code: def load_hiv(
      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 HIV dataset
  
      The HIV dataset was introduced by the Drug Therapeutics
      Program (DTP) AIDS Antiviral Screen, which tested the ability
      to inhibit HIV replication for over 40,000 compounds.
      Screening results were evaluated and placed into three
      categories: confirmed inactive (CI),confirmed active (CA) and
      confirmed moderately active (CM). We further combine the
      latter two labels, making it a classification task between
      inactive (CI) and active (CA and CM).
  
      Scaffold splitting is recommended for this dataset.
  
      The raw data csv file contains columns below:
  
      - "smiles": SMILES representation of the molecular structure
      - "activity": Three-class labels for screening results: CI/CM/CA
      - "HIV_active": Binary labels for screening results: 1 (CA/CM) and 0 (CI)
  
      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] AIDS Antiviral Screen Data.
          https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data
      """
      loader = _HIVLoader(
          featurizer,
          splitter,
          transformers,
          HIV_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("hiv", reload)
  
file_path: deepchem/deepchem/molnet/load_function/hiv_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_hiv(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader