code: def test_output_get_feature_importances_when_regressor_is_LinearRegression_lags_3_step_1():
      """
      Test output of get_feature_importances for step 1, when regressor is LinearRegression with lags=3
      and it is trained with y=pd.Series(np.arange(5)).
      """
      forecaster = ForecasterAutoregDirect(
          LinearRegression(), lags=3, steps=1
      )
      forecaster.fit(y=pd.Series(np.arange(5)))
  
      results = forecaster.get_feature_importances(
          step=1, sort_importance=False
      )
      expected = pd.DataFrame(
          {
              "feature": ["lag_1", "lag_2", "lag_3"],
              "importance": np.array(
                  [0.33333333, 0.33333333, 0.33333333]
              ),
          }
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
imports: import re
  import pytest
  from pytest import approx
  import numpy
  import pandas
  from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect
  from sklearn.exceptions import NotFittedError
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.neural_network import MLPRegressor
  from sklearn.ensemble import RandomForestRegressor
label_desc: Use image node_id 6 for calling a global function with example usage: test_output_get_feature_importances_when_regressor_is_LinearRegression_lags_3_step_1() without return types