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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 isochrone(self,age,feh=0.0,minm=None,maxm=None,dm=0.02, return_df=True,distance=None,AV=0.0):
 """ Returns stellar models at constant age and feh, for a range of masses :param age: log10(age) of desired isochrone. :param feh: (optional) Metallicity of desired isochrone (default = 0.0) :param minm, maxm: (optional) Mass range of desired isochrone (will default to max and min available) :param dm: (optional) Spacing in mass of desired isochrone. Default = 0.02 Msun. :param return_df: (optional) Whether to return a :class:``pandas.DataFrame`` or dictionary. Default is ``True``. :param distance: Distance in pc. If passed, then mags will be converted to apparent mags based on distance (and ``AV``). :param AV: V-band extinction (magnitudes). :return: :class:`pandas.DataFrame` or dictionary containing results. """

        if minm is None:
            minm = self.minmass
        if maxm is None:
            maxm = self.maxmass
        ms = np.arange(minm,maxm,dm)
        ages = np.ones(ms.shape)*age

        Ms = self.mass(ms,ages,feh)
        Rs = self.radius(ms,ages,feh)
        logLs = self.logL(ms,ages,feh)
        loggs = self.logg(ms,ages,feh)
        Teffs = self.Teff(ms,ages,feh)
        mags = {band:self.mag[band](ms,ages,feh) for band in self.bands}
        #for band in self.bands:
        #    mags[band] = self.mag[band](ms,ages)
        if distance is not None:
            dm = 5*np.log10(distance) - 5
            for band in mags:
                A = AV*EXTINCTION[band]
                mags[band] = mags[band] + dm + A

        props = {'M':Ms,'R':Rs,'logL':logLs,'logg':loggs,
                'Teff':Teffs,'mag':mags}

        if not return_df:
            return props
        else:
            d = {}
            for key in props.keys():
                if key=='mag':
                    for m in props['mag'].keys():
                        d['{}_mag'.format(m)] = props['mag'][m]
                else:
                    d[key] = props[key]
            try:
                df = pd.DataFrame(d)
            except ValueError:
                df = pd.DataFrame(d, index=[0])
            return df