code: def deeplearning_export():
      print("###### DEEPLEARNING ######")
      frame = h2o.import_file(
          path=pyunit_utils.locate("smalldata/junit/cars_20mpg.csv")
      )
      problem = random.sample(list(range(3)), 1)[0]
      predictors = [
          "displacement",
          "power",
          "weight",
          "acceleration",
          "year",
      ]
      if problem == 1:
          response_col = "economy_20mpg"
          frame[response_col] = frame[response_col].asfactor()
      elif problem == 2:
          response_col = "cylinders"
          frame[response_col] = frame[response_col].asfactor()
      else:
          response_col = "economy"
      print("Response column: {0}".format(response_col))
      model = H2ODeepLearningEstimator(
          nfolds=random.randint(3, 10),
          fold_assignment="Modulo",
          hidden=[20, 20],
          epochs=10,
      )
      model.train(x=predictors, y=response_col, 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 2 for calling a global function with example usage: deeplearning_export() without return types