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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def plot_sample(self, nsims=10, plot_data=True, **kwargs):
 """ Plots draws from the posterior predictive density against the data Parameters nsims : int (default : 1000) How many draws from the posterior predictive distribution plot_data boolean Whether to plot the data or not """


        if self.latent_variables.estimation_method not in ['BBVI', 'M-H']:
            raise Exception("No latent variables estimated!")
        else:
            import matplotlib.pyplot as plt
            import seaborn as sns

            figsize = kwargs.get('figsize',(10,7))
            plt.figure(figsize=figsize)
            date_index = self.index[max(self.ar, self.sc):self.data_length]
            mu, Y, scores = self._model(self.latent_variables.get_z_values())
            draws = self.sample(nsims).T
            plt.plot(date_index, draws, label='Posterior Draws', alpha=1.0)
            if plot_data is True:
                plt.plot(date_index, Y, label='Data', c='black', alpha=0.5, linestyle='', marker='s')
            plt.title(self.data_name)
            plt.show()