code: def config_seasonality_to_model_dims(
      config_seasonality: ConfigSeasonality,
  ):
      """Convert the NeuralProphet seasonal model configuration to input dims for TimeNet model.
  
      Parameters
      ----------
          config_seasonality : configure.ConfigSeasonality
              NeuralProphet seasonal model configuration
  
      Returns
      -------
          dict(int)
              Input dims for TimeNet model
      """
      if (
          config_seasonality is None
          or len(config_seasonality.periods) < 1
      ):
          return None
      seasonal_dims = OrderedDict({})
      for name, period in config_seasonality.periods.items():
          resolution = period.resolution
          if config_seasonality.computation == "fourier":
              resolution = 2 * resolution
          seasonal_dims[name] = resolution
      return seasonal_dims
  
imports: from __future__ import annotations
  import logging
  import math
  import os
  import sys
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Iterable, Optional, Union
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from neuralprophet import utils_torch
  from neuralprophet.hdays_utils import get_country_holidays
  from neuralprophet.logger import ProgressBar
label_desc: Use image node_id 12 for calling a global function with example usage: config_seasonality_to_model_dims(config_seasonality) and returns: seasonal_dims, None