class_docstr: Parameters
  ----------
  
  n_splits : int (default=200)
      Number of bootstrap iterations.
      Must be larger than 1.
  
  random_seed : int (default=None)
      If int, random_seed is the seed used by
      the random number generator.
  
  
  Returns
  -------
  train_idx : ndarray
      The training set indices for that split.
  
  test_idx : ndarray
      The testing set indices for that split.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/evaluate/BootstrapOutOfBag/
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docstr: Returns the number of splitting iterations in the cross-validator
  
  Parameters
  ----------
  X : object
      Always ignored, exists for compatibility with scikit-learn.
  
  y : object
      Always ignored, exists for compatibility with scikit-learn.
  
  groups : object
      Always ignored, exists for compatibility with scikit-learn.
  
  Returns
  -------
  
  n_splits : int
      Returns the number of splitting iterations in the cross-validator.
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code: def get_n_splits(self, X=None, y=None, groups=None):
      """Returns the number of splitting iterations in the cross-validator
  
      Parameters
      ----------
      X : object
          Always ignored, exists for compatibility with scikit-learn.
  
      y : object
          Always ignored, exists for compatibility with scikit-learn.
  
      groups : object
          Always ignored, exists for compatibility with scikit-learn.
  
      Returns
      -------
  
      n_splits : int
          Returns the number of splitting iterations in the cross-validator.
  
      """
      return self.n_splits
  
imports: import numpy
label_desc: Use image node_id 3 for calling the BootstrapOutOfBag obj's underlying member method code with example usage: obj.get_n_splits(X, y, groups) and returns: self