code: def test_nilearn_standardize_false(tmp_path):
      """Test removing confounds with no standardization."""
      # niftimasker default:
      # standardize=False, standardize_confounds=True, detrend=False
  
      # Simulate data; set demean to False as standardize_confounds=True
      (img, mask_conf, mask_rand, confounds, sample_mask) = _simu_img(
          tmp_path, demean=False
      )
  
      # Check that most variance is removed
      # in voxels composed of pure confounds
      tseries_std = _tseries_std(
          img,
          mask_conf,
          confounds,
          sample_mask,
          standardize_signal=False,
          standardize_confounds=True,
          detrend=False,
      )
      assert np.mean(tseries_std < 0.0001)
  
      # Check that most variance is preserved
      # in voxels composed of random noise
      tseries_std = _tseries_std(
          img,
          mask_rand,
          confounds,
          sample_mask,
          standardize_signal=False,
          standardize_confounds=True,
          detrend=False,
      )
      assert np.mean(tseries_std > 0.9)
  
imports: import re
  import numpy
  import pandas
  import pytest
  from nibabel import Nifti1Image
  from scipy.stats import pearsonr
  from sklearn.preprocessing import scale
  from nilearn._utils.data_gen import create_fake_bids_dataset
  from nilearn._utils.fmriprep_confounds import to_camel_case
  from nilearn.conftest import _rng
  from nilearn.interfaces.bids import get_bids_files
  from nilearn.interfaces.fmriprep import load_confounds
  from nilearn.interfaces.fmriprep.load_confounds import _check_strategy, _load_single_confounds_file
  from nilearn.interfaces.fmriprep.tests._testing import create_tmp_filepath, get_legal_confound
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 8 for calling a global function with example usage: test_nilearn_standardize_false(tmp_path) without return types