class_docstr: Prophet wrapper for the NeuralProphet forecaster.
  
  Parameters
  ----------
  growth: String 'linear' or 'flat' to specify a linear or
      flat trend. Note: 'flat' is equivalent to 'off' in NeuralProphet.
  changepoints: List of dates at which to include potential changepoints. If
      not specified, potential changepoints are selected automatically.
  n_changepoints: Number of potential changepoints to include. Not used
      if input `changepoints` is supplied. If `changepoints` is not supplied,
      then n_changepoints potential changepoints are selected uniformly from
      the first `changepoint_range` proportion of the history.
  changepoint_range: Proportion of history in which trend changepoints will
      be estimated. Defaults to 0.8 for the first 80%. Not used if
      `changepoints` is specified.
  yearly_seasonality: Fit yearly seasonality.
      Can be 'auto', True, False, or a number of Fourier terms to generate.
  weekly_seasonality: Fit weekly seasonality.
      Can be 'auto', True, False, or a number of Fourier terms to generate.
  daily_seasonality: Fit daily seasonality.
      Can be 'auto', True, False, or a number of Fourier terms to generate.
  holidays: pd.DataFrame with columns holiday (string) and ds (date type)
      and optionally columns lower_window and upper_window which specify a
      range of days around the date to be included as holidays.
      lower_window=-2 will include 2 days prior to the date as holidays. Also
      optionally can have a column prior_scale specifying the prior scale for
      that holiday.
  seasonality_mode: 'additive' (default) or 'multiplicative'.
  seasonality_prior_scale: Not supported for regularisation in NeuralProphet,
      please use the `seasonality_reg` arg instead.
  holidays_prior_scale: Not supported for regularisation in NeuralProphet.
  changepoint_prior_scale: Not supported for regularisation in NeuralProphet,
      please use the `trend_reg` arg instead.
  mcmc_samples: Not required for NeuralProphet
  interval_width: Float, width of the uncertainty intervals provided
      for the forecast. Converted to list of quantiles for NeuralProphet. Use
      the quantiles arg to pass quantiles directly to NeuralProphet.
  uncertainty_samples: Not required for NeuralProphet.
  stan_backend: Not supported by NeuralProphet.
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docstr: Add a seasonal component with specified period, number of Fourier
  components, and prior scale.
  
  Increasing the number of Fourier components allows the seasonality to
  change more quickly (at risk of overfitting). Default values for yearly
  and weekly seasonalities are 10 and 3 respectively.
  
  Increasing prior scale will allow this seasonality component more
  flexibility, decreasing will dampen it. If not provided, will use the
  seasonality_prior_scale provided on initialization (defaults
  to 10).
  
  Mode can be specified as either 'additive' or 'multiplicative'. If not
  specified, self.seasonality_mode will be used (defaults to additive).
  Additive means the seasonality will be added to the trend,
  multiplicative means it will multiply the trend.
  
  If condition_name is provided, the dataframe passed to `fit` and
  `predict` should have a column with the specified condition_name
  containing booleans which decides when to apply seasonality.
  
  Parameters
  ----------
  name: string name of the seasonality component.
  period: float number of days in one period.
  fourier_order: int number of Fourier components to use.
  prior_scale: Not supported in NeuralProphet.
  mode: optional 'additive' or 'multiplicative'
  condition_name: Not supported in NeuralProphet.
  
  Returns
  -------
  The NeuralProphet object.
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code: def add_seasonality(
      self,
      name,
      period,
      fourier_order,
      prior_scale=None,
      mode=None,
      condition_name=None,
      **kwargs,
  ):
      """Add a seasonal component with specified period, number of Fourier
      components, and prior scale.
  
      Increasing the number of Fourier components allows the seasonality to
      change more quickly (at risk of overfitting). Default values for yearly
      and weekly seasonalities are 10 and 3 respectively.
  
      Increasing prior scale will allow this seasonality component more
      flexibility, decreasing will dampen it. If not provided, will use the
      seasonality_prior_scale provided on initialization (defaults
      to 10).
  
      Mode can be specified as either 'additive' or 'multiplicative'. If not
      specified, self.seasonality_mode will be used (defaults to additive).
      Additive means the seasonality will be added to the trend,
      multiplicative means it will multiply the trend.
  
      If condition_name is provided, the dataframe passed to `fit` and
      `predict` should have a column with the specified condition_name
      containing booleans which decides when to apply seasonality.
  
      Parameters
      ----------
      name: string name of the seasonality component.
      period: float number of days in one period.
      fourier_order: int number of Fourier components to use.
      prior_scale: Not supported in NeuralProphet.
      mode: optional 'additive' or 'multiplicative'
      condition_name: Not supported in NeuralProphet.
  
      Returns
      -------
      The NeuralProphet object.
      """
      # Check for unsupported features
      if condition_name:
          raise NotImplementedError(
              "Conditioning on seasonality is not supported in NeuralProphet."
          )
      if prior_scale:
          log.warning(
              "Prior scale is not supported in NeuralProphet. Use the `regularisation` parameter for regularisation."
          )
      # Set attributes in NeuralProphet config
      try:
          self.season_config.mode = mode
      except AttributeError:
          log.warning(
              "Cannot set the seasonality mode attribute in NeuralProphet. Pleas inspect manually."
          )
      # Run the NeuralProphet function
      return super(TorchProphet, self).add_seasonality(
          name, period, fourier_order, **kwargs
      )
  
imports: import logging
  import numpy
  from neuralprophet.forecaster import NeuralProphet
label_desc: Use image node_id 9 for calling the TorchProphet obj's underlying member method code with example usage: obj.add_seasonality(name, period, fourier_order, prior_scale, mode, condition_name) and returns: super