code: def test_predict_output_when_regressor_is_LinearRegression_steps_is_1_in_sample_residuals_is_True_with_fixture(
      expected_pandas_dataframe,
  ):
      """
      Test output when regressor is LinearRegression and one step ahead is predicted
      using in sample residuals. This test is equivalent to the next one.
      """
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=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
      )
      forecaster.in_sample_residuals["2"] = np.full_like(
          forecaster.in_sample_residuals["2"], fill_value=20
      )
  
      predictions = forecaster.predict_interval(
          steps=1,
          levels=expected_pandas_dataframe[0],
          in_sample_residuals=True,
      )
      expected = expected_pandas_dataframe[1]
  
      pd.testing.assert_frame_equal(predictions, expected)
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  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_ForecasterAutoregMultiSeries import series
  from .fixtures_ForecasterAutoregMultiSeries import exog
  from .fixtures_ForecasterAutoregMultiSeries import exog_predict
label_desc: Use image node_id 2 for calling a global function with example usage: test_predict_output_when_regressor_is_LinearRegression_steps_is_1_in_sample_residuals_is_True_with_fixture(expected_pandas_dataframe) without return types