code: def javapredict_cars():
      # optional parameters
      params = {
          "ntrees": 5000,
          "max_depth": 10,
          "min_rows": 1,
          "learn_rate": 0.1,
          "balance_classes": random.sample([True, False], 1)[0],
      }
      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/junit/cars_nice_header.csv")
      )
      test = h2o.import_file(
          pyunit_utils.locate("smalldata/junit/cars_nice_header.csv")
      )
      x = [
          "name",
          "economy",
          "displacement",
          "power",
          "weight",
          "acceleration",
          "year",
      ]
      y = "cylinders"
  
      pyunit_utils.javapredict(
          "gbm", "numeric", train, test, x, y, **params
      )
  
imports: from builtins import zip
  import sys
  import h2o
  from tests import pyunit_utils
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: javapredict_cars() without return types