code: def test_multi_pca_with_masker_without_cca_smoke(multi_pca_data):
      """Multi-pca can run with a masker \
          and without canonical correlation analysis."""
      masker = MultiNiftiMasker(mask_args=dict(opening=0))
  
      multi_pca = _MultiPCA(
          mask=masker,
          do_cca=False,
          n_components=3,
      )
      multi_pca.fit(multi_pca_data[:2])
  
      # Smoke test the transform and inverse_transform
      multi_pca.inverse_transform(
          multi_pca.transform(multi_pca_data[-2:])
      )
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image
  from numpy.testing import assert_almost_equal
  from nilearn._utils.testing import write_imgs_to_path
  from nilearn.conftest import _affine_eye, _rng
  from nilearn.decomposition._multi_pca import _MultiPCA
  from nilearn.maskers import MultiNiftiMasker, NiftiMasker
label_desc: Use image node_id 11 for calling a global function with example usage: test_multi_pca_with_masker_without_cca_smoke(multi_pca_data) without return types