code: def test_output_and_UserWarning_get_feature_importances_when_regressor_no_attributes():
      """
      Test output of get_feature_importances when regressor is MLPRegressor with lags=3
      and it is trained with y=pd.Series(np.arange(10)). Since MLPRegressor hasn't attributes
      `feature_importances_` or `coef_, results = None and a UserWarning is issues.
      """
      forecaster = ForecasterAutoreg(
          MLPRegressor(solver="lbfgs", max_iter=50, random_state=123),
          lags=3,
      )
      forecaster.fit(y=pd.Series(np.arange(10)))
  
      estimator = forecaster.regressor
      expected = None
  
      warn_msg = re.escape(
          (
              f"Impossible to access feature importances for regressor of type "
              f"{type(estimator)}. This method is only valid when the "
              f"regressor stores internally the feature importances in the "
              f"attribute `feature_importances_` or `coef_`."
          )
      )
      with pytest.warns(UserWarning, match=warn_msg):
          results = forecaster.get_feature_importances()
          assert results is expected
  
file_path: skforecast/skforecast/ForecasterAutoreg/tests/test_get_feature_importances.py
imports: import re
  import pytest
  from pytest import approx
  import numpy
  import pandas
  from skforecast.ForecasterAutoreg import ForecasterAutoreg
  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 6 for calling a global function with example usage: test_output_and_UserWarning_get_feature_importances_when_regressor_no_attributes() without return types