code: def test_resampling_nan_big(affine_eye):
      """Test with an actual resampling, in the case of a bigish hole.
  
      This checks the extrapolation mechanism: if we don't do any
      extrapolation before resampling, the hole creates big
      artefacts
      """
      data = 10 * np.ones((10, 10, 10))
      data[4:6, 4:6, 4:6] = np.nan
      source_img = Nifti1Image(data, 2 * affine_eye)
  
      with pytest.warns(RuntimeWarning):
          resampled_img = resample_img(
              source_img, target_affine=affine_eye
          )
  
      resampled_data = get_data(resampled_img)
      assert_allclose(10, resampled_data[np.isfinite(resampled_data)])
  
imports: import copy
  import math
  import os
  from pathlib import Path
  import numpy
  import pytest
  from nibabel import Nifti1Header, Nifti1Image
  from nibabel.freesurfer import MGHImage
  from numpy.testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal, assert_equal
  from nilearn import _utils
  from nilearn._utils import testing
  from nilearn.conftest import _affine_eye, _rng
  from nilearn.image import get_data
  from nilearn.image.image import _pad_array, crop_img
  from nilearn.image.resampling import BoundingBoxError, coord_transform, from_matrix_vector, get_bounds, reorder_img, resample_img, resample_to_img
label_desc: Use image node_id 21 for calling a global function with example usage: test_resampling_nan_big(affine_eye) without return types