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 in built-in holidays for the specified country.
  
  These holidays will be included in addition to any specified on model
  initialization.
  
  Holidays will be calculated for arbitrary date ranges in the history
  and future. See the online documentation for the list of countries with
  built-in holidays.
  
  Built-in country holidays can only be set for a single country.
  
  Parameters
  ----------
  country_name: Name of the country, like 'UnitedStates' or 'US'
  
  Returns
  -------
  The NeuralProphet object.
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code: def add_country_holidays(self, country_name, **kwargs):
      """Add in built-in holidays for the specified country.
  
      These holidays will be included in addition to any specified on model
      initialization.
  
      Holidays will be calculated for arbitrary date ranges in the history
      and future. See the online documentation for the list of countries with
      built-in holidays.
  
      Built-in country holidays can only be set for a single country.
  
      Parameters
      ----------
      country_name: Name of the country, like 'UnitedStates' or 'US'
  
      Returns
      -------
      The NeuralProphet object.
      """
      super(TorchProphet, self).add_country_holidays(
          country_name=country_name, **kwargs
      )
  
imports: import logging
  import numpy
  from neuralprophet.forecaster import NeuralProphet
label_desc: Use image node_id 11 for calling the TorchProphet obj's underlying member method code with example usage: obj.add_country_holidays(country_name) without return types