean_pool,
-    # }
-    # )
-
-    model = Sequential(
-        "data",
-        [
-            (
-                lambda data: (data.x, data.edge_index, data.batch),
-                "data -> x, edge_index, batch",
-            ),
-            (GCNConv(dataset.num_node_features, 64), "x, edge_index -> x"),
-            (GCNConv(64, dataset.num_classes), "x, edge_index -> x"),
-            (global_mean_pool, "x, batch -> x"),
-        ],
-    )
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(device)
-    data = dataset[0].to(device)
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
-    model.eval()
-    out = model(data)
-    explainer = SCGNN()
-    explained = explainer.forward(
-        model,
-        data.x,
-        data.edge_index,
-        target=2,
-        interest_map_norm=True,
-        score_map_norm=True,
-    )