code: def test_output_get_feature_importances_ForecasterSarimax_pmdarima():
      """
      Test output of get_feature_importances ForecasterSarimax pmdarmia.
      """
      forecaster = ForecasterSarimax(regressor=ARIMA(order=(1, 1, 1)))
      forecaster.fit(y=pd.Series(np.arange(10)))
      results = forecaster.get_feature_importances()
  
      expected = pd.DataFrame(
          {
              "feature": ["intercept", "ar.L1", "ma.L1", "sigma2"],
              "importance": np.array(
                  [
                      0.49998574676910396,
                      0.5000130662306124,
                      7.479723906909597e-11,
                      2.658043128694438e-12,
                  ]
              ),
          }
      ).sort_values(by="importance", ascending=False)
  
      pd.testing.assert_frame_equal(expected, results)
  
file_path: skforecast/skforecast/ForecasterSarimax/tests/test_get_feature_importances.py
imports: import re
  import pytest
  import numpy
  import pandas
  from pmdarima.arima import ARIMA
  from skforecast.Sarimax import Sarimax
  from skforecast.ForecasterSarimax import ForecasterSarimax
  from sklearn.exceptions import NotFittedError
  from .fixtures_ForecasterSarimax import y
  from .fixtures_ForecasterSarimax import exog
label_desc: Use image node_id 2 for calling a global function with example usage: test_output_get_feature_importances_ForecasterSarimax_pmdarima() without return types