code: def test_create_train_X_y_UserWarning_when_levels_of_transformer_series_not_equal_to_series_col_names():
      """
      Test UserWarning is raised when `transformer_series` is a dict and its keys are
      not the same as forecaster.series_col_names.
      """
      series = pd.DataFrame(
          {"1": pd.Series(np.arange(5)), "2": pd.Series(np.arange(5))}
      )
      dict_transformers = {"1": StandardScaler(), "3": StandardScaler()}
  
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors_3,
          window_size=3,
          transformer_series=dict_transformers,
      )
      series_not_in_transformer_series = set(series.columns) - set(
          forecaster.transformer_series.keys()
      )
  
      warn_msg = re.escape(
          (
              f"{series_not_in_transformer_series} not present in `transformer_series`."
              f" No transformation is applied to these series."
          )
      )
      with pytest.warns(UserWarning, match=warn_msg):
          forecaster.create_train_X_y(series=series)
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from skforecast.exceptions import MissingValuesExogWarning
  from sklearn.linear_model import LinearRegression
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
label_desc: Use image node_id 8 for calling a global function with example usage: test_create_train_X_y_UserWarning_when_levels_of_transformer_series_not_equal_to_series_col_names() without return types