code: def load_bbbc001(
      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 BBBC001 dataset
  
      This dataset contains 6 images of human HT29 colon cancer cells. The task is
      to learn to predict the cell counts in these images. This dataset is too small
      to serve to train algorithms, but might serve as a good test dataset.
      https://data.broadinstitute.org/bbbc/BBBC001/
  
      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 = _BBBC001Loader(
          featurizer,
          splitter,
          transformers,
          BBBC1_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("bbbc001", 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 1 for calling a global function with example usage: load_bbbc001(splitter, transformers, reload, data_dir, save_dir) and returns: loader