code: def test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series_and_transform_exog():
      """
      Test predict_interval output when using LinearRegression as regressor, StandardScaler
      as transformer_series and transformer_exog as transformer_exog.
      """
      transformer_exog = ColumnTransformer(
          [
              ("scale", StandardScaler(), ["exog_1"]),
              ("onehot", OneHotEncoder(), ["exog_2"]),
          ],
          remainder="passthrough",
          verbose_feature_names_out=False,
      )
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series=StandardScaler(),
          transformer_exog=transformer_exog,
      )
      forecaster.fit(series=series, exog=exog)
      predictions = forecaster.predict_interval(
          steps=5, levels=["1", "2"], exog=exog_predict
      )
      expected = pd.DataFrame(
          data=np.array(
              [
                  [
                      0.50201669,
                      0.10891904,
                      0.90089875,
                      0.52531076,
                      0.10696675,
                      0.9156779,
                  ],
                  [
                      0.49804821,
                      0.09776535,
                      0.88356798,
                      0.51683613,
                      0.08385857,
                      0.92154261,
                  ],
                  [
                      0.59201747,
                      0.1880649,
                      0.96148336,
                      0.61509778,
                      0.18270673,
                      1.02114822,
                  ],
                  [
                      0.60179565,
                      0.216096,
                      0.9880778,
                      0.60446614,
                      0.18396533,
                      0.98997181,
                  ],
                  [
                      0.56736867,
                      0.17944505,
                      0.97036278,
                      0.56524059,
                      0.13232921,
                      0.97872883,
                  ],
              ]
          ),
          index=pd.RangeIndex(start=50, stop=55, step=1),
          columns=[
              "1",
              "1_lower_bound",
              "1_upper_bound",
              "2",
              "2_lower_bound",
              "2_upper_bound",
          ],
      )
  
      pd.testing.assert_frame_equal(predictions, expected)
  
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 12 for calling a global function with example usage: test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series_and_transform_exog() without return types