code: def drf_export():
      print("###### DRF ######")
      frame = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/bigcat_5000x2.csv"
          )
      )
      frame["y"] = frame["y"].asfactor()
      model = H2ORandomForestEstimator(
          ntrees=1, max_depth=1, nbins=100, nbins_cats=10
      )
      model.train(x="X", y="y", training_frame=frame)
      h2o.download_pojo(model, path=RESULT_DIR)
      model.download_mojo(path=RESULT_DIR)
  
imports: import sys
  import random
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
  from h2o.estimators.kmeans import H2OKMeansEstimator
  from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
  from h2o.estimators.word2vec import H2OWord2vecEstimator
  from h2o.exceptions import H2OValueError
label_desc: Use image node_id 9 for calling a global function with example usage: drf_export() without return types