code: def test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_True_exog_and_transformer():
      """
      Test output of predict_bootstrapping when regressor is LinearRegression,
      2 steps are predicted, using in-sample residuals, exog is included and both
      inputs are transformed.
      """
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series=StandardScaler(),
          transformer_exog=transformer_exog,
      )
      forecaster.fit(series=series, exog=exog)
      results = forecaster.predict_bootstrapping(
          steps=2, n_boot=4, exog=exog_predict, in_sample_residuals=True
      )
  
      expected_1 = pd.DataFrame(
          data=np.array(
              [
                  [0.16958001, 0.55887507, 0.41716867, 0.38128471],
                  [0.08114962, 0.48871107, 0.07673561, 0.25559782],
              ]
          ),
          columns=[f"pred_boot_{i}" for i in range(4)],
          index=pd.RangeIndex(start=50, stop=52),
      )
      expected_2 = pd.DataFrame(
          data=np.array(
              [
                  [0.24448676, 0.70066373, 0.77244769, 0.45857602],
                  [0.33048491, 0.44364526, 0.75067101, 0.16052538],
              ]
          ),
          columns=[f"pred_boot_{i}" for i in range(4)],
          index=pd.RangeIndex(start=50, stop=52),
      )
  
      expected = {"1": expected_1, "2": expected_2}
  
      for key in results.keys():
          pd.testing.assert_frame_equal(results[key], expected[key])
  
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.linear_model import LinearRegression
  from .fixtures_ForecasterAutoregMultiSeriesCustom import series
  from .fixtures_ForecasterAutoregMultiSeriesCustom import exog
  from .fixtures_ForecasterAutoregMultiSeriesCustom import exog_predict
label_desc: Use image node_id 13 for calling a global function with example usage: test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_True_exog_and_transformer() without return types