code: def test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series():
      """
      Test predict_interval output when using LinearRegression as regressor and StandardScaler.
      """
      forecaster = ForecasterAutoregMultiSeries(
          regressor=LinearRegression(),
          lags=5,
          transformer_series=StandardScaler(),
      )
      forecaster.fit(series=series)
      predictions = forecaster.predict_interval(steps=5, levels="1")
      expected = pd.DataFrame(
          data=np.array(
              [
                  [0.52791431, 0.18396317, 0.95754082],
                  [0.44509712, 0.0128976, 0.84417134],
                  [0.42176045, 0.05538845, 0.82860157],
                  [0.48087237, 0.13819232, 0.89557796],
                  [0.48268008, 0.1309113, 0.90553765],
              ]
          ),
          index=pd.RangeIndex(start=50, stop=55, step=1),
          columns=["1", "1_lower_bound", "1_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 9 for calling a global function with example usage: test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series() without return types