code: def javapredict_iris_drf():
      # optional parameters
      params = {"ntrees": 100, "max_depth": 5, "min_rows": 10}
      print("Parameter list:")
      for k, v in zip(list(params.keys()), list(params.values())):
          print("{0}, {1}".format(k, v))
  
      train = h2o.import_file(
          pyunit_utils.locate("smalldata/iris/iris_train.csv")
      )
      test = h2o.import_file(
          pyunit_utils.locate("smalldata/iris/iris_train.csv")
      )
      x = ["sepal_len", "sepal_wid", "petal_len", "petal_wid"]
      y = "species"
  
      pyunit_utils.javapredict(
          "random_forest", "class", 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_iris_drf() without return types