code: def test_recursive_predict_output_when_regressor_is_LinearRegression():
      """
      Test _recursive_predict output when using LinearRegression as regressor.
      """
      series = pd.DataFrame(
          {
              "1": pd.Series(np.arange(start=0, stop=50)),
              "2": pd.Series(np.arange(start=50, stop=100)),
          }
      )
  
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=5, transformer_series=None
      )
      forecaster.fit(series=series)
      level = "1"
      predictions_1 = forecaster._recursive_predict(
          steps=5,
          level=level,
          last_window=forecaster.last_window[level].to_numpy(),
          exog=None,
      )
      expected_1 = np.array([50.0, 51.0, 52.0, 53.0, 54.0])
  
      level = "2"
      predictions_2 = forecaster._recursive_predict(
          steps=5,
          level=level,
          last_window=forecaster.last_window[level].to_numpy(),
          exog=None,
      )
      expected_2 = np.array([100.0, 101.0, 102.0, 103.0, 104.0])
  
      assert predictions_1 == approx(expected_1)
      assert predictions_2 == approx(expected_2)
  
file_path: skforecast/skforecast/ForecasterAutoregMultiSeries/tests/test_recursive_predict.py
imports: from pytest import approx
  import numpy
  import pandas
  from sklearn.linear_model import Ridge
  from sklearn.linear_model import LinearRegression
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
label_desc: Use image node_id 1 for calling a global function with example usage: test_recursive_predict_output_when_regressor_is_LinearRegression() without return types