code: def test_passing_of_ncuts_in_find_cut_slices(affine_eye):
      data = np.zeros((50, 50, 50))
      x_map, y_map, z_map = 25, 5, 20
      data[
          x_map - 15 : x_map + 15,
          y_map - 3 : y_map + 3,
          z_map - 10 : z_map + 10,
      ] = 1
      img = nibabel.Nifti1Image(data, affine_eye)
      # smoke test to check if it rounds the floating point inputs
      for n_cuts in (1, 5.0, 0.9999999, 2.000000004):
          cut1 = find_cut_slices(img, direction="x", n_cuts=n_cuts)
          cut2 = find_cut_slices(
              img, direction="x", n_cuts=round(n_cuts)
          )
          np.testing.assert_array_equal(cut1, cut2)
  
file_path: nilearn/nilearn/plotting/tests/test_find_cuts.py
imports: import nibabel
  import numpy
  import pytest
  from nilearn.masking import compute_epi_mask
  from nilearn.plotting.find_cuts import _transform_cut_coords, find_cut_slices, find_parcellation_cut_coords, find_probabilistic_atlas_cut_coords, find_xyz_cut_coords
label_desc: Use image node_id 4 for calling a global function with example usage: test_passing_of_ncuts_in_find_cut_slices(affine_eye) without return types