code: def test_outlier_cut_coords():
      """Test to plot a subset of a large set of cuts found for a small area."""
      bg_img = load_mni152_template(resolution=2)
      data = np.zeros((79, 95, 79))
      affine = np.array(
          [
              [-2.0, 0.0, 0.0, 78.0],
              [0.0, 2.0, 0.0, -112.0],
              [0.0, 0.0, 2.0, -70.0],
              [0.0, 0.0, 0.0, 1.0],
          ]
      )
      # Color a cube around a corner area:
      x, y, z = 20, 22, 60
      x_map, y_map, z_map = coord_transform(
          x, y, z, np.linalg.inv(affine)
      )
      data[
          int(x_map) - 1 : int(x_map) + 1,
          int(y_map) - 1 : int(y_map) + 1,
          int(z_map) - 1 : int(z_map) + 1,
      ] = 1
      img = Nifti1Image(data, affine)
      cuts = find_cut_slices(img, n_cuts=20, direction="z")
      plot_stat_map(
          img, display_mode="z", cut_coords=cuts[-4:], bg_img=bg_img
      )
  
imports: import matplotlib.pyplot
  import numpy
  import pytest
  from nibabel import Nifti1Image
  from nilearn.conftest import _rng
  from nilearn.datasets import load_mni152_template
  from nilearn.image import get_data
  from nilearn.image.resampling import coord_transform
  from nilearn.plotting import plot_stat_map
  from nilearn.plotting.find_cuts import find_cut_slices
label_desc: Use image node_id 9 for calling a global function with example usage: test_outlier_cut_coords() without return types