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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code: def __init__(self, n_splits=200, random_seed=None):
      self.random_seed = random_seed
  
      if not isinstance(n_splits, int) or n_splits < 1:
          raise ValueError("Number of splits must be greater than 1.")
      self.n_splits = n_splits
  
imports: import numpy
label_desc: Use image node_id 1 to create a new BootstrapOutOfBag object from inherited base classes: object with example: obj = BootstrapOutOfBag(n_splits, random_seed)