code: def test_when_data_shorter_than_seasonal_period_then_average_forecast_is_used():
      prediction_length = 20
      seasonal_period = (
          DUMMY_TS_DATAFRAME.num_timesteps_per_item().max()
          + prediction_length
      )
  
      avg = AverageModel(
          hyperparameters=DEFAULT_HYPERPARAMETERS,
          prediction_length=prediction_length,
      )
      avg.fit(train_data=DUMMY_TS_DATAFRAME)
      predictions_avg = avg.predict(DUMMY_TS_DATAFRAME)
  
      seasonal_avg = SeasonalAverageModel(
          hyperparameters={
              "seasonal_period": seasonal_period,
              "n_jobs": 1,
          },
          prediction_length=prediction_length,
      )
      seasonal_avg.fit(train_data=DUMMY_TS_DATAFRAME)
      predictions_seasonal_avg = seasonal_avg.predict(
          DUMMY_TS_DATAFRAME
      )
  
      assert np.allclose(
          predictions_avg.values, predictions_seasonal_avg.values
      )
  
imports: import logging
  from typing import Dict
  import numpy
  import pandas
  import pytest
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.models.local import ADIDAModel, AutoARIMAModel, AutoCESModel, AutoETSModel, AverageModel, CrostonClassicModel, CrostonOptimizedModel, CrostonSBAModel, DynamicOptimizedThetaModel, ETSModel, IMAPAModel, NaiveModel, NPTSModel, SeasonalAverageModel, SeasonalNaiveModel, ThetaModel, ZeroModel
  from autogluon.timeseries.models.local.statsforecast import AbstractConformalizedStatsForecastModel
  from ..common import DUMMY_TS_DATAFRAME, DUMMY_VARIABLE_LENGTH_TS_DATAFRAME, dict_equal_primitive, get_data_frame_with_item_index
label_desc: Use image node_id 13 for calling a global function with example usage: test_when_data_shorter_than_seasonal_period_then_average_forecast_is_used() without return types