code: def load_sweet(
      featurizer: Union[dc.feat.Featurizer, str] = "ECFP",
      splitter: Union[dc.splits.Splitter, str, None] = "scaffold",
      transformers: List[Union[TransformerGenerator, str]] = [
          "balancing"
      ],
      reload: bool = True,
      data_dir: Optional[str] = None,
      save_dir: Optional[str] = None,
      **kwargs,
  ) -> Tuple[
      List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]
  ]:
      """Load sweet datasets.
  
      Sweetlead is a dataset of chemical structures for approved drugs, chemical isolates
      from traditional medicinal herbs, and regulated chemicals. Resulting structures are
      filtered for the active pharmaceutical ingredient, standardized, and differing
      formulations of the same drug were combined in the final database.
  
      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
  
      References
      ----------
      Novick, Paul A., et al. "SWEETLEAD: an in silico database of approved drugs, regulated
      chemicals, and herbal isolates for computer-aided drug discovery." PLoS One 8.11 (2013).
      """
      loader = _SweetLoader(
          featurizer,
          splitter,
          transformers,
          SWEETLEAD_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("sweet", reload)
  
file_path: deepchem/deepchem/molnet/load_function/sweetlead_datasets.py
imports: import os
  import deepchem
  from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
  from deepchem.data import Dataset
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling a global function with example usage: load_sweet(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader