code: def javapredict_2x100000():
      # optional parameters
      params = {"max_iterations": 1, "solver": "L_BFGS"}
      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/jira/2x100000_real.csv.gz")
      )
      test = train
      x = list(range(1, train.ncol))
      y = 0
  
      pyunit_utils.javapredict(
          algo="glm",
          equality="numeric",
          train=train,
          test=test,
          x=x,
          y=y,
          compile_only=True,
          **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_2x100000() without return types