code: def test_uneven_sampling(bs_setup, seed):
      bs = MovingBlockBootstrap(
          block_size=31, y=bs_setup.y_series, x=bs_setup.x_df, seed=seed
      )
      for _, kw in bs.bootstrap(10):
          assert kw["y"].shape == bs_setup.y_series.shape
          assert kw["x"].shape == bs_setup.x_df.shape
      bs = CircularBlockBootstrap(
          block_size=31, y=bs_setup.y_series, x=bs_setup.x_df, seed=seed
      )
      for _, kw in bs.bootstrap(10):
          assert kw["y"].shape == bs_setup.y_series.shape
          assert kw["x"].shape == bs_setup.x_df.shape
  
imports: from __future__ import annotations
  import copy
  from typing import Callable, NamedTuple
  import warnings
  import numpy
  from numpy.random import RandomState
  from numpy.testing import assert_allclose, assert_equal
  import pandas
  from pandas.testing import assert_frame_equal, assert_series_equal
  import pytest
  import scipy.stats
  from arch.bootstrap import CircularBlockBootstrap, IIDBootstrap, IndependentSamplesBootstrap, MovingBlockBootstrap, StationaryBootstrap
  from arch.bootstrap._samplers_python import stationary_bootstrap_sample
  from arch.bootstrap._samplers_python import stationary_bootstrap_sample_python
  from arch.bootstrap.base import _loo_jackknife
  from arch.utility.exceptions import StudentizationError
label_desc: Use image node_id 23 for calling a global function with example usage: test_uneven_sampling(bs_setup, seed) without return types