def METHOD_NAME(self, is_infer=False):
    dataset = pgl.dataset.BlogCatalogDataset()
    indegree = dataset.graph.indegree()
    outdegree = dataset.graph.outdegree()
    self.num_nodes = dataset.graph.num_nodes
    self.num_groups = dataset.num_groups
    self.batch_size = len(dataset.train_index)  #5156
    if is_infer:
        self.all_nodes = dataset.test_index
    else:
        self.all_nodes = dataset.train_index
    self.node_group_id = dataset.graph.node_feat['group_id']
    node = paddle.static.data(name="node", shape=[None], dtype='int64')
    labels = paddle.static.data(
        name="labels", shape=[None, 39], dtype='float32')
    feeds_list = [node, labels]
    return feeds_list