code: def test_reweighing_sex(old_adult, new_adult):
      """Test that the old and new Reweighing produce the same sample_weights."""
      X, y, sample_weight = new_adult
      orig_rew = OrigReweighing(
          unprivileged_groups=[{"sex": 0}],
          privileged_groups=[{"sex": 1}],
      )
      adult_fair = orig_rew.fit_transform(old_adult)
      rew = Reweighing("sex")
      _, new_sample_weight = rew.fit_transform(
          X, y, sample_weight=sample_weight
      )
  
      assert np.allclose(
          [
              [orig_rew.w_up_unfav, orig_rew.w_up_fav],
              [orig_rew.w_p_unfav, orig_rew.w_p_fav],
          ],
          rew.reweigh_factors_,
      )
      assert np.allclose(adult_fair.instance_weights, new_sample_weight)
  
file_path: AIF360/tests/sklearn/test_reweighing.py
imports: import numpy
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV
  from sklearn.metrics import accuracy_score, make_scorer
  from aif360.algorithms.preprocessing import Reweighing
  from aif360.sklearn.preprocessing import Reweighing, ReweighingMeta
label_desc: Use image node_id 1 for calling a global function with example usage: test_reweighing_sex(old_adult, new_adult) without return types