code: def _confounds_regression(
      standardize_signal="zscore_sample", standardize_confounds=True
  ):
      img, mask, conf = _simu_img()
      masker = NiftiMasker(
          standardize=standardize_signal,
          standardize_confounds=standardize_confounds,
          detrend=False,
          mask_img=mask,
      ).fit()
      tseries = masker.transform(img, confounds=conf)
      if standardize_confounds:
          conf = StandardScaler(with_std=False).fit_transform(conf)
      cov_mat = _cov_conf(tseries, conf)
      return np.sum(np.abs(cov_mat))
  
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 3 for calling a global function with example usage: _confounds_regression(standardize_signal, standardize_confounds) and returns: np