docstr: Compute single seasonality component.
  
  Parameters
  ----------
      features : torch.Tensor, float
          Features related to seasonality component, dims: (batch, n_forecasts, n_features)
      name : str
          Name of seasonality. for attribution to corresponding model weights.
      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 compute_fourier(self, features, name, meta=None):
      """Compute single seasonality component.
  
      Parameters
      ----------
          features : torch.Tensor, float
              Features related to seasonality component, dims: (batch, n_forecasts, n_features)
          name : str
              Name of seasonality. for attribution to corresponding model weights.
          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)
      """
      pass
  
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 2 for calling the FourierSeasonality obj's underlying member method code with example usage: obj.compute_fourier(features, name, meta) without return types