code: def test_region_extractor_fit_and_transform(maps_and_mask):
      maps, mask_img = maps_and_mask
  
      # Test maps are zero in the mask
      mask_data = get_data(mask_img)
      mask_data[1, 1, 1] = 0
      extractor_without_mask = RegionExtractor(maps)
      extractor_without_mask.fit()
      extractor_with_mask = RegionExtractor(maps, mask_img=mask_img)
      extractor_with_mask.fit()
  
      assert not np.all(
          get_data(extractor_without_mask.regions_img_)[mask_data == 0]
          == 0.0
      )
      assert np.all(
          get_data(extractor_with_mask.regions_img_)[mask_data == 0]
          == 0.0
      )
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image
  from scipy.ndimage import label
  from nilearn._utils.data_gen import generate_labeled_regions, generate_maps
  from nilearn._utils.exceptions import DimensionError
  from nilearn.conftest import _affine_eye, _img_4d_zeros, _shape_3d_default
  from nilearn.image import get_data
  from nilearn.regions import RegionExtractor, connected_label_regions, connected_regions
  from nilearn.regions.region_extractor import _remove_small_regions, _threshold_maps_ratio
label_desc: Use image node_id 16 for calling a global function with example usage: test_region_extractor_fit_and_transform(maps_and_mask) without return types