code: def test_gam_model_predict():
      covtype_df = h2o.import_file(
          pyunit_utils.locate("bigdata/laptop/covtype/covtype.full.csv")
      )
      train, valid = covtype_df.split_frame([0.9], seed=1234)
  
      # Prepare predictors and response columns
      covtype_X = covtype_df.col_names[
          :-1
      ]  # last column is Cover_Type, our desired response variable
      covtype_y = covtype_df.col_names[-1]
      # build model with cross validation and with validation dataset
      gam_multi_valid = H2OGeneralizedAdditiveEstimator(
          family="multinomial",
          solver="IRLSM",
          bs=[0, 0, 0],
          gam_columns=["Elevation", "Aspect", "Slope"],
          standardize=True,
          nfolds=2,
          fold_assignment="modulo",
          alpha=[0.9, 0.5, 0.1],
          lambda_search=True,
          nlambdas=5,
          max_iterations=3,
          seed=1234,
      )
      gam_multi_valid.train(
          covtype_X,
          covtype_y,
          training_frame=train,
          validation_frame=valid,
      )
      # build model with cross validation and no validation dataset
      gam_multi = H2OGeneralizedAdditiveEstimator(
          family="multinomial",
          solver="IRLSM",
          bs=[0, 0, 0],
          gam_columns=["Elevation", "Aspect", "Slope"],
          standardize=True,
          nfolds=2,
          fold_assignment="modulo",
          alpha=[0.9, 0.5, 0.1],
          lambda_search=True,
          nlambdas=5,
          max_iterations=3,
          seed=1234,
      )
      gam_multi.train(covtype_X, covtype_y, training_frame=train)
      # model should yield the same coefficients in both case
      gam_multi_coef = gam_multi.coef()
      gam_multi_valid_coef = gam_multi_valid.coef()
      pyunit_utils.assertEqualCoeffDicts(
          gam_multi_coef["coefficients"],
          gam_multi_valid_coef["coefficients"],
      )
  
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_model_predict() without return types