code: def test_predict_output_when_regressor_is_LinearRegression_steps_is_1_in_sample_residuals_is_False_with_fixture(
      expected_pandas_dataframe,
  ):
      """
      Test output when regressor is LinearRegression and one step ahead is predicted
      using out sample residuals.
      """
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=3, transformer_series=None
      )
      forecaster.fit(series=series_2, store_in_sample_residuals=True)
  
      residuals = {
          "1": np.full_like(
              forecaster.in_sample_residuals["1"], fill_value=10
          ),
          "2": np.full_like(
              forecaster.in_sample_residuals["2"], fill_value=20
          ),
      }
      forecaster.set_out_sample_residuals(residuals=residuals)
  
      predictions = forecaster.predict_interval(
          steps=1,
          levels=expected_pandas_dataframe[0],
          in_sample_residuals=False,
      )
      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 7 for calling a global function with example usage: test_predict_output_when_regressor_is_LinearRegression_steps_is_1_in_sample_residuals_is_False_with_fixture(expected_pandas_dataframe) without return types