code: def javapredict_dl_xlarge():
      # Should produce ~400MB POJO
      params = {"hidden": [3500, 3500], "epochs": 0.0001}
      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("bigdata/laptop/higgs_head_2M.csv")
      )
      test = train[list(range(0, 10)), :]
      x = list(range(1, train.ncol))
      y = 0
  
      pyunit_utils.javapredict(
          "deeplearning", "numeric", train, test, x, y, **params
      )
  
imports: from builtins import zip
  from builtins import range
  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_dl_xlarge() without return types