code: def plot_predict(self, h=5, past_values=20, intervals=True, **kwargs):
               
  
          figsize = kwargs.get('figsize',(10,7))
  
          if self.latent_variables.estimated is False:
              raise Exception("No latent variables estimated!")
          else:
  
              import matplotlib.pyplot as plt
              import seaborn as sns
  
              # Retrieve data, dates and (transformed) latent variables
              mu, Y = self._model(self.latent_variables.get_z_values())         
              date_index = self.shift_dates(h)
              t_z = self.transform_z()
  
              # Get mean prediction and simulations (for errors)
              mean_values = self._mean_prediction(mu, Y, h, t_z)
              if intervals is True:
                  sim_values = self._sim_prediction(mu, Y, h, t_z, 15000)
              else:
                  sim_values = self._sim_prediction(mu, Y, h, t_z, 2)
              error_bars, forecasted_values, plot_values, plot_index = self._summarize_simulations(mean_values, sim_values, date_index, h, past_values)
  
              plt.figure(figsize=figsize)
              if intervals is True:
                  alpha =[0.15*i/float(100) for i in range(50,12,-2)]
                  for count, pre in enumerate(error_bars):
                      plt.fill_between(date_index[-h-1:], forecasted_values-pre, forecasted_values+pre,alpha=alpha[count])            
              plt.plot(plot_index,plot_values)
              plt.title("Forecast for " + self.data_name)
              plt.xlabel("Time")
              plt.ylabel(self.data_name)
              plt.show()
docs: Plots forecasts with the estimated model
  
          Parameters
          ----------
          h : int (default : 5)
              How many steps ahead would you like to forecast?
  
          past_values : int (default : 20)
              How many past observations to show on the forecast graph?
  
          intervals : boolean
              Would you like to show prediction intervals for the forecast?
  
          Returns
          ----------
          - Plot of the forecast