code: def load_kaggle(
      shard_size=2000, featurizer=None, split=None, reload=True
  ):
      """Loads kaggle datasets. Generates if not stored already.
  
      The Kaggle dataset is an in-house dataset from Merck that was first introduced in the following paper:
  
      Ma, Junshui, et al. "Deep neural nets as a method for quantitative structure–activity relationships." Journal of chemical information and modeling 55.2 (2015): 263-274.
  
      It contains 100,000 unique Merck in-house compounds that were
      measured on 15 enzyme inhibition and ADME/TOX datasets.
      Unlike most of the other datasets featured in MoleculeNet,
      the Kaggle collection does not have structures for the
      compounds tested since they were proprietary Merck compounds.
      However, the collection does feature pre-computed descriptors
      for these compounds.
  
      Note that the original train/valid/test split from the source
      data was preserved here, so this function doesn't allow for
      alternate modes of splitting. Similarly, since the source data
      came pre-featurized, it is not possible to apply alternative
      featurizations.
  
      Parameters
      ----------
      shard_size: int, optional
          Size of the DiskDataset shards to write on disk
      featurizer: optional
          Ignored since featurization pre-computed
      split: optional
          Ignored since split pre-computed
      reload: bool, optional
          Whether to automatically re-load from disk
  
      """
      KAGGLE_tasks = [
          "3A4",
          "CB1",
          "DPP4",
          "HIVINT",
          "HIV_PROT",
          "LOGD",
          "METAB",
          "NK1",
          "OX1",
          "OX2",
          "PGP",
          "PPB",
          "RAT_F",
          "TDI",
          "THROMBIN",
      ]
      data_dir = deepchem.utils.data_utils.get_data_dir()
  
      data_dir = os.path.join(data_dir, "kaggle")
      if not os.path.exists(data_dir):
          os.mkdir(data_dir)
      train_dir = os.path.join(data_dir, "train_dir")
      valid_dir = os.path.join(data_dir, "valid_dir")
      test_dir = os.path.join(data_dir, "test_dir")
  
      if (
          os.path.exists(train_dir)
          and os.path.exists(valid_dir)
          and os.path.exists(test_dir)
      ):
          logger.info("Reloading existing datasets")
          train_dataset = deepchem.data.DiskDataset(train_dir)
          valid_dataset = deepchem.data.DiskDataset(valid_dir)
          test_dataset = deepchem.data.DiskDataset(test_dir)
      else:
          logger.info("Featurizing datasets")
          train_dataset, valid_dataset, test_dataset = gen_kaggle(
              KAGGLE_tasks,
              train_dir,
              valid_dir,
              test_dir,
              data_dir,
              shard_size=shard_size,
          )
  
      transformers = get_transformers(train_dataset)
      return (
          KAGGLE_tasks,
          (train_dataset, valid_dataset, test_dataset),
          transformers,
      )
  
file_path: deepchem/deepchem/molnet/load_function/kaggle_datasets.py
imports: import os
  import logging
  import time
  import deepchem
  from deepchem.molnet.load_function.kaggle_features import merck_descriptors
  from deepchem.utils import remove_missing_entries
label_desc: Use image node_id 3 for calling a global function with example usage: load_kaggle(shard_size, featurizer, split, reload) and returns: KAGGLE_tasks,  train_dataset,  valid_dataset,  test_dataset,  transformers