code: def test_signals_extraction_with_labels_without_mask_return_masked_atlas(
      signals, labels_img
  ):
      """Test masked_atlas is correct in conversion between signals and images \
      using regions defined by labels."""
      data_img = signals_to_img_labels(
          signals=signals, labels_img=labels_img
      )
  
      # test return_masked_atlas
      (
          _,
          _,
          masked_atlas_r,
      ) = img_to_signals_labels(
          imgs=data_img,
          labels_img=labels_img,
          return_masked_atlas=True,
      )
  
      labels_data = get_data(labels_img)
      labels_data_r = get_data(masked_atlas_r)
  
      # masked_atlas_r should be the same as labels_img
      assert_equal(labels_data_r, labels_data)
  
      # labels should be the same as before
      # the labels_img does not contain background
      assert list(np.unique(labels_data_r)) == list(range(1, 9))
  
imports: import warnings
  import numpy
  import pytest
  from nibabel import Nifti1Image
  from numpy.testing import assert_almost_equal, assert_equal
  from nilearn._utils.data_gen import generate_fake_fmri, generate_labeled_regions, generate_maps, generate_timeseries
  from nilearn._utils.exceptions import DimensionError
  from nilearn._utils.testing import write_imgs_to_path
  from nilearn.conftest import _affine_eye, _shape_3d_default
  from nilearn.image import get_data, new_img_like
  from nilearn.maskers import NiftiLabelsMasker
  from nilearn.regions.signal_extraction import _check_shape_and_affine_compatibility, _trim_maps, img_to_signals_labels, img_to_signals_maps, signals_to_img_labels, signals_to_img_maps
label_desc: Use image node_id 24 for calling a global function with example usage: test_signals_extraction_with_labels_without_mask_return_masked_atlas(signals, labels_img) without return types