code: def test_all_resolution_inference_height_control(
      control, affine_eye, img_3d_ones_eye, data_norm_isf
  ):
      # two-side fdr threshold + bonferroni
      data = data_norm_isf
      data[2:4, 5:7, 6:8] = 5.0
      data[0:2, 0:2, 6:8] = -5.0
      stat_img = Nifti1Image(data, affine_eye)
  
      th_map, _ = threshold_stats_img(
          stat_img,
          mask_img=img_3d_ones_eye,
          alpha=0.05,
          height_control=control,
          cluster_threshold=5,
      )
      vals = get_data(th_map)
      assert_equal(np.sum(vals > 0), 8)
      assert_equal(np.sum(vals < 0), 8)
      th_map, _ = threshold_stats_img(
          stat_img,
          mask_img=img_3d_ones_eye,
          alpha=0.05,
          height_control=control,
          cluster_threshold=5,
          two_sided=False,
      )
      vals = get_data(th_map)
  
      assert_equal(np.sum(vals > 0), 8)
      assert_equal(np.sum(vals < 0), 0)
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image
  from numpy.testing import assert_almost_equal, assert_equal
  from scipy.stats import norm
  from nilearn.conftest import _shape_3d_default
  from nilearn.glm import cluster_level_inference, fdr_threshold, threshold_stats_img
  from nilearn.glm.thresholding import _compute_hommel_value
  from nilearn.image import get_data
label_desc: Use image node_id 13 for calling a global function with example usage: test_all_resolution_inference_height_control(control, affine_eye, img_3d_ones_eye, data_norm_isf) without return types