code: def checkpoint_new_category_in_predictor():
      sv1 = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv")
      )
      sv2 = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv")
      )
      vir = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/virginica.csv")
      )
  
      from h2o.estimators.random_forest import H2ORandomForestEstimator
  
      m1 = H2ORandomForestEstimator(ntrees=100)
      m1.train(x=[0, 1, 2, 4], y=3, training_frame=sv1)
  
      m2 = H2ORandomForestEstimator(ntrees=200, checkpoint=m1.model_id)
      m2.train(x=[0, 1, 2, 4], y=3, training_frame=sv2)
  
      # attempt to continue building model, but with an expanded categorical predictor domain.
      # this should fail until we figure out proper behavior
      try:
          m2.train(x=[0, 1, 2, 4], y=3, training_frame=vir)
          assert (
              False
          ), "Expected continued model-building to fail with new categories introduced in predictor"
      except EnvironmentError:
          pass
  
label_desc: Use image node_id 1 for calling a global function with example usage: checkpoint_new_category_in_predictor() without return types