code: def test_predict_quantiles_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_False_exog_and_transformer(
      levels,
  ):
      """
      Test output of predict_quantiles when regressor is LinearRegression,
      2 steps are predicted, using out-sample residuals, exog is included and both
      inputs are transformed. Multiple levels.
      """
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series=StandardScaler(),
          transformer_exog=transformer_exog,
      )
  
      forecaster.fit(series=series, exog=exog)
      forecaster.out_sample_residuals = forecaster.in_sample_residuals
      results = forecaster.predict_quantiles(
          steps=2,
          quantiles=[0.05, 0.55, 0.95],
          levels=levels,
          exog=exog_predict,
          n_boot=4,
          in_sample_residuals=False,
      )
  
      expected = pd.DataFrame(
          data=np.array(
              [
                  [
                      0.20133572,
                      0.40460928,
                      0.53761911,
                      0.27660015,
                      0.61593304,
                      0.7616801,
                  ],
                  [
                      0.07739771,
                      0.19454095,
                      0.45374409,
                      0.18601931,
                      0.40403914,
                      0.70461714,
                  ],
              ]
          ),
          columns=[
              "1_q_0.05",
              "1_q_0.55",
              "1_q_0.95",
              "2_q_0.05",
              "2_q_0.55",
              "2_q_0.95",
          ],
          index=pd.RangeIndex(start=50, stop=52),
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
file_path: skforecast/skforecast/ForecasterAutoregMultiSeriesCustom/tests/test_predict_quantiles.py
imports: 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 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 3 for calling a global function with example usage: test_predict_quantiles_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_False_exog_and_transformer(levels) without return types