code: def test_predict_bootstrapping_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_True_exog_and_transformer_fixed_to_zero():
      """
      Test output of predict_bootstrapping when regressor is LinearRegression,
      2 steps are predicted, using in-sample residuals, exog is included, both
      inputs are transformed and in-sample residuals are fixed to 0.
      """
      forecaster = ForecasterAutoregMultiSeries(
          regressor=LinearRegression(),
          lags=3,
          transformer_series=StandardScaler(),
          transformer_exog=transformer_exog,
      )
      forecaster.fit(series=series, exog=exog)
  
      forecaster.in_sample_residuals["1"] = np.full_like(
          forecaster.in_sample_residuals["1"], fill_value=0
      )
      forecaster.in_sample_residuals["2"] = np.full_like(
          forecaster.in_sample_residuals["2"], fill_value=0
      )
      results = forecaster.predict_bootstrapping(
          steps=2, n_boot=4, exog=exog_predict, in_sample_residuals=True
      )
  
      expected_1 = pd.DataFrame(
          data=np.array(
              [
                  [0.50201669, 0.50201669, 0.50201669, 0.50201669],
                  [0.49804821, 0.49804821, 0.49804821, 0.49804821],
              ]
          ),
          columns=[f"pred_boot_{i}" for i in range(4)],
          index=pd.RangeIndex(start=50, stop=52),
      )
      expected_2 = pd.DataFrame(
          data=np.array(
              [
                  [0.52531076, 0.52531076, 0.52531076, 0.52531076],
                  [0.51683613, 0.51683613, 0.51683613, 0.51683613],
              ]
          ),
          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.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.linear_model import LinearRegression
  from .fixtures_ForecasterAutoregMultiSeries import series
  from .fixtures_ForecasterAutoregMultiSeries import exog
  from .fixtures_ForecasterAutoregMultiSeries import exog_predict
label_desc: Use image node_id 11 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_fixed_to_zero() without return types