code: def load_sider(
      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 SIDER dataset
  
      The Side Effect Resource (SIDER) is a database of marketed
      drugs and adverse drug reactions (ADR). The version of the
      SIDER dataset in DeepChem has grouped drug side effects into
      27 system organ classes following MedDRA classifications
      measured for 1427 approved drugs.
  
      Random splitting is recommended for this dataset.
  
      The raw data csv file contains columns below:
  
      - "smiles": SMILES representation of the molecular structure
      - "Hepatobiliary disorders" ~ "Injury, poisoning and procedural
          complications": Recorded side effects for the drug. Please refer
          to http://sideeffects.embl.de/se/?page=98 for details on ADRs.
  
      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] Kuhn, Michael, et al. "The SIDER database of drugs and side effects."
          Nucleic acids research 44.D1 (2015): D1075-D1079.
      .. [2] Altae-Tran, Han, et al. "Low data drug discovery with one-shot
          learning." ACS central science 3.4 (2017): 283-293.
      .. [3] Medical Dictionary for Regulatory Activities. http://www.meddra.org/
      """
      loader = _SiderLoader(
          featurizer,
          splitter,
          transformers,
          SIDER_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("sider", reload)
  
file_path: deepchem/deepchem/molnet/load_function/sider_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_sider(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader