code: def test_elasticnet_propensity_model(generate_regression_data):
      y, X, treatment, tau, b, e = generate_regression_data()
  
      pm = ElasticNetPropensityModel(random_state=RANDOM_SEED)
      ps = pm.fit_predict(X, treatment)
  
      assert roc_auc_score(treatment, ps) > 0.5
  
file_path: causalml/tests/test_propensity.py
imports: from causalml.propensity import ElasticNetPropensityModel, GradientBoostedPropensityModel, LogisticRegressionPropensityModel
  from causalml.metrics import roc_auc_score
  from .const import RANDOM_SEED
label_desc: Use image node_id 3 for calling a global function with example usage: test_elasticnet_propensity_model(generate_regression_data) without return types