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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_svd(self, lapack_driver='gesdd', modes=None, key=None, Bck=True, Lplot='In', cmap=None, vmin=None, vmax=None, cmap_topos=None, vmin_topos=None, vmax_topos=None, ntMax=None, nchMax=None, ms=4, inct=[1,10], incX=[1,5], incm=[1,5], lls=None, lct=None, lcch=None, lcm=None, cbck=None, invert=False, fmt_t='06.3f', fmt_X='01.0f', fmt_m='03.0f', fs=None, dmargin=None, labelpad=None, wintit=None, tit=None, fontsize=None, draw=True, connect=True):
 """ Plot the chosen modes of the svd decomposition All modes will be plotted, the keyword 'modes' is only used to determine the reference modes for computing a common scale for vizualisation Runs self.calc_svd() and then plots the result in an interactive figure """

        if self._isSpectral():
            msg = "svd not implemented yet for spectral data class"
            raise Exception(msg)
        # Computing (~0.2 s for 50 channels 1D and 1000 times)
        chronos, s, topos = _comp.calc_svd(self.data, lapack_driver=lapack_driver)

        # Plotting (~11 s for 50 channels 1D and 1000 times)
        kh = _plot.Data_plot_svd(self, chronos, s, topos, modes=modes,
                                 key=key, Bck=Bck, Lplot=Lplot,
                                 cmap=cmap, vmin=vmin, vmax=vmax,
                                 cmap_topos=cmap_topos, vmin_topos=vmin_topos,
                                 vmax_topos=vmax_topos,
                                 ntMax=ntMax, nchMax=nchMax, ms=ms,
                                 inct=inct, incX=incX, incm=incm,
                                 lls=lls, lct=lct, lcch=lcch, lcm=lcm, cbck=cbck,
                                 invert=invert, fmt_t=fmt_t, fmt_X=fmt_X, fmt_m=fmt_m,
                                 fs=fs, dmargin=dmargin, labelpad=labelpad, wintit=wintit,
                                 tit=tit, fontsize=fontsize, draw=draw,
                                 connect=connect)
        return kh