code: def plot_fit(self, **kwargs):
          
          import matplotlib.pyplot as plt
          import seaborn as sns
  
          figsize = kwargs.get('figsize',(10,7))
  
          if self.latent_variables.estimated is False:
              raise Exception("No latent variables estimated!")
          else:
              plt.figure(figsize=figsize)
              date_index = self.index[max(self.p, self.q):]
              t_params = self.transform_z()
              sigma2, Y, ___ = self._model(self.latent_variables.get_z_values())
              plt.plot(date_index, np.abs(Y-t_params[-1]), label=self.data_name + ' Absolute Demeaned Values')
              plt.plot(date_index, np.power(sigma2,0.5), label='GARCH(' + str(self.p) + ',' + str(self.q) + ') Conditional Volatility',c='black')
              plt.title(self.data_name + " Volatility Plot")  
              plt.legend(loc=2)   
              plt.show()
docs: Plots the fit of the model
  
          Returns
          ----------
          None (plots data and the fit)