normalize the MEGAN parameter like the other one

[thegodone]

Is there a way to have MEGAN parameter similar to like any other ones ?

for example :

 "model": {
            "class_name": "make_model",
            "module_name": "kgcnn.literature.rGIN",
            "config": {
                "name": "rGIN",
                "inputs": [{"shape": [None, 41], "name": "node_attributes", "dtype": "float32", "ragged": True},
                           {"shape": [None, 2], "name": "edge_indices", "dtype": "int64", "ragged": True}],
                "input_embedding": {"node": {"input_dim": 96, "output_dim": 100}},
                "depth": 5,
                "dropout": 0.05,
                "gin_mlp": {"units": [100, 100], "use_bias": True, "activation": ["relu", "relu"],
                            "use_normalization": True, "normalization_technique": "graph"},
                "rgin_args": {"random_range": 100},
                "last_mlp": {"use_bias": True, "units": [200, 100, 1], "activation": ["kgcnn>leaky_relu", "selu", "linear"]},
                "output_embedding": "graph",
                "output_mlp": {"activation": "linear", "units": 1}
            }
        }

currently this is the only one that have special config:

"model": {
            "class_name": "make_model",
            "module_name": "kgcnn.literature.MEGAN",
            "config": {
                'name': "MEGAN",
                'inputs': [
                    {'shape': (None, 41), 'name': "node_attributes", 'dtype': 'float32'},
                    {'shape': (None,), 'name': "edge_number", 'dtype': 'float32'},
                    {'shape': (None, 2), 'name': "edge_indices", 'dtype': 'int64'},
                    {"shape": [2], "name": "graph_attributes", "dtype": "float32"},
                    {"shape": (), "name": "total_nodes", "dtype": "int64"},
                    {"shape": (), "name": "total_edges", "dtype": "int64"},
                ],
                "input_tensor_type": "padded",
                'units': [60, 50, 40, 30],
                'importance_units': [],
                'final_units': [50, 30, 10, 1],
                'dropout_rate': 0.3,
                'final_dropout_rate': 0.00,
                'importance_channels': 3,
                'return_importances': False,
                'use_edge_features': False,
            }
        }