class_docstr: Implementation of the classic statistical model SARIMA (Seasonal
  AutoRegressive Integrated Moving Average) for forecasting.
code: def set_seasonality(self, theta, train_data: UnivariateTimeSeries):
      # Make sure seasonality is a positive int, and set it to 1 if the train data is constant
      theta = (
          1
          if np.max(train_data) == np.min(train_data)
          else max(1, int(theta))
      )
      self.config.seasonal_order = self.seasonal_order[:-1] + (theta,)
  
imports: import logging
  import warnings
  from typing import List, Tuple
  import numpy
  import pandas
  import statsmodels.api
  from merlion.models.automl.seasonality import SeasonalityModel
  from merlion.models.forecast.base import ForecasterExogBase, ForecasterExogConfig
  from merlion.transform.resample import TemporalResample
  from merlion.utils.time_series import UnivariateTimeSeries, to_pd_datetime, to_timestamp
label_desc: Use image node_id 9 for calling the Sarima obj's underlying member method code with example usage: obj.set_seasonality(theta, train_data) without return types