code: def test_output_get_feature_importances_when_regressor_is_LinearRegression_with_exog():
      """
      Test output of get_feature_importances when regressor is LinearRegression with lags=3
      and it is trained with y=pd.Series(np.arange(5)) and a exogenous variable
      exog=pd.Series(np.arange(10, 15), name='exog').
      """
      forecaster = ForecasterAutoreg(LinearRegression(), lags=3)
      forecaster.fit(
          y=pd.Series(np.arange(5)),
          exog=pd.Series(np.arange(10, 15), name="exog"),
      )
  
      results = forecaster.get_feature_importances(
          sort_importance=False
      )
      expected = pd.DataFrame(
          {
              "feature": ["lag_1", "lag_2", "lag_3", "exog"],
              "importance": np.array([0.25, 0.25, 0.25, 0.25]),
          }
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
file_path: skforecast/skforecast/ForecasterAutoreg/tests/test_get_feature_importances.py
imports: import re
  import pytest
  from pytest import approx
  import numpy
  import pandas
  from skforecast.ForecasterAutoreg import ForecasterAutoreg
  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 5 for calling a global function with example usage: test_output_get_feature_importances_when_regressor_is_LinearRegression_with_exog() without return types