code: def test_gam_effective_parameters():
      h2o_data = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/binomial_20_cols_10KRows.csv"
          )
      )
      h2o_data["C1"] = h2o_data["C1"].asfactor()
      h2o_data["C2"] = h2o_data["C2"].asfactor()
      h2o_data["C21"] = h2o_data["C21"].asfactor()
  
      gam = H2OGeneralizedAdditiveEstimator(
          family="binomial",
          gam_columns=["C11", "C12", "C13"],
          scale=[1, 1, 1],
          num_knots=[5, 6, 7],
          standardize=True,
          Lambda=[0],
          alpha=[0],
          max_iterations=3,
      )
      gam.train(x=["C1", "C2"], y="C21", training_frame=h2o_data)
  
      assert gam.parms["solver"]["input_value"] == "AUTO"
      assert gam.parms["solver"]["actual_value"] == "IRLSM"
      assert gam.parms["fold_assignment"]["input_value"] == "AUTO"
      assert gam.parms["fold_assignment"]["actual_value"] is None
  
      try:
          h2o.rapids(
              '(setproperty "{}" "{}")'.format(
                  "sys.ai.h2o.algos.evaluate_auto_model_parameters",
                  "false",
              )
          )
          gam = H2OGeneralizedAdditiveEstimator(
              family="binomial",
              gam_columns=["C11", "C12", "C13"],
              scale=[1, 1, 1],
              num_knots=[5, 6, 7],
              standardize=True,
              Lambda=[0],
              alpha=[0],
              max_iterations=3,
          )
          gam.train(x=["C1", "C2"], y="C21", training_frame=h2o_data)
  
          assert gam.parms["solver"]["input_value"] == "AUTO"
          assert gam.parms["solver"]["actual_value"] == "AUTO"
          assert gam.parms["fold_assignment"]["input_value"] == "AUTO"
          assert gam.parms["fold_assignment"]["actual_value"] == "AUTO"
      finally:
          h2o.rapids(
              '(setproperty "{}" "{}")'.format(
                  "sys.ai.h2o.algos.evaluate_auto_model_parameters",
                  "true",
              )
          )
  
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_effective_parameters() without return types