code: def load_bbbc002(
      splitter: Union[dc.splits.Splitter, str, None] = "index",
      transformers: List[Union[TransformerGenerator, str]] = [],
      reload: bool = True,
      data_dir: Optional[str] = None,
      save_dir: Optional[str] = None,
      **kwargs,
  ) -> Tuple[
      List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]
  ]:
      """Load BBBC002 dataset
  
      This dataset contains data corresponding to 5 samples of Drosophilia Kc167
      cells. There are 10 fields of view for each sample, each an image of size
      512x512. Ground truth labels contain cell counts for this dataset. Full
      details about this dataset are present at
      https://data.broadinstitute.org/bbbc/BBBC002/.
  
      Parameters
      ----------
      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
      """
      featurizer = dc.feat.UserDefinedFeaturizer(
          []
      )  # Not actually used
      loader = _BBBC002Loader(
          featurizer,
          splitter,
          transformers,
          BBBC2_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("bbbc002", reload)
  
file_path: deepchem/deepchem/molnet/load_function/bbbc_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
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: load_bbbc002(splitter, transformers, reload, data_dir, save_dir) and returns: loader