code: def test_output_get_feature_importances_when_pipeline_RandomForestRegressor():
      """
      Test output of get_feature_importances when regressor is pipeline,
      (StandardScaler() + RandomForestRegressor with lags=3),
      it is trained with series pandas DataFrame.
      """
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=make_pipeline(
              StandardScaler(),
              RandomForestRegressor(
                  n_estimators=1, max_depth=2, random_state=123
              ),
          ),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series=None,
      )
      forecaster.fit(series=series)
  
      results = forecaster.get_feature_importances(
          sort_importance=False
      )
      expected = pd.DataFrame(
          {
              "feature": [
                  "custom_predictor_0",
                  "custom_predictor_1",
                  "custom_predictor_2",
                  "1",
                  "2",
              ],
              "importance": np.array(
                  [
                      0.9446366782006932,
                      0.0,
                      0.05536332179930687,
                      0.0,
                      0.0,
                  ]
              ),
          }
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
imports: import re
  import pytest
  from pytest import approx
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  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 9 for calling a global function with example usage: test_output_get_feature_importances_when_pipeline_RandomForestRegressor() without return types