code: def test_small_radius(rng):
      affine = np.eye(4)
      shape = (3, 3, 3)
  
      data = rng.random(shape)
      mask = np.zeros(shape)
      mask[1, 1, 1] = 1
      mask[2, 2, 2] = 1
      affine = np.eye(4) * 1.2
      seed = (1.4, 1.4, 1.4)
  
      masker = NiftiSpheresMasker(
          [seed], radius=0.1, mask_img=nibabel.Nifti1Image(mask, affine)
      )
      masker.fit_transform(nibabel.Nifti1Image(data, affine))
  
      # Test if masking is taken into account
      mask[1, 1, 1] = 0
      mask[1, 1, 0] = 1
  
      masker = NiftiSpheresMasker(
          [seed], radius=0.1, mask_img=nibabel.Nifti1Image(mask, affine)
      )
      with pytest.raises(ValueError, match="These spheres are empty"):
          masker.fit_transform(nibabel.Nifti1Image(data, affine))
  
      masker = NiftiSpheresMasker(
          [seed], radius=1.6, mask_img=nibabel.Nifti1Image(mask, affine)
      )
      masker.fit_transform(nibabel.Nifti1Image(data, affine))
  
imports: import warnings
  import nibabel
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from nilearn._utils import data_gen
  from nilearn.image import get_data, new_img_like
  from nilearn.maskers import NiftiSpheresMasker
label_desc: Use image node_id 6 for calling a global function with example usage: test_small_radius(rng) without return types