def METHOD_NAME(self):
    """Check that singletask load and multitask load of dataset are same."""
    # Only for debug!
    np.random.seed(123)
    current_dir = os.path.dirname(os.path.realpath(__file__))
    # Make directories to store the raw and featurized datasets.
    data_dir = tempfile.mkdtemp()
    # Load dataset
    logger.info("About to load dataset.")
    dataset_file = os.path.join(
        current_dir, "../../models/tests/assets/multitask_example.csv")
    # Featurize tox21 dataset
    logger.info("About to featurize dataset.")
    featurizer = dc.feat.CircularFingerprint(size=1024)
    all_tasks = ["task%d" % i for i in range(17)]
    # For debugging purposes
    n_tasks = 17
    tasks = all_tasks[0:n_tasks]
    # multitask load
    loader = dc.data.CSVLoader(tasks=tasks,
                               feature_field="smiles",
                               featurizer=featurizer)
    dataset = loader.create_dataset(dataset_file, data_dir)
    # Do train/valid split.
    _, y_multi, w_multi, _ = (dataset.X, dataset.y, dataset.w, dataset.ids)
    # singletask load
    y_tasks, w_tasks, ids_tasks = [], [], []
    for task in tasks:
        logger.info("Processing task %s" % task)
        if os.path.exists(data_dir):
            shutil.rmtree(data_dir)
        loader = dc.data.CSVLoader(tasks=[task],
                                   feature_field="smiles",
                                   featurizer=featurizer)
        dataset = loader.create_dataset(dataset_file, data_dir)
        _, y_task, w_task, ids_task = (dataset.X, dataset.y, dataset.w,
                                       dataset.ids)
        y_tasks.append(y_task)
        w_tasks.append(w_task)
        ids_tasks.append(ids_task)
    # comparison
    for ind, task in enumerate(tasks):
        y_multi_task = y_multi[:, ind]
        w_multi_task = w_multi[:, ind]
        y_task = y_tasks[ind]
        w_task = w_tasks[ind]
        ids_task = ids_tasks[ind]
        np.testing.assert_allclose(y_multi_task.flatten(), y_task.flatten())
        np.testing.assert_allclose(w_multi_task.flatten(), w_task.flatten())