code: def test_conflicting_model_id():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/jira/pubdev_6686.csv")
      )
  
      # Start bunch of models to run concurrently
      rfs = []
      for i in range(10):
          print(i)
          rfs.append(start_model(train))
  
      # Parallel built models can fail, no guarantees
      successful = []
      for m in rfs:
          try:
              m.join()
              successful.append(m)
          except:
              pass
  
      # At least one has to succeed (typically the first one submitted)
      assert len(successful) > 0
  
      # A new model to overwrite the old one has to succeed
      start_model(train).join()
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: test_conflicting_model_id() without return types