code: def testFrameTransform():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/anovaGlm/Moore.csv")
      )
      answer = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/anovaGlm/MooreTransformed.csv"
          )
      )
      y = "conformity"
      x = ["fcategory", "partner.status"]
  
      model = H2OANOVAGLMEstimator(
          family="gaussian", lambda_=0, save_transformed_framekeys=True
      )
      model.train(x=x, y=y, training_frame=train)
      transformFrame = h2o.get_frame(
          model._model_json["output"]["transformed_columns_key"]["name"]
      )
      partAnswer = answer[
          [
              "fcategory1",
              "fcategory2",
              "partner.status1",
              "fcategory1:partner.status1",
              "fcategory2:partner.status1",
          ]
      ]
      partTransformFrame = transformFrame[
          [
              "fcategory_high",
              "fcategory_low",
              "partner.status_high",
              "fcategory_high:partner.status_high",
              "fcategory_low:partner.status_high",
          ]
      ]
      partAnswer.set_names(partTransformFrame.names)
      pyunit_utils.compare_frames_local(
          partAnswer, partTransformFrame, prob=1
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.anovaglm import H2OANOVAGLMEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: testFrameTransform() without return types