code: def _create_mask_with_3_regions_from_labels_data(labels_data, affine):
      """Create a mask containing only 3 regions."""
      mask_data = (
          (labels_data == 1) + (labels_data == 2) + (labels_data == 5)
      )
      return Nifti1Image(mask_data.astype(np.int8), affine)
  
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 2 for calling a global function with example usage: _create_mask_with_3_regions_from_labels_data(labels_data, affine) and returns: Nifti1Image