code: def test_SARIMAX_single_interval_against_statsmodels():
      """Compares Sktime's and Statsmodel's SARIMAX.
  
      Notes
      -----
      * Predict confidence intervals using underlying estimator and the wrapper.
      * Predicts for a single coverage.
      * Uses a non-default value of 97.5% to test inputs are actually being respected.
      """
      from statsmodels.tsa.api import SARIMAX as _SARIMAX
  
      df = make_forecasting_problem()
  
      sktime_model = SARIMAX(
          order=(1, 0, 0), trend="t", seasonal_order=(1, 0, 0, 6)
      )
      sktime_model.fit(df)
      sktime_pred_int = sktime_model.predict_interval(
          df.index, coverage=0.975
      )
      sktime_pred_int = sktime_pred_int.xs((0, 0.975), axis="columns")
  
      stats = _SARIMAX(
          endog=df,
          order=(1, 0, 0),
          trend="t",
          seasonal_order=(1, 0, 0, 6),
      )
      stats_fit = stats.fit()
      stats_pred_int = stats_fit.get_prediction(df.index[0]).conf_int(
          alpha=0.025
      )
      stats_pred_int.columns = ["lower", "upper"]
  
      assert_frame_equal(sktime_pred_int, stats_pred_int)
  
file_path: sktime/sktime/forecasting/tests/test_sarimax.py
imports: import pytest
  from numpy.testing import assert_allclose
  from pandas.testing import assert_frame_equal
  from sktime.forecasting.sarimax import SARIMAX
  from sktime.tests.test_switch import run_test_for_class
  from sktime.utils._testing.forecasting import make_forecasting_problem
label_desc: Use image node_id 2 for calling a global function with example usage: test_SARIMAX_single_interval_against_statsmodels() without return types