code: def model_from_seasonal_structure(
      seasonal_structure, observed_time_series, allow_drift=True
  ):
      return sum_lib.Sum(
          [  # pylint:disable=g-complex-comprehension
              seasonal.Seasonal(
                  num_seasons=season.num,  # pylint: disable=g-complex-comprehension
                  num_steps_per_season=season.duration,
                  allow_drift=allow_drift,
                  observed_time_series=observed_time_series,
                  name=str(season_type),
              )
              for season_type, season in seasonal_structure.items()
          ],
          observed_time_series=observed_time_series,
      )
  
file_path: probability/tensorflow_probability/python/sts/default_model.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.sts import structural_time_series
  from tensorflow_probability.python.sts.components import local_linear_trend
  from tensorflow_probability.python.sts.components import seasonal
  from tensorflow_probability.python.sts.components import sum
  from tensorflow_probability.python.sts.internal import seasonality_util
  from tensorflow_probability.python.sts.internal import util
label_desc: Use image node_id 2 for calling a global function with example usage: model_from_seasonal_structure(seasonal_structure, observed_time_series, allow_drift) and returns: sum_lib