code: def test_strategy_mean_and_last_seasonal_additional_combinations(
      n, window_length, sp, strategy
  ):
      """Check that naive forecasters yield the right forecasts given simple data.
  
      Test for perfectly cyclic data, and for robustness against a missing value. More
      specifically, check time series of n * window_length with a 1:n-1 train/test split,
      for different combinations of the period and seasonal periodicity. The time series
      contains perfectly cyclic data, so switching between the "mean" and "last"
      strategies should not make a difference.
      """
      # given <window_length> hours of data with a seasonal periodicity of <sp> hours
      freq = pd.Timedelta("1H")
      kwargs = (
          dict(closed="left")
          if pd.__version__ < "1.4.0"
          else dict(inclusive="left")
      )
      data = pd.Series(
          index=pd.date_range(
              "2021-06-01 00:00",
              periods=n * window_length,
              freq=freq,
              **kwargs,
          ),
          data=(
              [float(i) for i in range(1, sp + 1)] * n * window_length
          )[: n * window_length],
      )
  
      # For selected cases, remove a redundant data point by making it NaN
      if window_length > sp:
          # create a trailing NaN value in the training set
          data.iloc[window_length - 1] = np.nan
  
      # Split into train and test data
      train_data = data[:window_length]
      test_data = data[window_length:]
  
      # Forecast data does not retain the original frequency
      test_data.index.freq = None
  
      # For example, for n=2, window_length=4, sp=3:
  
      # print(train_data)
      # 2021-06-01 00:00:00    1.0
      # 2021-06-01 01:00:00    2.0
      # 2021-06-01 02:00:00    3.0
      # 2021-06-01 03:00:00    NaN
      # Freq: H, dtype: int64
  
      # print(test_data)
      # 2021-06-01 04:00:00    2.0  # (value of 3 hours earlier)
      # 2021-06-01 05:00:00    3.0  # (value of 3 hours earlier)
      # 2021-06-01 06:00:00    1.0  # (value of 6 hours earlier)
      # 2021-06-01 07:00:00    2.0  # (value of 6 hours earlier)
      # dtype: float64
  
      # forecast the next <(n-1) x window_length> hours with periodicity of <sp> hours
      fh = ForecastingHorizon(test_data.index, is_relative=False)
      model = NaiveForecaster(strategy=strategy, sp=sp)
      model.fit(train_data)
      forecast_data = model.predict(fh)
  
      # Make sure that the model (object) reports that it handles missing data
      assert model.get_tag("handles-missing-data")
  
      if sp < window_length:
          # We expect a perfect forecast given our perfectly cyclic data
          pd.testing.assert_series_equal(forecast_data, test_data)
      else:
          # We expect a few forecasts yield NaN values
          for i in range(1 + len(test_data) // sp):
              test_data[i * sp : i * sp + sp - window_length] = np.nan
          pd.testing.assert_series_equal(forecast_data, test_data)
  
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 9 for calling a global function with example usage: test_strategy_mean_and_last_seasonal_additional_combinations(n, window_length, sp, strategy) without return types