code: def test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series_as_dict():
      """
      Test predict_interval output when using LinearRegression as regressor and transformer_series
      is a dict with 2 different transformers.
      """
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=3,
          transformer_series={
              "1": StandardScaler(),
              "2": MinMaxScaler(),
          },
      )
      forecaster.fit(series=series)
      predictions = forecaster.predict_interval(steps=5, levels=["1"])
      expected = pd.DataFrame(
          data=np.array(
              [
                  [0.5567357, 0.15168829, 0.93889146],
                  [0.50694567, 0.08199289, 0.90753719],
                  [0.51127353, 0.08587232, 0.90581568],
                  [0.51327383, 0.14090782, 0.91811281],
                  [0.50978027, 0.10608631, 0.93405271],
              ]
          ),
          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 11 for calling a global function with example usage: test_predict_interval_output_when_regressor_is_LinearRegression_with_transform_series_as_dict() without return types