yapf formatted

                                             transformers)

  return pdbbind_tasks, all_dataset, transformers

def load_pdbbind_from_dir(data_folder, index_files, featurizer="grid", split="random", ex_ids=[], save_dir=None):

def load_pdbbind_from_dir(data_folder,

                          index_files,

                          featurizer="grid",

                          split="random",

                          ex_ids=[],

                          save_dir=None):

  """Load and featurize raw PDBBind dataset from a local directory with the option to avoid certain IDs.

    Parameters

      if len(pdb) == 4:

        pdbs.append(pdb)

  protein_files = [

        os.path.join(data_folder, pdb, "%s_protein.pdb" % pdb) for pdb in pdbs if pdb not in ex_ids

      os.path.join(data_folder, pdb, "%s_protein.pdb" % pdb)

      for pdb in pdbs

      if pdb not in ex_ids

  ]

  ligand_files = [

        os.path.join(data_folder, pdb, "%s_ligand.sdf" % pdb) for pdb in pdbs if pdb not in ex_ids

      os.path.join(data_folder, pdb, "%s_ligand.sdf" % pdb)

      for pdb in pdbs

      if pdb not in ex_ids

  ]

  # Extract labels

  labels_tmp = {}

  else:

    raise ValueError("Featurizer not supported")

  print("Featurizing Complexes")

    features, failures = featurizer.featurize_complexes(ligand_files, protein_files)

  features, failures = featurizer.featurize_complexes(ligand_files,

                                                      protein_files)

  # Delete labels for failing elements

  labels = np.delete(labels, failures)

  dataset = deepchem.data.DiskDataset.from_numpy(features, labels)