code: def cluster_level_design(rng):
      # create design
      target_var1 = np.arange(0, 10).reshape((-1, 1))  # positive effect
      voxel_vars = np.hstack(
          (
              -target_var1,  # negative effect
              target_var1,  # positive effect
              rng.random((10, 1)),  # random voxel
          )
      )
  
      columns = np.arange(0, voxel_vars.shape[1])
      # create 125 voxels
      chosen_columns = rng.choice(columns, size=125, p=[0.1, 0.1, 0.8])
      # corresponds to 5 x 5 x 5 x 10 niimg
      target_var = voxel_vars[:, chosen_columns]
      tested_var = np.arange(0, 20, 2)
  
      return target_var, tested_var
  
imports: import nibabel
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_less, assert_equal
  from scipy import stats
  from nilearn.conftest import _rng
  from nilearn.maskers import NiftiMasker
  from nilearn.mass_univariate import permuted_ols
label_desc: Use image node_id 9 for calling a global function with example usage: cluster_level_design(rng) and returns: target_var,  tested_var