code: def test_predict_output_when_regressor_is_LinearRegression_with_transform_series():
      """
      Test predict output when using LinearRegression as regressor and StandardScaler.
      """
      forecaster = ForecasterAutoregMultiVariate(
          regressor=LinearRegression(),
          level="l1",
          lags=5,
          steps=5,
          transformer_series=StandardScaler(),
      )
      forecaster.fit(series=series)
      results = forecaster.predict()
      expected = pd.DataFrame(
          data=np.array(
              [
                  0.60056539,
                  0.42924504,
                  0.34173573,
                  0.44231236,
                  0.40133213,
              ]
          ),
          index=pd.RangeIndex(start=50, stop=55, step=1),
          columns=["l1"],
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.exceptions import NotFittedError
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.preprocessing import FunctionTransformer
  from sklearn.compose import make_column_transformer
  from sklearn.compose import make_column_selector
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LinearRegression
  from sklearn.ensemble import HistGradientBoostingRegressor
  from lightgbm import LGBMRegressor
  from .fixtures_ForecasterAutoregMultiVariate import series
  from .fixtures_ForecasterAutoregMultiVariate import exog
  from .fixtures_ForecasterAutoregMultiVariate import exog_predict
label_desc: Use image node_id 8 for calling a global function with example usage: test_predict_output_when_regressor_is_LinearRegression_with_transform_series() without return types