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: Initializes StratifiedIndependentSamplesBootstrap.
  
  Args:
    *args: Positional arguments to bootstrap. Typically used for the
      performance on a suite of tasks with multiple runs/episodes. The inputs
      are assumed to be of the shape `(num_runs, num_tasks, ..)`.
    random_state: If specified, ensures reproducibility in uncertainty
      estimates.
    **kwargs: Keyword arguments, passed directly to `IIDBootstrap`.
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code: def __init__(
      self,
      *args: np.ndarray,
      random_state: Optional[random.RandomState] = None,
      **kwargs: np.ndarray,
  ) -> None:
      """Initializes StratifiedIndependentSamplesBootstrap.
  
      Args:
        *args: Positional arguments to bootstrap. Typically used for the
          performance on a suite of tasks with multiple runs/episodes. The inputs
          are assumed to be of the shape `(num_runs, num_tasks, ..)`.
        random_state: If specified, ensures reproducibility in uncertainty
          estimates.
        **kwargs: Keyword arguments, passed directly to `IIDBootstrap`.
      """
  
      super().__init__(*args, random_state=random_state, **kwargs)
      self._args_shapes = [arg.shape for arg in args]
      self._kwargs_shapes = {
          key: val.shape for key, val in self._kwargs.items()
      }
      self._args_strata_indices = [
          self._get_strata_indices(arg_shape)
          for arg_shape in self._args_shapes
      ]
      self._kwargs_strata_indices = {
          key: self._get_strata_indices(kwarg_shape)
          for key, kwarg_shape in self._kwargs_shapes.items()
      }
  
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 1 to create a new StratifiedIndependentBootstrap object from inherited base classes: arch_bs with example: obj = StratifiedIndependentBootstrap()