How to use the megnet.data.crystal.CrystalGraph function in megnet

embedding_file = args.embedding_file
    k_folds = list(map(int, args.k_folds.split(",")))
    print("args is : {}".format(args))

    print(
        "Local devices are : {}, \n\n Available gpus are : {}".format(
            device_lib.list_local_devices(), K.tensorflow_backend._get_available_gpus()
        )
    )

    # prepare output path
    if not os.path.exists(output_path):
        os.makedirs(output_path, exist_ok=True)

    # Get a crystal graph with cutoff radius A
    cg = CrystalGraph(
        bond_convertor=GaussianDistance(np.linspace(0, radius + 1, 100), 0.5),
        cutoff=radius,
    )

    if graph_file is not None:
        # load graph data
        with gzip.open(graph_file, "rb") as f:
            valid_graph_dict = pickle.load(f)
        idx_list = list(range(len(valid_graph_dict)))
        valid_idx_list = [
            idx for idx, graph in valid_graph_dict.items() if graph is not None
        ]
    else:
        # load structure data
        with gzip.open(args.input_file, "rb") as f:
            df = pd.DataFrame(pickle.load(f))[["structure", prop_col]]

act=act,
                                  is_classification=is_classification,
                                  l2_coef=l2_coef,
                                  dropout=dropout,
                                  dropout_on_predict=dropout_on_predict)

        # Compile the model with the optimizer
        loss = 'binary_crossentropy' if is_classification else loss

        opt_params = {'lr': lr}
        if optimizer_kwargs is not None:
            opt_params.update(optimizer_kwargs)
        model.compile(Adam(**opt_params), loss, metrics=metrics)

        if graph_converter is None:
            graph_converter = CrystalGraph(cutoff=4, bond_converter=GaussianDistance(np.linspace(0, 5, 100), 0.5))

        super().__init__(model=model, target_scaler=target_scaler, graph_converter=graph_converter)

act=act,
                                  is_classification=is_classification,
                                  l2_coef=l2_coef,
                                  dropout=dropout,
                                  dropout_on_predict=dropout_on_predict)

        # Compile the model with the optimizer
        loss = 'binary_crossentropy' if is_classification else loss

        opt_params = {'lr': lr}
        if optimizer_kwargs is not None:
            opt_params.update(optimizer_kwargs)
        model.compile(Adam(**opt_params), loss, metrics=metrics)

        if graph_converter is None:
            graph_converter = CrystalGraph(cutoff=4, bond_converter=GaussianDistance(np.linspace(0, 5, 100), 0.5))

        super().__init__(model=model, target_scaler=target_scaler, graph_converter=graph_converter)