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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_posterior_marginal(self, idx_param=0, res=100, smoothing=0, range_min=None, range_max=None, label_xaxis=True, other_plot_args={}, true_model=None ):
 """ Plots a marginal of the requested parameter. :param int idx_param: Index of parameter to be marginalized. :param int res1: Resolution of of the axis. :param float smoothing: Standard deviation of the Gaussian kernel used to smooth; same units as parameter. :param float range_min: Minimum range of the output axis. :param float range_max: Maximum range of the output axis. :param bool label_xaxis: Labels the :math:`x`-axis with the model parameter name given by this updater's model. :param dict other_plot_args: Keyword arguments to be passed to matplotlib's ``plot`` function. :param np.ndarray true_model: Plots a given model parameter vector as the "true" model for comparison. .. seealso:: :meth:`SMCUpdater.posterior_marginal` """

        res = plt.plot(*self.posterior_marginal(
            idx_param, res, smoothing,
            range_min, range_max
        ), **other_plot_args)
        if label_xaxis:
            plt.xlabel('${}$'.format(self.model.modelparam_names[idx_param]))
        if true_model is not None:
            true_model = true_model[0, idx_param] if true_model.ndim == 2 else true_model[idx_param]
            old_ylim = plt.ylim()
            plt.vlines(true_model, old_ylim[0] - 0.1, old_ylim[1] + 0.1, color='k', linestyles='--')
            plt.ylim(old_ylim)

        return res