code: def test_output_get_feature_importances_when_regressor_is_LinearRegression():
      """
      Test output of get_feature_importances when regressor is LinearRegression with lags=3
      and it is trained with series pandas DataFrame.
      """
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=3
      )
      forecaster.fit(series=series)
  
      results = forecaster.get_feature_importances(
          sort_importance=False
      )
      expected = pd.DataFrame(
          {
              "feature": ["lag_1", "lag_2", "lag_3", "1", "2"],
              "importance": np.array(
                  [
                      3.33333333e-01,
                      3.33333333e-01,
                      3.33333333e-01,
                      -1.48164367e-16,
                      1.48164367e-16,
                  ]
              ),
          }
      )
  
      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 4 for calling a global function with example usage: test_output_get_feature_importances_when_regressor_is_LinearRegression() without return types