code: def drf_leaf_node_assignment_mojo_test():
      problems = ["binomial", "multinomial", "regression"]
      PROBLEM = problems[randint(0, (len(problems) - 1))]
      TESTROWS = 2000
      df = pyunit_utils.random_dataset(
          PROBLEM, verbose=False, NTESTROWS=TESTROWS
      )
      train = df[TESTROWS:, :]
      test = df[:TESTROWS, :]
      x = list(set(df.names) - {"respose"})
      params = {"ntrees": 50, "max_depth": 4}
      TMPDIR = tempfile.mkdtemp()
      my_gbm = pyunit_utils.build_save_model_generic(
          params, x, train, "response", "DRF", TMPDIR
      )
      MOJONAME = pyunit_utils.getMojoName(my_gbm._id)
  
      h2o.download_csv(
          test[x], os.path.join(TMPDIR, "in.csv")
      )  # save test file, h2o predict/mojo use same file
      pred_h2o, pred_mojo = pyunit_utils.mojo_predict(
          my_gbm, TMPDIR, MOJONAME, get_leaf_node_assignment=True
      )  # load model and perform predict
      pyunit_utils.compare_string_frames_local(pred_h2o, pred_mojo, 0.5)
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from random import randint
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: drf_leaf_node_assignment_mojo_test() without return types