def METHOD_NAME(
    featurizer: Union[dc.feat.Featurizer, str] = "RxnFeaturizer",
    splitter: Union[dc.splits.Splitter, str, None] = None,
    transformers: List[Union[TransformerGenerator, str]] = [],
    reload: bool = True,
    data_dir: Optional[str] = None,
    save_dir: Optional[str] = None,
    subset: str = "MIT",
    sep_reagent: bool = True,
    **kwargs
) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:
    """Load USPTO Datasets.
    The USPTO dataset consists of over 1.8 Million organic chemical reactions
    extracted from US patents and patent applications. The dataset contains the
    reactions in the form of reaction SMILES, which have the general format:
    reactant>reagent>product.
    Molnet provides ability to load subsets of the USPTO dataset namely MIT,
    STEREO and 50K. The MIT dataset contains around 479K reactions, curated by
    jin et al. The STEREO dataset contains around 1 Million Reactions, it does
    not have duplicates and the reactions include stereochemical information.
    The 50K dataset contatins 50,000 reactions and is the benchmark for
    retrosynthesis predictions. The reactions are additionally classified into 10
    reaction classes. The canonicalized version of the dataset used by the loader
    is the same as that used by Somnath et. al.
    The loader uses the SpecifiedSplitter to use the same splits as specified
    by Schwaller et. al and Dai et. al. Custom splitters could also be used. There
    is a toggle in the loader to skip the source/target transformation needed for
    seq2seq tasks. There is an additional toggle to load the dataset with the
    reagents and reactants separated or mixed. This alters the entries in source
    by replacing the '>' with '.' , effectively loading them as an unified
    SMILES string.
    Parameters
    ----------
    featurizer: Featurizer or str
        the featurizer to use for processing the data.  Alternatively you can pass
        one of the names from dc.molnet.featurizers as a shortcut.
    splitter: Splitter or str
        the splitter to use for splitting the data into training, validation, and
        test sets.  Alternatively you can pass one of the names from
        dc.molnet.splitters as a shortcut. If this is None, all the data
        will be included in a single dataset.
    transformers: list of TransformerGenerators or strings
        the Transformers to apply to the data. Each one is specified by a
        TransformerGenerator or, as a shortcut, one of the names from
        dc.molnet.transformers.
    reload: bool
        if True, the first call for a particular featurizer and splitter will cache
        the datasets to disk, and subsequent calls will reload the cached datasets.
    data_dir: str
        a directory to save the raw data in
    save_dir: str
        a directory to save the dataset in
    subset: str (default 'MIT')
        Subset of dataset to download. 'FULL', 'MIT', 'STEREO', and '50K' are supported.
    sep_reagent: bool (default True)
        Toggle to load dataset with reactants and reagents either separated or mixed.
    skip_transform: bool (default True)
        Toggle to skip the source/target transformation.
    Returns
    -------
    tasks, datasets, transformers: tuple
        tasks : list
            Column names corresponding to machine learning target variables.
        datasets : tuple
            train, validation, test splits of data as
            ``deepchem.data.datasets.Dataset`` instances.
        transformers : list
            ``deepchem.trans.transformers.Transformer`` instances applied
            to dataset.
    References
    ----------
    .. [1] Lowe, D. Chemical reactions from US patents (1976-Sep2016)
        (Version 1). figshare (2017). https://doi.org/10.6084/m9.figshare.5104873.v1
    .. [2] Somnath, Vignesh Ram, et al. "Learning graph models for retrosynthesis
        prediction." arXiv preprint arXiv:2006.07038 (2020).
    .. [3] Schwaller, Philippe, et al. "Molecular transformer: a model for
        uncertainty-calibrated chemical reaction prediction."
        ACS central science 5.9 (2019): 1572-1583.
    .. [4] Dai, Hanjun, et al. "Retrosynthesis prediction with conditional
        graph logic network." arXiv preprint arXiv:2001.01408 (2020).
    """
    tokenizer = RobertaTokenizerFast.from_pretrained(
        "seyonec/PubChem10M_SMILES_BPE_450k")
    if featurizer == "plain":
        featurizer = dc.feat.DummyFeaturizer()
    else:
        featurizer = RxnFeaturizer(tokenizer, sep_reagent=sep_reagent)
    loader = _USPTOLoader(featurizer,
                          splitter,
                          transformers,
                          USPTO_TASK,
                          data_dir,
                          save_dir,
                          subset=subset,
                          sep_reagent=sep_reagent,
                          **kwargs)
    return loader.load_dataset(loader.name, reload)