code: def testOrdinalLogit():
      Dtrain = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/glm_ordinal_logit/ordinal_ordinal_20_training_set.csv"
          )
      )
      Dtrain["C21"] = Dtrain["C21"].asfactor()
  
      print("Fit model on dataset")
      model = glm(
          family="ordinal",
          alpha=[0.5],
          lambda_=[0.001],
          max_iterations=1000,
          beta_epsilon=1e-8,
          objective_epsilon=1e-8,
      )
      model.train(x=list(range(0, 20)), y="C21", training_frame=Dtrain)
      predH2O = model.predict(Dtrain)
      r = glm.getGLMRegularizationPath(model)
      m2 = glm.makeGLMModel(
          model=model, coefs=r["coefficients"][0]
      )  # model generated from setting coefficients to model
      f2 = m2.predict(Dtrain)
      pyunit_utils.compare_frames_local(predH2O, f2, prob=1)
      coefs = r["coefficients"][0]
      coefs["h2o_dream"] = 3.1415
  
      try:
          glm.makeGLMModel(model=model, coefs=coefs)
          assert False, "Should have thrown an exception!"
      except Exception as ex:
          print(ex)
          temp = str(ex)
          assert (
              "Server error java.lang.IllegalArgumentException:" in temp
          ) and (
              "model coefficient length 189 is different from coefficient provided by user "
          ) in temp, "Wrong exception was received."
          print("coefficient test passed!")
  
label_desc: Use image node_id 1 for calling a global function with example usage: testOrdinalLogit() without return types