code: def test_auto_rebalance_parameter_is_set():
      fr = h2o.import_file(
          path=pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      target = "CAPSULE"
      fr[target] = fr[target].asfactor()
  
      gbm = H2OGradientBoostingEstimator(seed=42, auto_rebalance=False)
      gbm.train(y=target, training_frame=fr)
      assert not gbm.actual_params[
          "auto_rebalance"
      ], "Parameter is not set"
      assert not gbm.params["auto_rebalance"][
          "input"
      ], "Parameter is not set"
  
      gbm = H2OGradientBoostingEstimator(seed=42)
      gbm.train(y=target, training_frame=fr)
      assert gbm.actual_params[
          "auto_rebalance"
      ], "Parameter is not set - default is True"
      assert gbm.params["auto_rebalance"][
          "input"
      ], "Parameter is not set - default is True"
  
      try:
          H2ORandomForestEstimator(seed=42, auto_rebalance=False)
          assert False, "Should fail"
      except TypeError as e:
          assert "auto_rebalance" in str(
              e
          ), "Test should fail and complain about auto_rebalance parameter"
  
imports: import os
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OGradientBoostingEstimator, H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_auto_rebalance_parameter_is_set() without return types