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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_sed(sed, showlnl=False, **kwargs):
 """Render a plot of a spectral energy distribution. Parameters showlnl : bool Overlay a map of the delta-loglikelihood values vs. flux in each energy bin. cmap : str Colormap that will be used for the delta-loglikelihood map. llhcut : float Minimum delta-loglikelihood value. ul_ts_threshold : float TS threshold that determines whether the MLE or UL is plotted in each energy bin. """


        ax = kwargs.pop('ax', plt.gca())
        cmap = kwargs.get('cmap', 'BuGn')

        annotate_name(sed, ax=ax)
        SEDPlotter.plot_flux_points(sed, **kwargs)

        if np.any(sed['ts'] > 9.):

            if 'model_flux' in sed:
                SEDPlotter.plot_model(sed['model_flux'],
                                      noband=showlnl, **kwargs)

        if showlnl:
            SEDPlotter.plot_lnlscan(sed, **kwargs)

        ax.set_yscale('log')
        ax.set_xscale('log')
        ax.set_xlabel('Energy [MeV]')
        ax.set_ylabel('E$^{2}$dN/dE [MeV cm$^{-2}$ s$^{-1}$]')