code: def test_save_and_load_forecaster_SkforecastVersionWarning():
      """
      Test warning used to notify that the skforecast version installed in the
      environment differs from the version used to create the forecaster.
      """
      forecaster = ForecasterAutoreg(
          regressor=LinearRegression(), lags=3
      )
      rng = np.random.default_rng(123)
      y = pd.Series(rng.normal(size=100))
      forecaster.fit(y=y)
      forecaster.skforecast_version = "0.0.0"
      save_forecaster(
          forecaster=forecaster,
          file_name="forecaster.joblib",
          verbose=False,
      )
  
      warn_msg = re.escape(
          (
              f"The skforecast version installed in the environment differs "
              f"from the version used to create the forecaster.\n"
              f"    Installed Version  : {skforecast.__version__}\n"
              f"    Forecaster Version : 0.0.0\n"
              f"This may create incompatibilities when using the library."
          )
      )
      with pytest.warns(SkforecastVersionWarning, match=warn_msg):
          load_forecaster(file_name="forecaster.joblib", verbose=False)
          os.remove("forecaster.joblib")
  
imports: import os
  import re
  import joblib
  import pytest
  import inspect
  import warnings
  import numpy
  import pandas
  import skforecast
  from skforecast.ForecasterAutoreg import ForecasterAutoreg
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from skforecast.utils import save_forecaster
  from skforecast.utils import load_forecaster
  from skforecast.exceptions import SkforecastVersionWarning
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 5 for calling a global function with example usage: test_save_and_load_forecaster_SkforecastVersionWarning() without return types