code: def parametersKmeans():
      print("Getting data...")
      iris = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris.csv")
      )
  
      print("Create and and duplicate...")
  
      iris_km = H2OKMeansEstimator(k=3, seed=1234)
      iris_km.train(x=list(range(4)), training_frame=iris)
      parameters = iris_km._model_json["parameters"]
      param_dict = {pp["name"]: pp["actual_value"] for pp in parameters}
      fold_column = param_dict.pop("fold_column")
      del param_dict["model_id"]
      del param_dict["training_frame"]
      del param_dict["validation_frame"]
      del param_dict["max_runtime_secs"]
      iris_km_again = H2OKMeansEstimator(
          **param_dict
      )  # not all parameters go here - invalid test
      # remove assigning the x parameter to prevent H2OValueError: Properties x and ignored_columns cannot be specified simultaneously
      iris_km_again.train(training_frame=iris, fold_column=fold_column)
  
      print("wss")
      wss = iris_km.withinss().sort()
      wss_again = iris_km_again.withinss().sort()
      assert wss == wss_again, "expected wss to be equal"
  
      print("centers")
      centers = iris_km.centers()
      centers_again = iris_km_again.centers()
      assert centers == centers_again, "expected centers to be the same"
  
label_desc: Use image node_id 1 for calling a global function with example usage: parametersKmeans() without return types