code: def test_unmask_from_to_3d_array(rng, size=5):
      for ndim in range(1, 4):
          shape = [size] * ndim
          mask = np.zeros(shape).astype(bool)
          mask[rng.uniform(size=shape) > 0.8] = 1
          support = rng.standard_normal(size=mask.sum())
          full = unmask_from_to_3d_array(support, mask)
          np.testing.assert_array_equal(full.shape, shape)
          np.testing.assert_array_equal(full[mask], support)
  
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 19 for calling a global function with example usage: test_unmask_from_to_3d_array(rng, size) without return types