code: def test_prostate():
      h2o_data = h2o.upload_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      h2o_data.summary()
  
      sm_data = pd.read_csv(
          pyunit_utils.locate("smalldata/logreg/prostate.csv")
      ).values
      sm_data_response = sm_data[:, 1]
      sm_data_features = sm_data[:, 2:]
  
      h2o_glm = H2OGeneralizedLinearEstimator(
          family="binomial", nfolds=10, alpha=0.5
      )
      h2o_glm.train(
          x=list(range(2, h2o_data.ncol)), y=1, training_frame=h2o_data
      )
      sm_glm = sm.GLM(
          endog=sm_data_response,
          exog=sm_data_features,
          family=sm.families.Binomial(),
      ).fit()
  
      print(
          "statsmodels null deviance {0}".format(sm_glm.null_deviance)
      )
      print("h2o null deviance {0}".format(h2o_glm.null_deviance()))
      assert (
          abs(sm_glm.null_deviance - h2o_glm.null_deviance()) < 1e-5
      ), "Expected null deviances to be the same"
  
imports: import h2o
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from tests import pyunit_utils
  import pandas
  import statsmodels.api
label_desc: Use image node_id 1 for calling a global function with example usage: test_prostate() without return types