code: def __init__(
      self,
      config,
      id_list,
      quantiles,
      num_seasonalities_modelled,
      n_forecasts,
      device,
  ):
      super().__init__(
          config=config,
          n_forecasts=n_forecasts,
          num_seasonalities_modelled=num_seasonalities_modelled,
          quantiles=quantiles,
          id_list=id_list,
          device=device,
      )
      self.season_dims = utils.config_seasonality_to_model_dims(
          self.config_seasonality
      )
      if self.season_dims is not None:
          # Seasonality parameters for global or local modelling
          self.season_params = nn.ParameterDict(
              {
                  # dimensions -
                  # [no. of quantiles, num_seasonalities_modelled, no. of fourier terms for each seasonality]
                  name: init_parameter(
                      dims=[len(self.quantiles)]
                      + [self.num_seasonalities_modelled]
                      + [dim]
                  )
                  for name, dim in self.season_dims.items()
              }
          )
  
file_path: neural_prophet/neuralprophet/components/seasonality/fourier.py
imports: from abc import abstractmethod
  import torch
  import torch.nn
  from neuralprophet import utils
  from neuralprophet.components.seasonality import Seasonality
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 1 to create a new FourierSeasonality object from inherited base classes: Seasonality with example: obj = FourierSeasonality(config, id_list, quantiles, num_seasonalities_modelled, n_forecasts, device)