code: def test_gam_gamColumns():
      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=[1, 1, 1],
          num_knots=[5, 5, 5],
      )
      h2o_model.train(x=myX, y=myY, training_frame=h2o_data)
      gamFrame = h2o.get_frame(
          h2o_model._model_json["output"]["gam_transformed_center_key"]
      )
      gamFrame = gamFrame.drop("C1").drop("C2").drop("C11")
      gamFrameAns = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/gam_test/multinomial_10_classes_10_cols_10000_Rows_train_C6Gam_center.csv"
          )
      )
      gamFrameAns = gamFrameAns.cbind(
          h2o.import_file(
              pyunit_utils.locate(
                  "smalldata/gam_test/multinomial_10_classes_10_cols_10000_Rows_train_C7Gam_center.csv"
              )
          )
      )
      gamFrameAns = gamFrameAns.cbind(
          h2o.import_file(
              pyunit_utils.locate(
                  "smalldata/gam_test/multinomial_10_classes_10_cols_10000_Rows_train_C8Gam_center.csv"
              )
          )
      )
      gamFrameAns.set_names(gamFrame.names)
      pyunit_utils.compare_frames_local(gamFrameAns, gamFrame)
      print("gam gamcolumn test completed successfully")
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_gam_gamColumns() without return types