code: def set_params(
      actFun, missingValuesHandling, setAllFactor, enableEncoder=False
  ):
      dropOutRatio = 0.25
      hiddens, hidden_dropout_ratios = random_networkSize(
          actFun
      )  # generate random size layers
      seed = 12345
      if ("dropout") in actFun:
          params = {
              "hidden": hiddens,
              "standardize": True,
              "missing_values_handling": missingValuesHandling,
              "activation": actFun,
              "use_all_factor_levels": setAllFactor,
              "hidden_dropout_ratios": hidden_dropout_ratios,
              "input_dropout_ratio": dropOutRatio,
              "autoencoder": enableEncoder,
              "seed": seed,
              "reproducible": True,
          }
      else:
          params = {
              "hidden": hiddens,
              "standardize": True,
              "missing_values_handling": missingValuesHandling,
              "activation": actFun,
              "use_all_factor_levels": setAllFactor,
              "input_dropout_ratio": dropOutRatio,
              "autoencoder": enableEncoder,
              "seed": seed,
              "reproducible": True,
          }
      print(params)
      return params
  
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 3 for calling a global function with example usage: set_params(actFun, missingValuesHandling, setAllFactor, enableEncoder) and returns: params