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`.
class_docstr_tok: ["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","`","."]
docstr: Update independent sampling indices for the next bootstrap iteration.
docstr_tok: ["Update","independent","sampling","indices","for","the","next","bootstrap","iteration","."]
code: def update_indices(
      self,
  ) -> Tuple[
      List[Tuple[np.ndarray, ...]], Dict[str, Tuple[np.ndarray, ...]]
  ]:
      """Update independent sampling indices for the next bootstrap iteration."""
  
      pos_indices = [
          self._get_indices(
              self._num_arg_items[i],
              self._args_shapes[i],
              self._args_strata_indices[i],
          )
          for i in range(self._num_args)
      ]
      kw_indices = {}
      for key in self._kwargs:
          kw_indices[key] = self._get_indices(
              self._num_kw_items[key],
              self._kwargs_shapes[key],
              self._kwargs_strata_indices[key],
          )
      return pos_indices, kw_indices
  
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 4 for calling the StratifiedIndependentBootstrap obj's underlying member method code with example usage: obj.update_indices() and returns: pos_indices,  kw_indices