code: def test_custom_distribution_reuse():
      from h2o.utils.distributions import CustomDistributionGaussian
  
      train = h2o.import_file(
          pyunit_utils.locate("smalldata/iris/iris_train.csv")
      )
      y = "petal_wid"
      x = train.columns
      x.remove(y)
  
      nfolds = 2
      for i in range(3):
          test_wrong_multinomial()
          custom_dist1 = h2o.upload_custom_distribution(
              CustomDistributionGaussian
          )
          gbm = H2OGradientBoostingEstimator(
              nfolds=nfolds,
              fold_assignment="Modulo",
              keep_cross_validation_predictions=True,
              distribution="custom",
              custom_distribution_func=custom_dist1,
          )
          gbm.train(x=x, y=y, training_frame=train)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.utils.distributions import CustomDistributionGaussian, CustomDistributionBernoulli, CustomDistributionMultinomial, CustomDistributionGeneric
  from tests.pyunit_utils import regression_model_distribution, regression_model_default
  from tests.pyunit_utils import multinomial_model_default, multinomial_model_distribution
  from tests.pyunit_utils import binomial_model_default, binomial_model_distribution, check_model_metrics
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 9 for calling a global function with example usage: test_custom_distribution_reuse() without return types