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).
      """
      forecaster = ForecasterAutoregMultiVariate(
          regressor=make_pipeline(
              StandardScaler(), RandomForestRegressor(random_state=123)
          ),
          level="l1",
          lags=3,
          steps=1,
          transformer_series=None,
      )
      forecaster.fit(series=series)
      results = forecaster.get_feature_importances(step=1)
      expected = pd.DataFrame(
          {
              "feature": [
                  "l1_lag_1",
                  "l1_lag_2",
                  "l1_lag_3",
                  "l2_lag_1",
                  "l2_lag_2",
                  "l2_lag_3",
              ],
              "importance": np.array(
                  [
                      0.17522374,
                      0.17408016,
                      0.17918514,
                      0.13738555,
                      0.17994996,
                      0.15417544,
                  ]
              ),
          }
      ).sort_values(by="importance", ascending=False)
  
      pd.testing.assert_frame_equal(results, expected)
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  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 10 for calling a global function with example usage: test_output_get_feature_importances_when_pipeline_RandomForestRegressor() without return types