code: def load_clintox(
      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 ClinTox dataset
  
      The ClinTox dataset compares drugs approved by the FDA and
      drugs that have failed clinical trials for toxicity reasons.
      The dataset includes two classification tasks for 1491 drug
      compounds with known chemical structures:
  
      #. clinical trial toxicity (or absence of toxicity)
      #. FDA approval status.
  
      List of FDA-approved drugs are compiled from the SWEETLEAD
      database, and list of drugs that failed clinical trials for
      toxicity reasons are compiled from the Aggregate Analysis of
      ClinicalTrials.gov(AACT) database.
  
      Random splitting is recommended for this dataset.
  
      The raw data csv file contains columns below:
  
      - "smiles" - SMILES representation of the molecular structure
      - "FDA_APPROVED" - FDA approval status
      - "CT_TOX" - Clinical trial results
  
      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
      ----------
      .. [1] Gayvert, Kaitlyn M., Neel S. Madhukar, and Olivier Elemento.
          "A data-driven approach to predicting successes and failures of clinical
          trials."
          Cell chemical biology 23.10 (2016): 1294-1301.
      .. [2] Artemov, Artem V., et al. "Integrated deep learned transcriptomic and
          structure-based predictor of clinical trials outcomes." bioRxiv (2016):
          095653.
      .. [3] 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): e79568.
      .. [4] Aggregate Analysis of ClincalTrials.gov (AACT) Database.
          https://www.ctti-clinicaltrials.org/aact-database
      """
      loader = _ClintoxLoader(
          featurizer,
          splitter,
          transformers,
          CLINTOX_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("clintox", reload)
  
file_path: deepchem/deepchem/molnet/load_function/clintox_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_clintox(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader