code: def test_predict_quantiles_output_when_forecaster_is_LinearRegression_steps_is_2_in_sample_residuals_False_exog_and_transformer():
      """
      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.
      """
      forecaster = ForecasterAutoregCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_y=StandardScaler(),
          transformer_exog=StandardScaler(),
      )
  
      forecaster.fit(y=y, exog=exog)
      forecaster.out_sample_residuals = forecaster.in_sample_residuals
      results = forecaster.predict_quantiles(
          steps=2,
          exog=exog_predict,
          quantiles=[0.05, 0.55, 0.95],
          n_boot=4,
          in_sample_residuals=False,
      )
  
      expected = pd.DataFrame(
          data=np.array(
              [
                  [0.40057552, 0.50101108, 0.69024814],
                  [-0.05165537, 0.05909311, 0.34957492],
              ]
          ),
          columns=["q_0.05", "q_0.55", "q_0.95"],
          index=pd.RangeIndex(start=50, stop=52),
      )
  
      pd.testing.assert_frame_equal(expected, results)
  
file_path: skforecast/skforecast/ForecasterAutoregCustom/tests/test_predict_quantiles.py
imports: import numpy
  import pandas
  from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import StandardScaler
  from .fixtures_ForecasterAutoregCustom import y
  from .fixtures_ForecasterAutoregCustom import exog
  from .fixtures_ForecasterAutoregCustom 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() without return types