def prepare_model_input(self, data, if_use_features=False):
    edge_index_np = np.hstack(data.edge_index_nps[0].values())
    edge_index_np = np.hstack([edge_index_np, np.flip(edge_index_np, 0)])
    edge_index = torch.from_numpy(edge_index_np).long().to(self.train_args['device'])
    kwargs = {'edge_index': edge_index}
    if if_use_features:
        kwargs['x'] = data.x
    return kwargs
def prepare_model_input(self, data, if_use_features=False):
    edge_index_np = np.hstack(data.edge_index_nps[0].values())
    edge_index_np = np.hstack([edge_index_np, np.flip(edge_index_np, 0)])
    edge_index = torch.from_numpy(edge_index_np).long().to(self.train_args['device'])
    kwargs = {'edge_index': edge_index}
    if if_use_features:
        kwargs['x'] = data.x
    return kwargs