code: def _tfce_design():
      target_var1 = np.arange(0, 10).reshape((-1, 1))  # positive effect
      target_var = np.hstack(
          (  # corresponds to 3 x 3 x 3 x 10 niimg
              target_var1,  # voxel 1 has positive effect
              -target_var1,  # voxel 2 has negative effect
              _rng().random((10, 25)),  # 25 remaining voxels
          )
      )
      tested_var = np.arange(0, 20, 2)
  
      mask_img = nib.Nifti1Image(np.ones((3, 3, 3)), np.eye(4))
      masker = NiftiMasker(mask_img)
      masker.fit(mask_img)
  
      n_descriptors = np.prod(mask_img.shape)
      n_regressors = 1  # tested_var is 1D
  
      return target_var, tested_var, masker, n_descriptors, n_regressors
  
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 1 for calling a global function with example usage: _tfce_design() and returns: target_var,  tested_var,  masker,  n_descriptors,  n_regressors