code: def test_engines_global_methods(load_pos_and_neg_data):
      """Tests the setting of engines using methods like set_engine (global changes).
      We test for both statistical models and regression models.
      """
  
      exp = TSForecastingExperiment()
      data = load_pos_and_neg_data
  
      exp.setup(
          data=data,
          fold=2,
          fh=12,
          fold_strategy="sliding",
          verbose=False,
          engine={
              "auto_arima": "statsforecast",
              "lr_cds_dt": "sklearnex",
          },
      )
  
      # Globally reset engine ----
      # A. Statistical Models
      assert exp.get_engine("auto_arima") == "statsforecast"
      exp._set_engine("auto_arima", "pmdarima")
      assert exp.get_engine("auto_arima") == "pmdarima"
      model = exp.create_model("auto_arima", cross_validation=False)
      assert isinstance(model, PmdAutoARIMA)
  
      # B. Regression Models
      assert exp.get_engine("lr_cds_dt") == "sklearnex"
      exp._set_engine("lr_cds_dt", "sklearn")
      assert exp.get_engine("lr_cds_dt") == "sklearn"
      model = exp.create_model("lr_cds_dt", cross_validation=False)
      assert isinstance(model.regressor, SklearnLinearRegression)
  
imports: from sklearn.linear_model import LinearRegression
  from sktime.forecasting.arima import AutoARIMA
  from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
  from pycaret.time_series import TSForecastingExperiment
label_desc: Use image node_id 2 for calling a global function with example usage: test_engines_global_methods(load_pos_and_neg_data) without return types