code: def test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_fixed():
      """
      Test output of predict_bootstrapping when regressor is LinearRegression,
      2 steps are predicted, using in-sample residuals that are fixed.
      """
      forecaster = ForecasterAutoregDirect(
          regressor=LinearRegression(), steps=2, lags=3
      )
      forecaster.fit(y=y, exog=exog)
      forecaster.in_sample_residuals = {
          1: pd.Series([1, 1, 1, 1, 1, 1, 1]),
          2: pd.Series([5, 5, 5, 5, 5, 5, 5]),
      }
      results = forecaster.predict_bootstrapping(
          steps=2, exog=exog_predict, n_boot=4, in_sample_residuals=True
      )
      expected = pd.DataFrame(
          data=np.array(
              [
                  [1.67523588, 1.67523588, 1.67523588, 1.67523588],
                  [5.38024988, 5.38024988, 5.38024988, 5.38024988],
              ]
          ),
          columns=[f"pred_boot_{i}" for i in range(4)],
          index=pd.RangeIndex(start=50, stop=52),
      )
  
      pd.testing.assert_frame_equal(expected, results)
  
file_path: skforecast/skforecast/ForecasterAutoregDirect/tests/test_predict_bootstrapping.py
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import StandardScaler
  from .fixtures_ForecasterAutoregDirect import y
  from .fixtures_ForecasterAutoregDirect import exog
  from .fixtures_ForecasterAutoregDirect import exog_predict
label_desc: Use image node_id 9 for calling a global function with example usage: test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_fixed() without return types