code: def test_connected_label_regions_return_empty_for_large_min_size(
      labels_img,
  ):
      """If min_size is large and if all the regions are removed \
      then empty image will be returned."""
      extract_reg_min_size_large = connected_label_regions(
          labels_img, min_size=500
      )
  
      assert np.unique(get_data(extract_reg_min_size_large)) == 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 25 for calling a global function with example usage: test_connected_label_regions_return_empty_for_large_min_size(labels_img) without return types