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 = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series=StandardScaler(),
      )
      forecaster.fit(series=series)
      predictions = forecaster.predict_interval(steps=5, levels="1")
      expected = pd.DataFrame(
          data=np.array(
              [
                  [0.49300446, 0.1127329, 0.8957496],
                  [0.50358145, 0.06968912, 0.90112753],
                  [0.50306954, 0.06633492, 0.88968806],
                  [0.50796191, 0.13082997, 0.90510251],
                  [0.50781568, 0.11536063, 0.93464907],
              ]
          ),
          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.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 10 for calling a global function with example usage: test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series() without return types