code: def javapredict_gbm_hexdev_692():
      train = h2o.upload_file(
          pyunit_utils.locate(
              "smalldata/logreg/prostate_train_null_column_name.csv"
          )
      )
      test = h2o.upload_file(
          pyunit_utils.locate(
              "smalldata/logreg/prostate_train_null_column_name.csv"
          )
      )
      params = {
          "ntrees": 100,
          "max_depth": 5,
          "seed": 42,
          "training_frame": train,
          "learn_rate": 0.1,
          "min_rows": 10,
          "distribution": "bernoulli",
      }  # 651MB pojo
      train["CAPSULE"] = train["CAPSULE"].asfactor()
      test["CAPSULE"] = test["CAPSULE"].asfactor()
      print("Parameter list:")
      for k, v in zip(list(params.keys()), list(params.values())):
          print("{0}, {1}".format(k, v))
  
      x = list(range(0, train.ncol))
      y = "CAPSULE"
  
      pyunit_utils.javapredict(
          "gbm", "class", 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_gbm_hexdev_692() without return types