code: def _simu_img():
      # Random confounds
      rng = _rng()
      conf = 2 + rng.standard_normal((100, 6))
      # Random 4D volume
      vol = 100 + 10 * rng.standard_normal((5, 5, 2, 100))
      img = Nifti1Image(vol, np.eye(4))
      # Create an nifti image with the data, and corresponding mask
      mask = Nifti1Image(np.ones([5, 5, 2]), np.eye(4))
      return img, mask, conf
  
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 1 for calling a global function with example usage: _simu_img() and returns: img,  mask,  conf