class_docstr: Class for splits based on input order. 
class_docstr_tok: ["Class","for","splits","based","on","input","order","."]
docstr: Parameters
  -----------
  valid_indices: list of int
      indices of samples in the valid set
  test_indices: list of int
      indices of samples in the test set
docstr_tok: ["Parameters","--","--","--","--","--","-","valid_indices",":","list","of","int","indices","of","samples","in","the","valid","set","test_indices",":","list","of","int","indices","of","samples","in","the","test","set"]
code: def __init__(
      self, verbose=False, valid_indices=None, test_indices=None
  ):
      """
      Parameters
      -----------
      valid_indices: list of int
          indices of samples in the valid set
      test_indices: list of int
          indices of samples in the test set
      """
      self.verbose = verbose
      self.valid_indices = valid_indices
      self.test_indices = test_indices
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import tempfile
  import numpy
  import itertools
  from rdkit import Chem
  from rdkit import DataStructs
  from rdkit.Chem import AllChem
  from rdkit.ML.Cluster import Butina
  import deepchem
  from deepchem.data import DiskDataset
  from deepchem.utils import ScaffoldGenerator
  from deepchem.utils.save import log
  from deepchem.data import NumpyDataset
  from deepchem.utils.save import load_data
label_desc: Use image node_id 1 to create a new IndiceSplitter object from inherited base classes: Splitter with example: obj = IndiceSplitter(verbose, valid_indices, test_indices)