code: def test_fit_correct_dict_create_transformer_series():
      """
      Test fit method creates correctly all the auxiliary dicts transformer_series_.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10)),
              "l2": pd.Series(np.arange(10)),
              "l3": pd.Series(np.arange(10)),
          }
      )
  
      transformer_series = {
          "l1": StandardScaler(),
          "l3": StandardScaler(),
          "l4": StandardScaler(),
      }
  
      forecaster = ForecasterAutoregMultiVariate(
          regressor=LinearRegression(),
          level="l1",
          lags=3,
          steps=2,
          transformer_series=transformer_series,
      )
  
      forecaster.fit(series=series, store_in_sample_residuals=False)
      expected_transformer_series_ = {
          "l1": forecaster.transformer_series_["l1"],
          "l2": None,
          "l3": forecaster.transformer_series_["l3"],
      }
  
      assert (
          forecaster.transformer_series_ == expected_transformer_series_
      )
  
      forecaster.fit(
          series=series[["l1", "l2"]], store_in_sample_residuals=False
      )
      expected_transformer_series_ = {
          "l1": forecaster.transformer_series_["l1"],
          "l2": None,
      }
  
      assert (
          forecaster.transformer_series_ == expected_transformer_series_
      )
  
file_path: skforecast/skforecast/ForecasterAutoregMultiVariate/tests/test_fit.py
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  from sklearn.linear_model import LinearRegression
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 2 for calling a global function with example usage: test_fit_correct_dict_create_transformer_series() without return types