def METHOD_NAME(shard_size=2000, featurizer=None, split=None, reload=True):
    """Loads kaggle datasets. Generates if not stored already.
    The Kaggle dataset is an in-house dataset from Merck that was first introduced in the following paper:
    Ma, Junshui, et al. "Deep neural nets as a method for quantitative structure–activity relationships." Journal of chemical information and modeling 55.2 (2015): 263-274.
    It contains 100,000 unique Merck in-house compounds that were
    measured on 15 enzyme inhibition and ADME/TOX datasets.
    Unlike most of the other datasets featured in MoleculeNet,
    the Kaggle collection does not have structures for the
    compounds tested since they were proprietary Merck compounds.
    However, the collection does feature pre-computed descriptors
    for these compounds.
    Note that the original train/valid/test split from the source
    data was preserved here, so this function doesn't allow for
    alternate modes of splitting. Similarly, since the source data
    came pre-featurized, it is not possible to apply alternative
    featurizations.
    Parameters
    ----------
    shard_size: int, optional
        Size of the DiskDataset shards to write on disk
    featurizer: optional
        Ignored since featurization pre-computed
    split: optional
        Ignored since split pre-computed
    reload: bool, optional
        Whether to automatically re-load from disk
    """
    KAGGLE_tasks = [
        '3A4', 'CB1', 'DPP4', 'HIVINT', 'HIV_PROT', 'LOGD', 'METAB', 'NK1',
        'OX1', 'OX2', 'PGP', 'PPB', 'RAT_F', 'TDI', 'THROMBIN'
    ]
    data_dir = deepchem.utils.data_utils.get_data_dir()
    data_dir = os.path.join(data_dir, "kaggle")
    if not os.path.exists(data_dir):
        os.mkdir(data_dir)
    train_dir = os.path.join(data_dir, "train_dir")
    valid_dir = os.path.join(data_dir, "valid_dir")
    test_dir = os.path.join(data_dir, "test_dir")
    if (os.path.exists(train_dir) and os.path.exists(valid_dir) and
            os.path.exists(test_dir)):
        logger.info("Reloading existing datasets")
        train_dataset = deepchem.data.DiskDataset(train_dir)
        valid_dataset = deepchem.data.DiskDataset(valid_dir)
        test_dataset = deepchem.data.DiskDataset(test_dir)
    else:
        logger.info("Featurizing datasets")
        train_dataset, valid_dataset, test_dataset = \
          gen_kaggle(KAGGLE_tasks, train_dir, valid_dir, test_dir, data_dir,
                      shard_size=shard_size)
    transformers = get_transformers(train_dataset)
    return KAGGLE_tasks, (train_dataset, valid_dataset,
                          test_dataset), transformers