code: def javapredict_smallcat():
      # optional parameters
      params = {"epochs": 100}
      print("Parameter list:")
      for k, v in zip(list(params.keys()), list(params.values())):
          print("{0}, {1}".format(k, v))
  
      train = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv")
      )
      test = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/virginica.csv")
      )
      x = [0, 1, 2, 4]
      y = 3
  
      pyunit_utils.javapredict(
          "deeplearning", "numeric", train, test, x, y, **params
      )
  
imports: from builtins import zip
  import sys
  import h2o
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: javapredict_smallcat() without return types