class_docstr: Random permutation cross-validator.
  
  Yields indices to split data into training and test sets.
  
  .. warning::
  
     By default, this performs a blockwise-shuffle. That is,
     each block is shuffled internally, but data are not shuffled
     between blocks. If your data is ordered, then set ``blockwise=False``.
  
  Note: contrary to other cross-validation strategies, random splits
  do not guarantee that all folds will be different, although this is
  still very likely for sizeable datasets.
  
  Parameters
  ----------
  n_splits : int, default 10
      Number of re-shuffling & splitting iterations.
  
  test_size : float, int, None, default=0.1
      If float, should be between 0.0 and 1.0 and represent the proportion
      of the dataset to include in the test split. If int, represents the
      absolute number of test samples. If None, the value is set to the
      complement of the train size.
  
  train_size : float, int, or None, default=None
      If float, should be between 0.0 and 1.0 and represent the
      proportion of the dataset to include in the train split. If
      int, represents the absolute number of train samples. If None,
      the value is automatically set to the complement of the test size.
  
  blockwise : bool, default True
      Whether to shuffle data only within blocks (True), or allow data to
      be shuffled between blocks (False). Shuffling between blocks can
      be much more expensive, especially in distributed environments.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
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code: def get_n_splits(self, X=None, y=None, groups=None):
      return self.n_splits
  
imports: import itertools
  import logging
  import numbers
  import warnings
  import dask
  import dask.array
  import dask.dataframe
  import numpy
  import sklearn.model_selection
  from sklearn.model_selection._split import BaseCrossValidator, _validate_shuffle_split
  from sklearn.utils import check_random_state
  from dask_ml._compat import DASK_2130, DASK_VERSION
  from dask_ml.utils import check_array, check_matching_blocks
  from .._utils import draw_seed
label_desc: Use image node_id 5 for calling the ShuffleSplit obj's underlying member method code with example usage: obj.get_n_splits(X, y, groups) and returns: self