class_docstr: Stratified Bootstrap where each input is independently resampled.
  
  This bootstrap is useful for computing CIs for metrics which take multiple
  score arrays, possibly with different number of runs, as input, such as
  average probability of improvement. See also: `StratifiedBootstrap` and
  `arch_bs.IndependentSamplesBootstrap`.
  
  Attributes:
    data: tuple, Two-element tuple with the pos_data in the first position and
      kw_data in the second (pos_data, kw_data). Derived from
      `IndependentSamplesBootstrap`.
    pos_data: tuple, Tuple containing the positional arguments (in the order
      entered). Derived from `IndependentSamplesBootstrap`.
    kw_data: dict, Dictionary containing the keyword arguments. Derived from
      `IndependentSamplesBootstrap`.
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docstr: Samples partial indices for bootstrap resamples.
  
  Args:
    array_shape: Shape of array for which strata indices are created.
  
  Returns:
    A list of arrays of size N x 1 x 1 x .., 1 x M x 1 x ..,
    1 x 1 x L x .. and so on, where the `array_shape` is `N x M x L x ..`.
docstr_tok: ["Samples","partial","indices","for","bootstrap","resamples",".","Args",":","array_shape",":","Shape","of","array","for","which","strata","indices","are","created",".","Returns",":","A","list","of","arrays","of","size","N","x","1","x","1","x","..",",","1","x","M","x","1","x","..",",","1","x","1","x","L","x","..","and","so","on",",","where","the","`","array_shape","`","is","`","N","x","M","x","L","x","..","`","."]
code: def _get_strata_indices(
      self, array_shape: Tuple[int, ...]
  ) -> List[np.ndarray]:
      """Samples partial indices for bootstrap resamples.
  
      Args:
        array_shape: Shape of array for which strata indices are created.
  
      Returns:
        A list of arrays of size N x 1 x 1 x .., 1 x M x 1 x ..,
        1 x 1 x L x .. and so on, where the `array_shape` is `N x M x L x ..`.
      """
      ogrid_indices = tuple(slice(x) for x in (0, *array_shape[1:]))
      strata_indices = np.ogrid[ogrid_indices]
      return strata_indices[1:]
  
file_path: rliable/rliable/library.py
imports: from typing import Callable, Dict, List, Optional, Tuple, Union, Mapping
  from absl import logging
  import arch.bootstrap
  import numpy
  from numpy import random
label_desc: Use image node_id 2 for calling the StratifiedIndependentBootstrap obj's underlying member method code with example usage: obj._get_strata_indices(array_shape) and returns: strata_indices