Update

    src = self.edge_index[0]

    dst = self.edge_index[1]

    if self_loop:

      src = np.concatenate([src, np.arange(self.num_nodes)])

      dst = np.concatenate([dst, np.arange(self.num_nodes)])

    g = dgl.graph(

        (torch.from_numpy(src).long(), torch.from_numpy(dst).long()),

    if self.edge_features is not None:

      g.edata['edge_attr'] = torch.from_numpy(self.edge_features).float()

    if self_loop:

      # This assumes that the edge features for self loops are full-zero tensors

      # In the future we may want to support featurization for self loops

      g.add_edges(np.arange(self.num_nodes), np.arange(self.num_nodes))

    return g