code: def test_multi_pca_with_confounds_smoke(multi_pca_data, mask_img):
      confounds = [np.arange(10).reshape(5, 2)] * 8
  
      multi_pca = _MultiPCA(
          mask=mask_img, n_components=3, random_state=0
      )
      multi_pca.fit(multi_pca_data, confounds=confounds)
  
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 7 for calling a global function with example usage: test_multi_pca_with_confounds_smoke(multi_pca_data, mask_img) without return types