code: def test_actual_default_input_stopping_rounds():
      train = h2o.import_file(
          path=pu.locate("smalldata/extdata/australia.csv")
      )
      target = "runoffnew"
      exclude_algos = ["DeepLearning", "GLM"]
      aml = H2OAutoML(
          project_name="actual_default_input_stopping_rounds",
          exclude_algos=exclude_algos,
          max_models=10,
          seed=automl_seed,
      )
      aml.train(y=target, training_frame=train)
  
      base_models = get_partitioned_model_names(aml.leaderboard).base
      # when using cv, all cv models are trained with the stopping_rounds = 3 (default), but the final model resets stopping_rounds to 0 and use e. g. average ntrees, iterations...
      check_model_property(
          base_models, "stopping_rounds", True, 0, 0, 3
      )
  
imports: import sys, os
  import random
  import h2o
  from h2o.automl import H2OAutoML
  from tests import pyunit_utils
  from _automl_utils import get_partitioned_model_names
label_desc: Use image node_id 2 for calling a global function with example usage: test_actual_default_input_stopping_rounds() without return types