code: def load_permeability(featurizer="ECFP", split="index"):
      """Load membrain permeability datasets. Does not do train/test split"""
      print("About to load membrain permeability dataset.")
      current_dir = os.path.dirname(os.path.realpath(__file__))
      dataset_file = os.path.join(
          current_dir, "../../datasets/membrane_permeability.sdf"
      )
      # Featurize permeability dataset
      print("About to featurize membrain permeability dataset.")
  
      if featurizer == "ECFP":
          featurizer_func = dc.feat.CircularFingerprint(size=1024)
      elif featurizer == "GraphConv":
          featurizer_func = dc.feat.ConvMolFeaturizer()
  
      permeability_tasks = sorted(["LogP(RRCK)"])
  
      loader = dc.data.SDFLoader(
          tasks=permeability_tasks,
          clean_mols=True,
          featurizer=featurizer_func,
      )
      dataset = loader.featurize(dataset_file)
  
      splitters = {
          "index": dc.splits.IndexSplitter(),
          "random": dc.splits.RandomSplitter(),
          "scaffold": dc.splits.ScaffoldSplitter(),
      }
      splitter = splitters[split]
      train, valid, test = splitter.train_valid_test_split(dataset)
      return permeability_tasks, (train, valid, test), []
  
file_path: deepchem/examples/membrane_permeability/membrane_permeability_datasets.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import os
  import numpy
  import shutil
  import deepchem
label_desc: Use image node_id 1 for calling a global function with example usage: load_permeability(featurizer, split) and returns: permeability_tasks,  train,  valid,  test,  list