code: def test_gam_modelMetrics():
      print("Checking modelmetrics for binomial")
      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()
      myY = "C21"
      h2o_data["C21"] = h2o_data["C21"].asfactor()
  
      bs = [1, 2, 3]
      buildModelMetricsCheck(
          h2o_data, myY, ["C11", "C12", "C13"], "binomial", bs
      )
  
      print("Checking modelmetrics for gaussian")
      h2o_data = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/glm_test/gaussian_20cols_10000Rows.csv"
          )
      )
      h2o_data["C1"] = h2o_data["C1"].asfactor()
      h2o_data["C2"] = h2o_data["C2"].asfactor()
      myY = "C21"
      bs = [0, 3, 1]
      buildModelMetricsCheck(
          h2o_data, myY, ["C11", "C12", "C13"], "gaussian", bs
      )
  
      print("Checking modelmetrics for multinomial")
      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()
      myY = "C11"
      h2o_data["C11"] = h2o_data["C11"].asfactor()
      bs = [3, 1, 0]
      buildModelMetricsCheck(
          h2o_data, myY, ["C6", "C7", "C8"], "multinomial", bs
      )
  
      print("gam modelmetrics 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_modelMetrics() without return types