code: def checkpoint_new_category_in_response():
      sv = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv")
      )
      iris = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/iris.csv")
      )
  
      sv = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv")
      )
      iris = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/iris.csv")
      )
  
      m1 = H2ODeepLearningEstimator(epochs=100)
      m1.train(x=[0, 1, 2, 3], y=4, training_frame=sv)
  
      # attempt to continue building model, but with an expanded categorical response domain.
      # this should fail
      try:
          m2 = H2ODeepLearningEstimator(
              checkpoint=m1.model_id, epochs=200
          )
          m2.train(x=[0, 1, 2, 3], y=4, training_frame=iris)
          assert (
              False
          ), "Expected continued model-building to fail with new categories introduced in response"
      except EnvironmentError:
          pass
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: checkpoint_new_category_in_response() without return types