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: Fit the NeuralProphet model.
  
  This sets self.params to contain the fitted model parameters. It is a
  dictionary parameter names as keys and the following items:
      k (Mx1 array): M posterior samples of the initial slope.
      m (Mx1 array): The initial intercept.
      delta (MxN array): The slope change at each of N changepoints.
      beta (MxK matrix): Coefficients for K seasonality features.
      sigma_obs (Mx1 array): Noise level.
  Note that M=1 if MAP estimation.
  
  Parameters
  ----------
  df: pd.DataFrame containing the history. Must have columns ds (date
      type) and y, the time series. If self.growth is 'logistic', then
      df must also have a column cap that specifies the capacity at
      each ds.
  kwargs: Additional arguments passed to the optimizing or sampling
      functions in Stan.
  
  Returns
  -------
  The fitted NeuralProphet object.
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code: def fit(self, df, **kwargs):
      """Fit the NeuralProphet model.
  
      This sets self.params to contain the fitted model parameters. It is a
      dictionary parameter names as keys and the following items:
          k (Mx1 array): M posterior samples of the initial slope.
          m (Mx1 array): The initial intercept.
          delta (MxN array): The slope change at each of N changepoints.
          beta (MxK matrix): Coefficients for K seasonality features.
          sigma_obs (Mx1 array): Noise level.
      Note that M=1 if MAP estimation.
  
      Parameters
      ----------
      df: pd.DataFrame containing the history. Must have columns ds (date
          type) and y, the time series. If self.growth is 'logistic', then
          df must also have a column cap that specifies the capacity at
          each ds.
      kwargs: Additional arguments passed to the optimizing or sampling
          functions in Stan.
  
      Returns
      -------
      The fitted NeuralProphet object.
      """
      # Check for unsupported features
      if "cap" in df.columns:
          raise NotImplementedError(
              "Saturating forecasts using cap is not supported in NeuralProphet."
          )
      if "show_progress" in kwargs:
          del kwargs["show_progress"]
      # Handle holidays as events
      if hasattr(self, "events_df"):
          df = self.create_df_with_events(df, self.events_df)
      # Run the NeuralProphet function
      metrics_df = super(TorchProphet, self).fit(df=df, **kwargs)
      # Store the df for future use like in Prophet
      self.history = df
      return metrics_df
  
imports: import logging
  import numpy
  from neuralprophet.forecaster import NeuralProphet
label_desc: Use image node_id 5 for calling the TorchProphet obj's underlying member method code with example usage: obj.fit(df) and returns: metrics_df