code: def test_output_get_feature_importances_ForecasterSarimax_skforecast():
      """
      Test output of get_feature_importances ForecasterSarimax skforecast.
      """
      forecaster = ForecasterSarimax(
          regressor=Sarimax(
              order=(1, 1, 1), maxiter=1000, method="cg", disp=False
          )
      )
      forecaster.fit(y=y, exog=exog)
      results = forecaster.get_feature_importances(
          sort_importance=False
      )
  
      expected = pd.DataFrame(
          {
              "feature": ["exog", "ar.L1", "ma.L1", "sigma2"],
              "importance": np.array(
                  [
                      0.9690539855149568,
                      0.4666537980992382,
                      -0.5263430267037418,
                      0.7862622654382363,
                  ]
              ),
          }
      )
  
      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 3 for calling a global function with example usage: test_output_get_feature_importances_ForecasterSarimax_skforecast() without return types