code: def random_networkSize(actFunc):
      no_hidden_layers = 5
      hidden = []
      hidden_dropouts = []
      for k in range(1, no_hidden_layers + 1):
          hidden.append(8)
          if "dropout" in actFunc.lower():
              hidden_dropouts.append(0.25)
  
      return hidden, hidden_dropouts
  
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 5 for calling a global function with example usage: random_networkSize(actFunc) and returns: hidden,  hidden_dropouts