How to use the lightkurve.prf.GaussianPrior function in lightkurve

----------
        **kwargs : dict
            Arguments to be passed to the `TPFModel` constructor, e.g.
            `star_priors`.

        Returns
        -------
        model : TPFModel object
            Model with appropriate defaults for this Target Pixel File.
        """
        from .prf import TPFModel, StarPrior, BackgroundPrior
        from .prf import UniformPrior, GaussianPrior
        # Set up the model
        if 'star_priors' not in kwargs:
            centr_col, centr_row = self.estimate_centroids()
            star_priors = [StarPrior(col=GaussianPrior(mean=np.nanmedian(centr_col),
                                                       var=np.nanstd(centr_col)**2),
                                     row=GaussianPrior(mean=np.nanmedian(centr_row),
                                                       var=np.nanstd(centr_row)**2),
                                     flux=UniformPrior(lb=0.5*np.nanmax(self.flux[0]),
                                                       ub=2*np.nansum(self.flux[0]) + 1e-10),
                                     targetid=self.targetid)]
            kwargs['star_priors'] = star_priors
        if 'prfmodel' not in kwargs:
            kwargs['prfmodel'] = self.get_prf_model()
        if 'background_prior' not in kwargs:
            if np.all(np.isnan(self.flux_bkg)):  # If TargetPixelFile has no background flux data
                # Use the median of the lower half of flux as an estimate for flux_bkg
                clipped_flux = np.ma.masked_where(self.flux > np.percentile(self.flux, 50),
                                                  self.flux)
                flux_prior = GaussianPrior(mean=np.ma.median(clipped_flux),
                                           var=np.ma.std(clipped_flux)**2)

star_priors = [StarPrior(col=GaussianPrior(mean=np.nanmedian(centr_col),
                                                       var=np.nanstd(centr_col)**2),
                                     row=GaussianPrior(mean=np.nanmedian(centr_row),
                                                       var=np.nanstd(centr_row)**2),
                                     flux=UniformPrior(lb=0.5*np.nanmax(self.flux[0]),
                                                       ub=2*np.nansum(self.flux[0]) + 1e-10),
                                     targetid=self.targetid)]
            kwargs['star_priors'] = star_priors
        if 'prfmodel' not in kwargs:
            kwargs['prfmodel'] = self.get_prf_model()
        if 'background_prior' not in kwargs:
            if np.all(np.isnan(self.flux_bkg)):  # If TargetPixelFile has no background flux data
                # Use the median of the lower half of flux as an estimate for flux_bkg
                clipped_flux = np.ma.masked_where(self.flux > np.percentile(self.flux, 50),
                                                  self.flux)
                flux_prior = GaussianPrior(mean=np.ma.median(clipped_flux),
                                           var=np.ma.std(clipped_flux)**2)
            else:
                flux_prior = GaussianPrior(mean=np.nanmedian(self.flux_bkg),
                                           var=np.nanstd(self.flux_bkg)**2)
            kwargs['background_prior'] = BackgroundPrior(flux=flux_prior)
        return TPFModel(**kwargs)

var=np.nanstd(centr_row)**2),
                                     flux=UniformPrior(lb=0.5*np.nanmax(self.flux[0]),
                                                       ub=2*np.nansum(self.flux[0]) + 1e-10),
                                     targetid=self.targetid)]
            kwargs['star_priors'] = star_priors
        if 'prfmodel' not in kwargs:
            kwargs['prfmodel'] = self.get_prf_model()
        if 'background_prior' not in kwargs:
            if np.all(np.isnan(self.flux_bkg)):  # If TargetPixelFile has no background flux data
                # Use the median of the lower half of flux as an estimate for flux_bkg
                clipped_flux = np.ma.masked_where(self.flux > np.percentile(self.flux, 50),
                                                  self.flux)
                flux_prior = GaussianPrior(mean=np.ma.median(clipped_flux),
                                           var=np.ma.std(clipped_flux)**2)
            else:
                flux_prior = GaussianPrior(mean=np.nanmedian(self.flux_bkg),
                                           var=np.nanstd(self.flux_bkg)**2)
            kwargs['background_prior'] = BackgroundPrior(flux=flux_prior)
        return TPFModel(**kwargs)

Arguments to be passed to the `TPFModel` constructor, e.g.
            `star_priors`.

        Returns
        -------
        model : TPFModel object
            Model with appropriate defaults for this Target Pixel File.
        """
        from .prf import TPFModel, StarPrior, BackgroundPrior
        from .prf import UniformPrior, GaussianPrior
        # Set up the model
        if 'star_priors' not in kwargs:
            centr_col, centr_row = self.estimate_centroids()
            star_priors = [StarPrior(col=GaussianPrior(mean=np.nanmedian(centr_col),
                                                       var=np.nanstd(centr_col)**2),
                                     row=GaussianPrior(mean=np.nanmedian(centr_row),
                                                       var=np.nanstd(centr_row)**2),
                                     flux=UniformPrior(lb=0.5*np.nanmax(self.flux[0]),
                                                       ub=2*np.nansum(self.flux[0]) + 1e-10),
                                     targetid=self.targetid)]
            kwargs['star_priors'] = star_priors
        if 'prfmodel' not in kwargs:
            kwargs['prfmodel'] = self.get_prf_model()
        if 'background_prior' not in kwargs:
            if np.all(np.isnan(self.flux_bkg)):  # If TargetPixelFile has no background flux data
                # Use the median of the lower half of flux as an estimate for flux_bkg
                clipped_flux = np.ma.masked_where(self.flux > np.percentile(self.flux, 50),
                                                  self.flux)
                flux_prior = GaussianPrior(mean=np.ma.median(clipped_flux),
                                           var=np.ma.std(clipped_flux)**2)
            else:
                flux_prior = GaussianPrior(mean=np.nanmedian(self.flux_bkg),