code: def test_output_get_feature_importances_when_regressor_is_RandomForest_with_exog():
      """
      Test output of get_feature_importances when regressor is RandomForestRegressor with lags=3
      and it is trained with series pandas DataFrame and a exogenous variable
      exog=pd.Series(np.arange(10, 20), name='exog').
      """
      forecaster = ForecasterAutoregMultiSeries(
          RandomForestRegressor(
              n_estimators=1, max_depth=2, random_state=123
          ),
          lags=3,
      )
      forecaster.fit(
          series=series, exog=pd.Series(np.arange(10, 20), name="exog")
      )
  
      results = forecaster.get_feature_importances()
      expected = pd.DataFrame(
          {
              "feature": ["lag_1", "lag_2", "lag_3", "exog", "1", "2"],
              "importance": np.array(
                  [0.73269896, 0.0, 0.21193772, 0.05536332, 0.0, 0.0]
              ),
          }
      ).sort_values(by="importance", ascending=False)
  
      pd.testing.assert_frame_equal(results, expected)
  
file_path: skforecast/skforecast/ForecasterAutoregMultiSeries/tests/test_get_feature_importances.py
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from sklearn.exceptions import NotFittedError
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import LinearRegression
  from sklearn.neural_network import MLPRegressor
  from sklearn.ensemble import RandomForestRegressor
label_desc: Use image node_id 3 for calling a global function with example usage: test_output_get_feature_importances_when_regressor_is_RandomForest_with_exog() without return types