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: y : array-like or None (default: None)
      Argument is not used and only included as parameter
      for compatibility, similar to `KFold` in scikit-learn.
  
  groups : array-like or None (default: None)
      Argument is not used and only included as parameter
      for compatibility, similar to `KFold` in scikit-learn.
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code: def split(self, X, y=None, groups=None):
      """
  
      y : array-like or None (default: None)
          Argument is not used and only included as parameter
          for compatibility, similar to `KFold` in scikit-learn.
  
      groups : array-like or None (default: None)
          Argument is not used and only included as parameter
          for compatibility, similar to `KFold` in scikit-learn.
  
  
      """
      rng = np.random.RandomState(self.random_seed)
      sample_idx = np.arange(X.shape[0])
      set_idx = set(sample_idx)
  
      for _ in range(self.n_splits):
          train_idx = rng.choice(
              sample_idx, size=sample_idx.shape[0], replace=True
          )
          test_idx = np.array(list(set_idx - set(train_idx)))
          yield train_idx, test_idx
  
imports: import numpy
label_desc: Use image node_id 2 for calling the BootstrapOutOfBag obj's underlying member method code with example usage: obj.split(X, y, groups) without return types