docstr: Compute all seasonality components.
  
  Parameters
  ----------
      s : torch.Tensor, float
          dict of named seasonalities (keys) with their features (values)
          dims of each dict value (batch, n_forecasts, n_features)
      meta: dict
          Metadata about the all the samples of the model input batch. Contains the following:
              * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.
  
  Returns
  -------
      torch.Tensor
          Forecast component of dims (batch, n_forecasts)
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code: def forward(self, s, meta):
      """Compute all seasonality components.
  
      Parameters
      ----------
          s : torch.Tensor, float
              dict of named seasonalities (keys) with their features (values)
              dims of each dict value (batch, n_forecasts, n_features)
          meta: dict
              Metadata about the all the samples of the model input batch. Contains the following:
                  * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.
  
      Returns
      -------
          torch.Tensor
              Forecast component of dims (batch, n_forecasts)
      """
      device = s[list(s.keys())[0]].device
      x = torch.zeros(
          size=(
              s[list(s.keys())[0]].shape[0],
              s[list(s.keys())[0]].shape[1],
              len(self.quantiles),
          ),
          device=device,
      )
      for name, features in s.items():
          x = x + self.compute_fourier(features, name, meta)
      return x
  
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 3 for calling the FourierSeasonality obj's underlying member method code with example usage: obj.forward(s, meta) and returns: x