code: def deeplearning_mojo_pojo():
      problem_types = ["regression", "binomial", "multinomial"]
      auto_encoder_on = [False]
      missing_values = ["Skip", "MeanImputation"]
      all_factors = [False, True]
      for problem in problem_types:
          # generate random dataset
          df = random_dataset(problem)
          train = df[NTESTROWS:, :]
          test = df[:NTESTROWS, :]
          x = list(set(df.names) - {"response"})
  
          for encoder_on in auto_encoder_on:
              # maxout not support for autoEncoder
              if encoder_on:
                  all_act = [
                      "rectifier",
                      "tanh_with_dropout",
                      "rectifier_with_dropout",
                      "tanh",
                  ]
              else:
                  all_act = [
                      "maxout",
                      "rectifier",
                      "maxout_with_dropout",
                      "tanh_with_dropout",
                      "rectifier_with_dropout",
                      "tanh",
                  ]
  
              for act_fun in all_act:
                  for missing_values_handling in missing_values:
                      for set_all_factor in all_factors:
                          print(
                              "AutoEncoderOn is: {0} and problem type is: {1}".format(
                                  encoder_on, problem
                              )
                          )
                          print(
                              "Activation function: {0}, missing value handling: {1}, skippAllFactor: "
                              "{2}".format(
                                  act_fun,
                                  missing_values_handling,
                                  set_all_factor,
                              )
                          )
                          run_comparison_tests(
                              encoder_on,
                              act_fun,
                              missing_values_handling,
                              set_all_factor,
                              train,
                              test,
                              x,
                          )
  
file_path: h2o-3/h2o-py/tests/testdir_javapredict/pyunit_pubdev_6231_deeplearning_mojo_pojo_large.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