code: def test_threshold_stats_img_no_height_control(
      data_norm_isf, img_3d_ones_eye, affine_eye
  ):
      data = data_norm_isf
      data[2:4, 5:7, 6:8] = 5.0
      stat_img = Nifti1Image(data, affine_eye)
  
      # excessive cluster forming threshold
      th_map, _ = threshold_stats_img(
          stat_img,
          mask_img=img_3d_ones_eye,
          threshold=100,
          height_control=None,
          cluster_threshold=0,
      )
      vals = get_data(th_map)
  
      assert np.sum(vals > 0) == 0
  
      # direct threshold
      th_map, _ = threshold_stats_img(
          stat_img,
          mask_img=img_3d_ones_eye,
          threshold=4.0,
          height_control=None,
          cluster_threshold=0,
      )
      vals = get_data(th_map)
  
      assert np.sum(vals > 0) == 8
  
      # without mask
      th_map, _ = threshold_stats_img(
          stat_img,
          None,
          threshold=4.0,
          height_control=None,
          cluster_threshold=0,
      )
      vals = get_data(th_map)
  
      assert np.sum(vals > 0) == 8
  
      # without a map
      th_map, threshold = threshold_stats_img(
          None,
          None,
          threshold=3.0,
          height_control=None,
          cluster_threshold=0,
      )
  
      assert threshold == 3.0
      assert th_map is None
  
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 4 for calling a global function with example usage: test_threshold_stats_img_no_height_control(data_norm_isf, img_3d_ones_eye, affine_eye) without return types