code: def load_muv(
      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 MUV dataset
  
      The Maximum Unbiased Validation (MUV) group is a benchmark dataset selected
      from PubChem BioAssay by applying a refined nearest neighbor analysis.
  
      The MUV dataset contains 17 challenging tasks for around 90 thousand
      compounds and is specifically designed for validation of virtual screening
      techniques.
  
      Scaffold splitting is recommended for this dataset.
  
      The raw data csv file contains columns below:
  
      - "mol_id" - PubChem CID of the compound
      - "smiles" - SMILES representation of the molecular structure
      - "MUV-XXX" - Measured results (Active/Inactive) for bioassays
  
      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] Rohrer, Sebastian G., and Knut Baumann. "Maximum unbiased validation
          (MUV) data sets for virtual screening based on PubChem bioactivity data."
          Journal of chemical information and modeling 49.2 (2009): 169-184.
      """
      loader = _MuvLoader(
          featurizer,
          splitter,
          transformers,
          MUV_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("muv", reload)
  
file_path: deepchem/deepchem/molnet/load_function/muv_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_muv(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader