code: def deeplearning_mojo_pojo():
      problemtypes = ["regression", "binomial", "multinomial"]
      autoEncoderOn = [False]
      missingValues = ["Skip", "MeanImputation"]
      allFactors = [False, True]
      for problem in problemtypes:
          df = random_dataset(problem)  # generate random dataset
          train = df[NTESTROWS:, :]
          test = df[:NTESTROWS, :]
          x = list(set(df.names) - {"response"})
  
          for encoderOn in autoEncoderOn:
              if encoderOn:  # maxout not support for autoEncoder
                  allAct = [
                      "rectifier",
                      "tanh_with_dropout",
                      "rectifier_with_dropout",
                      "tanh",
                  ]
              else:
                  allAct = [
                      "maxout",
                      "rectifier",
                      "maxout_with_dropout",
                      "tanh_with_dropout",
                      "rectifier_with_dropout",
                      "tanh",
                  ]
  
              for actFun in allAct:
                  for missingValuesHandling in missingValues:
                      for setAllFactor in allFactors:
                          print(
                              "AutoEncoderOn is: {0} and problem type is: {1}".format(
                                  encoderOn, problem
                              )
                          )
                          print(
                              "Activation function: {0}, missing value handling: {1}, skippAllFactor: "
                              "{2}".format(
                                  actFun,
                                  missingValuesHandling,
                                  setAllFactor,
                              )
                          )
                          runComparisonTests(
                              encoderOn,
                              actFun,
                              missingValuesHandling,
                              setAllFactor,
                              train,
                              test,
                              x,
                          )
  
file_path: h2o-3/h2o-py/tests/testdir_javapredict/pyunit_pubdev_4628_pubdev_4950_deeplearning_mojo_pojo_NOPASS.py
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils
  import random
  import re
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: deeplearning_mojo_pojo() without return types