code: def test_predict_interval_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_is_True():
      """
      Test output when regressor is LinearRegression and two step ahead is predicted
      using in sample residuals.
      """
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series=None,
      )
      forecaster.fit(series=series_2, store_in_sample_residuals=True)
  
      forecaster.in_sample_residuals["1"] = np.full_like(
          forecaster.in_sample_residuals["1"], fill_value=10
      )
      expected_1 = pd.DataFrame(
          np.array(
              [[10.0, 20.0, 20.0], [11.0, 24.33333333, 24.33333333]]
          ),
          columns=["1", "1_lower_bound", "1_upper_bound"],
          index=pd.RangeIndex(start=10, stop=12, step=1),
      )
      results_1 = forecaster.predict_interval(
          steps=2, levels="1", in_sample_residuals=True
      )
  
      forecaster.in_sample_residuals["2"] = np.full_like(
          forecaster.in_sample_residuals["2"], fill_value=20
      )
      expected_2 = pd.DataFrame(
          np.array(
              [
                  [10.0, 30.0, 30.0],
                  [11.0, 37.66666666666667, 37.66666666666667],
              ]
          ),
          columns=["2", "2_lower_bound", "2_upper_bound"],
          index=pd.RangeIndex(start=10, stop=12, step=1),
      )
      results_2 = forecaster.predict_interval(
          steps=2, levels="2", in_sample_residuals=True
      )
  
      expected_3 = pd.DataFrame(
          data=np.array(
              [
                  [10.0, 20.0, 20.0, 10.0, 30.0, 30.0],
                  [
                      11.0,
                      24.33333333,
                      24.33333333,
                      11.0,
                      37.66666666666667,
                      37.66666666666667,
                  ],
              ]
          ),
          columns=[
              "1",
              "1_lower_bound",
              "1_upper_bound",
              "2",
              "2_lower_bound",
              "2_upper_bound",
          ],
          index=pd.RangeIndex(start=10, stop=12, step=1),
      )
      results_3 = forecaster.predict_interval(
          steps=2, levels=["1", "2"], in_sample_residuals=True
      )
  
      pd.testing.assert_frame_equal(results_1, expected_1)
      pd.testing.assert_frame_equal(results_2, expected_2)
      pd.testing.assert_frame_equal(results_3, expected_3)
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import MinMaxScaler
  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 7 for calling a global function with example usage: test_predict_interval_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_is_True() without return types