class_docstr: Repeated Multilabel Stratified K-Fold cross validator.
  Repeats Mulilabel Stratified K-Fold n times with different randomization
  in each repetition.
  Parameters
  ----------
  n_splits : int, default=5
      Number of folds. Must be at least 2.
  n_repeats : int, default=10
      Number of times cross-validator needs to be repeated.
  random_state : None, int or RandomState, default=None
      Random state to be used to generate random state for each
      repetition as well as randomly breaking ties within the iterative
      stratification algorithm.
  Examples
  --------
  >>> from iterstrat.ml_stratifiers import RepeatedMultilabelStratifiedKFold
  >>> import numpy as np
  >>> X = np.array([[1,2], [3,4], [1,2], [3,4], [1,2], [3,4], [1,2], [3,4]])
  >>> y = np.array([[0,0], [0,0], [0,1], [0,1], [1,1], [1,1], [1,0], [1,0]])
  >>> rmskf = RepeatedMultilabelStratifiedKFold(n_splits=2, n_repeats=2,
  ...     random_state=0)
  >>> for train_index, test_index in rmskf.split(X, y):
  ...     print("TRAIN:", train_index, "TEST:", test_index)
  ...     X_train, X_test = X[train_index], X[test_index]
  ...     y_train, y_test = y[train_index], y[test_index]
  ...
  TRAIN: [0 3 4 6] TEST: [1 2 5 7]
  TRAIN: [1 2 5 7] TEST: [0 3 4 6]
  TRAIN: [0 1 4 5] TEST: [2 3 6 7]
  TRAIN: [2 3 6 7] TEST: [0 1 4 5]
  See also
  --------
  RepeatedStratifiedKFold: Repeats (Non-multilabel) Stratified K-Fold
  n times.
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code: def __init__(self, n_splits=5, *, n_repeats=10, random_state=None):
      super(RepeatedMultilabelStratifiedKFold, self).__init__(
          MultilabelStratifiedKFold,
          n_repeats=n_repeats,
          random_state=random_state,
          n_splits=n_splits,
      )
  
file_path: iterative-stratification/iterstrat/ml_stratifiers.py
imports: import numpy
  from sklearn.utils import check_random_state
  from sklearn.utils.validation import _num_samples, check_array
  from sklearn.utils.multiclass import type_of_target
  from sklearn.model_selection._split import _BaseKFold, _RepeatedSplits, BaseShuffleSplit, _validate_shuffle_split
label_desc: Use image node_id 1 to create a new RepeatedMultilabelStratifiedKFold object from inherited base classes: _RepeatedSplits with example: obj = RepeatedMultilabelStratifiedKFold(n_splits)