code: def test_multi_pca_score_single_subject(mask_img):
      """Multi-pca can run on single subject data."""
      data, _, _, _ = _make_multi_pca_test_data(with_activation=False)
  
      multi_pca = _MultiPCA(
          mask=mask_img, random_state=0, memory_level=0, n_components=3
      )
      multi_pca.fit(data[0])
      s = multi_pca.score(data[0])
  
      assert isinstance(s, float)
      assert 0.0 <= s <= 1.0
  
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 14 for calling a global function with example usage: test_multi_pca_score_single_subject(mask_img) without return types