def METHOD_NAME(batch_size, segment="train"):
    """
    Get a DataLoader instance for dataset of sudoku. Every iteration of the dataloader returns
    a DGLGraph instance, the ndata of the graph contains:
    'q': question, e.g. the sudoku puzzle to be solved, the position is to be filled with number from 1-9
         if the value in the position is 0
    'a': answer, the ground truth of the sudoku puzzle
    'row': row index for each position in the grid
    'col': column index for each position in the grid
    :param batch_size: Batch size for the dataloader
    :param segment: The segment of the datasets, must in ['train', 'valid', 'test']
    :return: A pytorch DataLoader instance
    """
    data = _get_sudoku_dataset(segment)
    q, a = zip(*data)
    dataset = ListDataset(q, a)
    if segment == "train":
        data_sampler = RandomSampler(dataset)
    else:
        data_sampler = SequentialSampler(dataset)
    basic_graph = _basic_sudoku_graph()
    sudoku_indices = np.arange(0, 81)
    rows = sudoku_indices // 9
    cols = sudoku_indices % 9
    def collate_fn(batch):
        graph_list = []
        for q, a in batch:
            q = torch.tensor(q, dtype=torch.long)
            a = torch.tensor(a, dtype=torch.long)
            graph = copy(basic_graph)
            graph.ndata["q"] = q  # q means question
            graph.ndata["a"] = a  # a means answer
            graph.ndata["row"] = torch.tensor(rows, dtype=torch.long)
            graph.ndata["col"] = torch.tensor(cols, dtype=torch.long)
            graph_list.append(graph)
        batch_graph = dgl.batch(graph_list)
        return batch_graph
    dataloader = DataLoader(
        dataset, batch_size, sampler=data_sampler, collate_fn=collate_fn
    )
    return dataloader