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: Validates the inputs to NeuralProphet.
docstr_tok: ["Validates","the","inputs","to","NeuralProphet","."]
code: def validate_inputs(self):
      """
      Validates the inputs to NeuralProphet.
      """
      log.error(
          "Not required in NeuralProphet as all inputs are automatically checked."
      )
  
imports: import logging
  import numpy
  from neuralprophet.forecaster import NeuralProphet
label_desc: Use image node_id 2 for calling the TorchProphet obj's underlying member method code with example usage: obj.validate_inputs() without return types