code: def test_output_get_feature_importances_when_regressor_is_LinearRegression():
      """
      Test output of get_feature_importances when regressor is LinearRegression with
      create_predictors and it is trained with y=pd.Series(np.arange(7)).
      """
      forecaster = ForecasterAutoregCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=5,
      )
      forecaster.fit(y=pd.Series(np.arange(7)))
  
      results = forecaster.get_feature_importances(
          sort_importance=False
      )
      expected = pd.DataFrame(
          {
              "feature": [
                  "custom_predictor_0",
                  "custom_predictor_1",
                  "custom_predictor_2",
                  "custom_predictor_3",
                  "custom_predictor_4",
              ],
              "importance": np.array([0.2, 0.2, 0.2, 0.2, 0.2]),
          }
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
imports: import re
  import pytest
  from pytest import approx
  import numpy
  import pandas
  from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
  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() without return types