code: def test_classification_parallel():
      from pycaret.parallel import FugueBackend
  
      pc.setup(
          data_func=lambda: get_data("juice", verbose=False),
          target="Purchase",
          session_id=0,
          n_jobs=1,
          verbose=False,
          html=False,
      )
  
      test_models = pc.models().index.tolist()[:5]
  
      pc.compare_models(include=test_models, n_select=2)
      pc.compare_models(
          include=test_models, n_select=2, parallel=FugueBackend("dask")
      )
  
      fconf = {
          "fugue.rpc.server": "fugue.rpc.flask.FlaskRPCServer",  # keep this value
          "fugue.rpc.flask_server.host": "localhost",  # the driver ip address workers can access
          "fugue.rpc.flask_server.port": "3333",  # the open port on the dirver
          "fugue.rpc.flask_server.timeout": "2 sec",  # the timeout for worker to talk to driver
      }
  
      be = FugueBackend(
          "dask",
          fconf,
          display_remote=True,
          batch_size=3,
          top_only=False,
      )
      pc.compare_models(n_select=2, parallel=be)
  
      res = pc.pull()
      assert res.shape[0] > 10
  
      pc.add_metric(
          id="mydummy",
          name="DUMMY",
          score_func=_score_dummy,
          target="pred_proba",
          greater_is_better=True,
      )
  
      pc.compare_models(n_select=2, sort="DUMMY", parallel=be)
      pc.pull()
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_classification_parallel() without return types