code: def test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_y_and_transform_exog():
      """
      Test predict output when using LinearRegression as regressor, StandardScaler
      as transformer_y and transformer_exog as transformer_exog.
      """
      y = pd.Series(
          np.array([-0.59, 0.02, -0.9, 1.09, -3.61, 0.72, -0.11, -0.4])
      )
      exog = pd.DataFrame(
          {
              "col_1": [7.5, 24.4, 60.3, 57.3, 50.7, 41.4, 87.2, 47.4],
              "col_2": ["a", "a", "a", "a", "b", "b", "b", "b"],
          }
      )
      exog_predict = exog.copy()
      exog_predict.index = pd.RangeIndex(start=8, stop=16)
  
      transformer_y = StandardScaler()
      transformer_exog = ColumnTransformer(
          [
              ("scale", StandardScaler(), ["col_1"]),
              ("onehot", OneHotEncoder(), ["col_2"]),
          ],
          remainder="passthrough",
          verbose_feature_names_out=False,
      )
      forecaster = ForecasterAutoreg(
          regressor=LinearRegression(),
          lags=5,
          transformer_y=transformer_y,
          transformer_exog=transformer_exog,
      )
  
      forecaster.fit(y=y, exog=exog)
      predictions = forecaster.predict_interval(
          steps=5, exog=exog_predict
      )
      expected = pd.DataFrame(
          data=np.array(
              [
                  [0.50619336, 0.50619336, 0.50619336],
                  [-0.09630298, -0.09630298, -0.09630298],
                  [0.05254973, 0.05254973, 0.05254973],
                  [0.12281153, 0.12281153, 0.12281153],
                  [0.00221741, 0.00221741, 0.00221741],
              ]
          ),
          index=pd.RangeIndex(start=8, stop=13, step=1),
          columns=["pred", "lower_bound", "upper_bound"],
      )
  
      pd.testing.assert_frame_equal(predictions, expected)
  
file_path: skforecast/skforecast/ForecasterAutoreg/tests/test_predict_interval.py
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoreg import ForecasterAutoreg
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 6 for calling a global function with example usage: test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_y_and_transform_exog() without return types