code: def test_component_sign(mask_img):
      # Regression test
      # We should have a heuristic that flips the sign of components in
      # DictLearning to have more positive values than negative values, for
      # instance by making sure that the largest value is positive.
  
      data, components, rng = _make_canica_test_data(
          n_subjects=2, noisy=True
      )
      for mp in components:
          assert -mp.min() <= mp.max()
  
      dict_learning = DictLearning(
          n_components=4,
          random_state=rng,
          mask=mask_img,
          smoothing_fwhm=0.0,
          alpha=1,
      )
      dict_learning.fit(data)
      for mp in iter_img(dict_learning.components_img_):
          mp = get_data(mp)
          assert np.sum(mp[mp <= 0]) <= np.sum(mp[mp > 0])
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image
  from nilearn._utils.testing import write_imgs_to_path
  from nilearn.conftest import _affine_eye
  from nilearn.decomposition.dict_learning import DictLearning
  from nilearn.decomposition.tests.test_canica import _make_canica_test_data
  from nilearn.image import get_data, iter_img
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 5 for calling a global function with example usage: test_component_sign(mask_img) without return types