class_docstr: Splitters split up Datasets into pieces for training/validation/testing.
  
  In machine learning applications, it's often necessary to split up a dataset
  into training/validation/test sets. Or to k-fold split a dataset (that is,
  divide into k equal subsets) for cross-validation. The `Splitter` class is
  an abstract superclass for all splitters that captures the common API across
  splitter classes.
  
  Note that `Splitter` is an abstract superclass. You won't want to
  instantiate this class directly. Rather you will want to use a concrete
  subclass for your application.
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docstr: Return indices for specified split
  
  Parameters
  ----------
  dataset: dc.data.Dataset
      Dataset to be split.
  seed: int, optional (default None)
      Random seed to use.
  frac_train: float, optional (default 0.8)
      The fraction of data to be used for the training split.
  frac_valid: float, optional (default 0.1)
      The fraction of data to be used for the validation split.
  frac_test: float, optional (default 0.1)
      The fraction of data to be used for the test split.
  log_every_n: int, optional (default None)
      Controls the logger by dictating how often logger outputs
      will be produced.
  
  Returns
  -------
  Tuple
      A tuple `(train_inds, valid_inds, test_inds)` of the indices (integers) for
      the various splits.
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code: def split(
      self,
      dataset: Dataset,
      frac_train: float = 0.8,
      frac_valid: float = 0.1,
      frac_test: float = 0.1,
      seed: Optional[int] = None,
      log_every_n: Optional[int] = None,
  ) -> Tuple:
      """Return indices for specified split
  
      Parameters
      ----------
      dataset: dc.data.Dataset
          Dataset to be split.
      seed: int, optional (default None)
          Random seed to use.
      frac_train: float, optional (default 0.8)
          The fraction of data to be used for the training split.
      frac_valid: float, optional (default 0.1)
          The fraction of data to be used for the validation split.
      frac_test: float, optional (default 0.1)
          The fraction of data to be used for the test split.
      log_every_n: int, optional (default None)
          Controls the logger by dictating how often logger outputs
          will be produced.
  
      Returns
      -------
      Tuple
          A tuple `(train_inds, valid_inds, test_inds)` of the indices (integers) for
          the various splits.
      """
      raise NotImplementedError
  
imports: import inspect
  import itertools
  import logging
  import os
  import random
  import tempfile
  from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
  import deepchem
  import numpy
  import pandas
  from deepchem.data import Dataset, DiskDataset
  from deepchem.utils import get_print_threshold
label_desc: Use image node_id 4 for calling the Splitter obj's underlying member method code with example usage: obj.split(dataset, frac_train, frac_valid, frac_test, seed, log_every_n) without return types