code: def test_compute_multi_epi_mask(affine_eye):
      # Check that an empty list of images creates a meaningful error
      with pytest.raises(TypeError):
          compute_multi_epi_mask([])
      # As it calls intersect_masks, we only test resampling here.
      # Same masks as test_intersect_masks
      mask_a = np.zeros((4, 4, 1), dtype=bool)
      mask_a[2:4, 2:4] = 1
      mask_a_img = Nifti1Image(mask_a.astype("uint8"), affine_eye)
  
      mask_b = np.zeros((8, 8, 1), dtype=bool)
      mask_b[2:6, 2:6] = 1
      mask_b_img = Nifti1Image(mask_b.astype("uint8"), affine_eye / 2.0)
  
      with warnings.catch_warnings():
          warnings.simplefilter("ignore", _MaskWarning)
          with pytest.raises(ValueError):
              compute_multi_epi_mask([mask_a_img, mask_b_img])
      mask_ab = np.zeros((4, 4, 1), dtype=bool)
      mask_ab[2, 2] = 1
      mask_ab_ = compute_multi_epi_mask(
          [mask_a_img, mask_b_img],
          threshold=1.0,
          opening=0,
          target_affine=affine_eye,
          target_shape=(4, 4, 1),
      )
      assert_array_equal(mask_ab, get_data(mask_ab_))
  
imports: import warnings
  import numpy
  import pytest
  from nibabel import Nifti1Image
  from numpy.testing import assert_array_equal
  from sklearn.preprocessing import StandardScaler
  from nilearn import masking
  from nilearn._utils import data_gen
  from nilearn._utils.exceptions import DimensionError
  from nilearn._utils.testing import write_imgs_to_path
  from nilearn.conftest import _rng
  from nilearn.image import get_data, high_variance_confounds
  from nilearn.maskers import NiftiMasker
  from nilearn.masking import _MaskWarning, _unmask_3d, _unmask_4d, compute_background_mask, compute_brain_mask, compute_epi_mask, compute_multi_brain_mask, compute_multi_epi_mask, extrapolate_out_mask, intersect_masks, unmask, unmask_from_to_3d_array
label_desc: Use image node_id 13 for calling a global function with example usage: test_compute_multi_epi_mask(affine_eye) without return types