code: def test_prostate():
      h2o_data = h2o.import_file(
          pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      h2o_data["AGE"] = h2o_data["AGE"].asfactor()
  
      bc = []
  
      name = "AGE"
      lower_bound = 0.1
      upper_bound = 0.5
      bc.append([name, lower_bound, upper_bound])
  
      name = "RACE"
      lower_bound = -0.5
      upper_bound = 0.5
      bc.append([name, lower_bound, upper_bound])
  
      name = "DCAPS"
      lower_bound = -0.4
      upper_bound = 0.4
      bc.append([name, lower_bound, upper_bound])
  
      name = "DPROS"
      lower_bound = -0.3
      upper_bound = 0.3
      bc.append([name, lower_bound, upper_bound])
  
      name = "PSA"
      lower_bound = -0.2
      upper_bound = 0.5
      bc.append([name, lower_bound, upper_bound])
  
      name = "VOL"
      lower_bound = -0.5
      upper_bound = 0.5
      bc.append([name, lower_bound, upper_bound])
  
      name = "GLEASON"
      lower_bound = -0.5
      upper_bound = 0.5
      bc.append([name, lower_bound, upper_bound])
  
      beta_constraints = h2o.H2OFrame(bc)
      beta_constraints.set_names(
          ["names", "lower_bounds", "upper_bounds"]
      )
  
      h2o_glm = H2OGeneralizedLinearEstimator(
          family="binomial",
          nfolds=10,
          alpha=0.5,
          beta_constraints=beta_constraints,
      )
      h2o_glm.train(
          x=list(range(2, h2o_data.ncol)), y=1, training_frame=h2o_data
      )
  
      for i in range(
          len(h2o_glm._model_json["output"]["coefficients_table"][0])
      ):
          for constraint in beta_constraints.as_data_frame().to_numpy():
              if h2o_glm._model_json["output"]["coefficients_table"][0][
                  i
              ].startswith(constraint[0]):
                  assert (
                      h2o_glm._model_json["output"][
                          "coefficients_table"
                      ][1][i]
                      >= constraint[1]
                  )
                  assert (
                      h2o_glm._model_json["output"][
                          "coefficients_table"
                      ][1][i]
                      <= constraint[2]
                  )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_prostate() without return types