code: def test_gam_model_predict():
      train = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/model_selection/backwardBinomial200C50KRows.csv"
          )
      )
      valid = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/model_selection/backwardBinomial200C50KRows.csv"
          )
      )
  
      x = ["C1", "C2", "C3"]
      y = "response"
      train[y] = train[y].asfactor()
      valid[y] = valid[y].asfactor()
      # build model with cross validation and with validation dataset
      gam_model_valid = H2OGeneralizedAdditiveEstimator(
          family="binomial",
          solver="IRLSM",
          gam_columns=["C4"],
          scale=[0.0001],
          num_knots=[5],
          standardize=True,
          nfolds=2,
          fold_assignment="modulo",
          alpha=[0.9, 0.5, 0.1],
          lambda_search=True,
          nlambdas=5,
          max_iterations=3,
          bs=[2],
          seed=12345,
      )
      gam_model_valid.train(
          x, y, training_frame=train, validation_frame=valid
      )
      # build model with cross validation and no validation dataset
      gam_model = H2OGeneralizedAdditiveEstimator(
          family="binomial",
          solver="IRLSM",
          gam_columns=["C4"],
          scale=[0.0001],
          num_knots=[5],
          standardize=True,
          nfolds=2,
          fold_assignment="modulo",
          alpha=[0.9, 0.5, 0.1],
          lambda_search=True,
          nlambdas=5,
          max_iterations=3,
          bs=[2],
          seed=12345,
      )
      gam_model.train(x, y, training_frame=train)
      # model should yield the same coefficients in both case
      gam_model_coef = gam_model.coef()
      gam_model_valid_coef = gam_model_valid.coef()
      pyunit_utils.assertEqualCoeffDicts(
          gam_model_coef, gam_model_valid_coef
      )
  
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