def METHOD_NAME(name, normalized_feature=True):
    if name == 'cora':
        dataset = pgl.dataset.CoraDataset()
    elif name == "pubmed":
        dataset = pgl.dataset.CitationDataset("pubmed", symmetry_edges=True)
    elif name == "citeseer":
        dataset = pgl.dataset.CitationDataset("citeseer", symmetry_edges=True)
    else:
        raise ValueError(name + " dataset doesn't exists")
    indegree = dataset.graph.indegree()
    dataset.graph.node_feat["words"] = normalize(dataset.graph.node_feat[
        "words"])
    dataset.graph.tensor()
    train_index = dataset.train_index
    dataset.train_label = paddle.to_tensor(
        np.expand_dims(dataset.y[train_index], -1))
    dataset.train_index = paddle.to_tensor(np.expand_dims(train_index, -1))
    val_index = dataset.val_index
    dataset.val_label = paddle.to_tensor(
        np.expand_dims(dataset.y[val_index], -1))
    dataset.val_index = paddle.to_tensor(np.expand_dims(val_index, -1))
    test_index = dataset.test_index
    dataset.test_label = paddle.to_tensor(
        np.expand_dims(dataset.y[test_index], -1))
    dataset.test_index = paddle.to_tensor(np.expand_dims(test_index, -1))
    return dataset