code: def test_gam_transformed_frame_serialization():
      h2o_data = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv"
          )
      )
      h2o_data["C1"] = h2o_data["C1"].asfactor()
      h2o_data["C2"] = h2o_data["C2"].asfactor()
      myX = ["C1", "C2"]
      myY = "C11"
      h2o_data["C11"] = h2o_data["C11"].asfactor()
      h2o_model = H2OGeneralizedAdditiveEstimator(
          family="multinomial",
          gam_columns=["C6", "C7", "C8"],
          keep_gam_cols=True,
          scale=[2, 1, 0],
          num_knots=[5, 5, 5],
          bs=[0, 1, 3],
      )
      h2o_model.train(x=myX, y=myY, training_frame=h2o_data)
      gam_frame = h2o.get_frame(
          h2o_model._model_json["output"]["gam_transformed_center_key"]
      )
      tmpdir = tempfile.mkdtemp()
      filename = os.path.join(tmpdir, "gamXFrame.csv")
      h2o.download_csv(gam_frame, filename)
      model_path = h2o.save_model(h2o_model, tmpdir)
  
      h2o.remove_all()
      loaded_model = h2o.load_model(model_path)
      gam_frame_loaded = h2o.get_frame(
          loaded_model._model_json["output"][
              "gam_transformed_center_key"
          ]
      )
      gam_frame_original = h2o.import_file(filename)
      pyunit_utils.compare_frames_local(
          gam_frame_loaded[2:15],
          gam_frame_original[2:15],
          prob=1,
          tol=1e-6,
      )
      print("Test completed.")
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
  import tempfile
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: test_gam_transformed_frame_serialization() without return types