code: def test_logistic_regression_propensity_model_model_kwargs(
      generate_regression_data,
  ):
      y, X, treatment, tau, b, e = generate_regression_data()
  
      pm = LogisticRegressionPropensityModel(random_state=123)
  
      assert pm.model.random_state == 123
  
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 2 for calling a global function with example usage: test_logistic_regression_propensity_model_model_kwargs(generate_regression_data) without return types