code: def test_regularization(sensitive_classification_dataset):
      """Tests whether increasing regularization decreases the norm of the coefficient vector"""
      X, y = sensitive_classification_dataset
  
      prev_theta_norm = np.inf
      for C in [1, 0.5, 0.2, 0.1]:
          fair = EqualOpportunityClassifier(
              covariance_threshold=None,
              sensitive_cols=["x1"],
              C=C,
              positive_target=True,
          ).fit(X, y)
          theta_norm = np.abs(np.sum(fair.estimators_[0].coef_))
          assert theta_norm < prev_theta_norm
          prev_theta_norm = theta_norm
  
file_path: scikit-lego/tests/test_estimators/test_equal_opportunity.py
imports: import numpy
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklego.common import flatten
  from sklego.linear_model import EqualOpportunityClassifier
  from sklego.metrics import equal_opportunity_score
  from tests.conftest import classifier_checks, general_checks, nonmeta_checks, select_tests
label_desc: Use image node_id 5 for calling a global function with example usage: test_regularization(sensitive_classification_dataset) without return types