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 = ForecasterAutoregMultiSeries(
          regressor=LinearRegression(),
          lags=5,
          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(
              [
                  [
                      0.53267333,
                      0.17691231,
                      0.9399491,
                      0.55496412,
                      0.14714165,
                      0.9246417,
                  ],
                  [
                      0.44478046,
                      0.04641456,
                      0.83203647,
                      0.57787982,
                      0.14450512,
                      0.93373637,
                  ],
                  [
                      0.52579563,
                      0.13671047,
                      0.92765308,
                      0.66389117,
                      0.23054494,
                      1.02657915,
                  ],
                  [
                      0.57391142,
                      0.21373477,
                      0.97709097,
                      0.65789846,
                      0.23499484,
                      1.02002429,
                  ],
                  [
                      0.54633594,
                      0.1725495,
                      0.94995973,
                      0.5841187,
                      0.18811899,
                      0.9513278,
                  ],
              ]
          ),
          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.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 11 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