code: def test_vif_with_enum_only():
      print(
          "Testing glm cross-validation with alpha array, default lambda values for binomial models."
      )
      h2o_data = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/binomial_20_cols_10KRows.csv"
          )
      )
      enum_columns = [
          "C1",
          "C2",
          "C3",
          "C4",
          "C5",
          "C6",
          "C7",
          "C8",
          "C9",
          "C10",
      ]
      for cname in enum_columns:
          h2o_data[cname] = h2o_data[cname].asfactor()
      myY = "C21"
      h2o_data["C21"] = h2o_data["C21"].asfactor()
      myX = ["C1", "C2", "C3", "C4", "C5"]
      try:
          model = H2OGeneralizedLinearEstimator(
              family="binomial",
              lambda_=0,
              generate_variable_inflation_factors=True,
          )
          model.train(training_frame=h2o_data, x=myX, y=myY)
          assert False, "Should have thrown an exception!"
      except Exception as ex:
          print(ex)
          temp = str(ex)
          assert (
              "generate_variable_inflation_factors:  cannot be enabled for GLM models with only non-numerical predictors."
              in temp
          ), "Wrong exception was received."
  
      print("glm Multinomial makeGLMModel test completed!")
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_vif_with_enum_only() without return types