code: def test_naive_predict_interval_mean(strategy, sp, window_length, fh):
      """Checks naive prediction interval means are equal to mean predictions.
  
      Note: this is largely a smoke test to check the validity of tricky rolling
      mean and seasonal mean array operations within pred_var.
      """
      n_timepoints = 100000
      mu, sigma = 0.0, 10.0
      fake_idx = pd.date_range(
          "1980", periods=n_timepoints + 1, freq="H"
      )
      np.random.seed(42)
      y = pd.Series(
          np.random.normal(mu, sigma, size=n_timepoints),
          index=fake_idx[:-1],
      )
  
      forecaster = NaiveForecaster(
          strategy, sp=sp, window_length=window_length
      ).fit(y)
      y_pred_interval = forecaster.predict_interval(fh=fh)
      y_pred = forecaster.predict(fh=fh)
      pd.testing.assert_series_equal(
          y_pred_interval.mean(axis=1), y_pred
      )
  
imports: import numpy
  import pandas
  import pytest
  from sktime.datatypes._utilities import get_cutoff
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.tests._config import TEST_OOS_FHS, TEST_SPS, TEST_WINDOW_LENGTHS_INT
  from sktime.utils._testing.forecasting import _assert_correct_pred_time_index
  from sktime.utils.validation.forecasting import check_fh
label_desc: Use image node_id 11 for calling a global function with example usage: test_naive_predict_interval_mean(strategy, sp, window_length, fh) without return types