code: def test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_is_False():
      """
      Test output of predict_bootstrapping when regressor is LinearRegression and
      2 steps are predicted using out-sample residuals.
      """
      forecaster = ForecasterAutoregCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
      )
      forecaster.fit(y=y, exog=exog)
      forecaster.out_sample_residuals = forecaster.in_sample_residuals
      expected = pd.DataFrame(
          data=np.array(
              [
                  [0.39132925, 0.71907802, 0.45297104, 0.52687879],
                  [-0.06983175, 0.40010008, 0.06326565, 0.05134409],
              ]
          ),
          columns=[f"pred_boot_{i}" for i in range(4)],
          index=pd.RangeIndex(start=50, stop=52),
      )
      results = forecaster.predict_bootstrapping(
          steps=2,
          n_boot=4,
          exog=exog_predict,
          in_sample_residuals=False,
      )
  
      pd.testing.assert_frame_equal(expected, results)
  
file_path: skforecast/skforecast/ForecasterAutoregCustom/tests/test_predict_bootstrapping.py
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import StandardScaler
  from .fixtures_ForecasterAutoregCustom import y
  from .fixtures_ForecasterAutoregCustom import exog
  from .fixtures_ForecasterAutoregCustom import exog_predict
label_desc: Use image node_id 7 for calling a global function with example usage: test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_is_False() without return types