code: def compare_predictions_against_statsmodels(
      sktime_point_predictions,
      sktime_interval_predictions,
      statsmodels_predictions,
  ) -> None:
      """Compare predictions from ``sktime`` wrapper against ``statsmodels`` estimator.
  
      Notes
      -----
      * compare point predictions - predictive mean
      * compare confidence intervals for multiple coverage values, viz. ``COVERAGES``
      """
      statsmodels_point_predictions = (
          statsmodels_predictions.predicted_mean
      )
      assert_frame_equal(
          sktime_point_predictions, statsmodels_point_predictions
      )
  
      for coverage in COVERAGES:
          statsmodels_interval_predictions = (
              statsmodels_predictions.conf_int(alpha=(1 - coverage))
          )
  
          for target in TARGET_COLUMNS:
              sktime_results = sktime_interval_predictions.xs(
                  (target, coverage), axis="columns"
              )
  
              statsmodels_results = (
                  statsmodels_interval_predictions.filter(
                      regex=f"{target}$"
                  )
              )
              statsmodels_results.columns = ["lower", "upper"]
  
              assert_frame_equal(sktime_results, statsmodels_results)
  
imports: import pytest
  from pandas.testing import assert_frame_equal
  from sktime.datasets import load_longley
  from sktime.forecasting.dynamic_factor import DynamicFactor
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 for calling a global function with example usage: compare_predictions_against_statsmodels(sktime_point_predictions, sktime_interval_predictions, statsmodels_predictions) without return types