code: def test_all_resolution_inference(data_norm_isf, affine_eye):
      data = data_norm_isf
      data[2:4, 5:7, 6:8] = 5.0
      stat_img = Nifti1Image(data, affine_eye)
  
      # standard case
      th_map = cluster_level_inference(
          stat_img, threshold=3, alpha=0.05
      )
      vals = get_data(th_map)
  
      assert np.sum(vals > 0) == 8
  
      # high threshold
      th_map = cluster_level_inference(
          stat_img, threshold=6, alpha=0.05
      )
      vals = get_data(th_map)
  
      assert np.sum(vals > 0) == 0
  
      # list of thresholds
      th_map = cluster_level_inference(
          stat_img, threshold=[3, 6], alpha=0.05
      )
      vals = get_data(th_map)
  
      assert np.sum(vals > 0) == 8
  
      # verbose mode
      th_map = cluster_level_inference(
          stat_img, threshold=3, alpha=0.05, verbose=True
      )
  
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 8 for calling a global function with example usage: test_all_resolution_inference(data_norm_isf, affine_eye) without return types