Update

                 number_atom_features=75,

                 n_classes: int = 2,

                 nfeat_name: str = 'x',

                 self_loop: bool = True,

                 **kwargs):

        """

        Parameters

        nfeat_name: str

            For an input graph ``g``, the model assumes that it stores node features in

            ``g.ndata[nfeat_name]`` and will retrieve input node features from that.

        self_loop: bool

            Whether to add self loops for the nodes, i.e. edges from nodes to themselves.

            Default to True.

        kwargs

            This can include any keyword argument of TorchModel.

        """

        super(GCNModel, self).__init__(

            model, loss=loss, output_types=output_types, **kwargs)

        self._self_loop = self_loop

    def _prepare_batch(self, batch):

        """Create batch data for GCN.

            raise ImportError('This class requires dgl.')

        inputs, labels, weights = batch

        dgl_graphs = [graph.to_dgl_graph(self_loop=True) for graph in inputs[0]]

        dgl_graphs = [graph.to_dgl_graph(self_loop=self._self_loop) for graph in inputs[0]]

        inputs = dgl.batch(dgl_graphs).to(self.device)

        _, labels, weights = super(GCNModel, self)._prepare_batch(([], labels, weights))

        return inputs, labels, weights