code: def adaBoost_save_and_load():
      print("AdaBoost Save Load Test")
  
      train = h2o.import_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      train["CAPSULE"] = train["CAPSULE"].asfactor()
  
      adaboost_model = H2OAdaBoostEstimator(nlearners=7, seed=12)
      adaboost_model.train(training_frame=train, y="CAPSULE")
      predict = adaboost_model.predict(train)
  
      path = pyunit_utils.locate("results")
  
      assert os.path.isdir(
          path
      ), "Expected save directory {0} to exist, but it does not.".format(
          path
      )
      model_path = h2o.save_model(adaboost_model, path=path, force=True)
  
      assert os.path.isfile(
          model_path
      ), "Expected load file {0} to exist, but it does not.".format(
          model_path
      )
      reloaded = h2o.load_model(model_path)
      predict_reloaded = reloaded.predict(train)
  
      assert isinstance(
          reloaded, H2OAdaBoostEstimator
      ), "Expected and H2OAdaBoostEstimator, but got {0}".format(
          reloaded
      )
  
      assert pyunit_utils.compare_frames_local(
          predict, predict_reloaded, returnResult=True
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OAdaBoostEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: adaBoost_save_and_load() without return types