code: def load_dataset(dataset, featurizer, split="random"):
      """
      Load specific dataset for benchmark.
  
      Parameters
      ----------
      dataset: string
          choice of which datasets to use, should be: tox21, muv, sider,
          toxcast, pcba, delaney, factors, hiv, hopv, kaggle, kinase, nci,
          clintox, hiv, pcba_128, pcba_146, pdbbind, chembl, qm7, qm7b, qm9,
          sampl, uv
      featurizer: string or dc.feat.Featurizer.
          choice of featurization.
      split: string,  optional (default=None)
          choice of splitter function, None = using the default splitter
      """
      dataset_loading_functions = {
          "bace_c": deepchem.molnet.load_bace_classification,
          "bace_r": deepchem.molnet.load_bace_regression,
          "bbbp": deepchem.molnet.load_bbbp,
          "chembl": deepchem.molnet.load_chembl,
          "clearance": deepchem.molnet.load_clearance,
          "clintox": deepchem.molnet.load_clintox,
          "delaney": deepchem.molnet.load_delaney,
          "factors": deepchem.molnet.load_factors,
          "hiv": deepchem.molnet.load_hiv,
          "hopv": deepchem.molnet.load_hopv,
          "hppb": deepchem.molnet.load_hppb,
          "kaggle": deepchem.molnet.load_kaggle,
          "kinase": deepchem.molnet.load_kinase,
          "lipo": deepchem.molnet.load_lipo,
          "muv": deepchem.molnet.load_muv,
          "nci": deepchem.molnet.load_nci,
          "pcba": deepchem.molnet.load_pcba,
          "pcba_128": deepchem.molnet.load_pcba_128,
          "pcba_146": deepchem.molnet.load_pcba_146,
          "pcba_2475": deepchem.molnet.load_pcba_2475,
          "pdbbind": deepchem.molnet.load_pdbbind,
          "ppb": deepchem.molnet.load_ppb,
          "qm7": deepchem.molnet.load_qm7,
          "qm8": deepchem.molnet.load_qm8,
          "qm9": deepchem.molnet.load_qm9,
          "sampl": deepchem.molnet.load_sampl,
          "sider": deepchem.molnet.load_sider,
          "thermosol": deepchem.molnet.load_thermosol,
          "tox21": deepchem.molnet.load_tox21,
          "toxcast": deepchem.molnet.load_toxcast,
          "uv": deepchem.molnet.load_uv,
      }
      print("-------------------------------------")
      print("Loading dataset: %s" % dataset)
      print("-------------------------------------")
      # loading datasets
      if split is not None:
          print("Splitting function: %s" % split)
      tasks, all_dataset, transformers = dataset_loading_functions[
          dataset
      ](featurizer=featurizer, split=split)
      return tasks, all_dataset, transformers
  
file_path: deepchem/deepchem/molnet/run_benchmark.py
imports: import os
  import time
  import csv
  import numpy
  import deepchem
  import pickle
  from deepchem.molnet.run_benchmark_models import benchmark_classification, benchmark_regression
  from deepchem.molnet.check_availability import CheckFeaturizer, CheckSplit
  from deepchem.molnet.preset_hyper_parameters import hps
label_desc: Use image node_id 2 for calling a global function with example usage: load_dataset(dataset, featurizer, split) and returns: tasks,  all_dataset,  transformers