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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 apply_snapshot(self, argset):
 """ Create a clone of this Channel where histograms are modified according to the values of the nuisance parameters in the snapshot. This is useful when creating post-fit distribution plots. Parameters argset : RooArtSet A RooArgSet of RooRealVar nuisance parameters Returns ------- channel : Channel The modified channel """

        clone = self.Clone()
        args = [var for var in argset if not (
            var.name.startswith('binWidth_obs_x_') or
            var.name.startswith('gamma_stat') or
            var.name.startswith('nom_'))]
        # handle NormFactors first
        nargs = []
        for var in args:
            is_norm = False
            name = var.name.replace('alpha_', '')
            for sample in clone.samples:
                if sample.GetNormFactor(name) is not None:
                    log.info("applying snapshot of {0} on sample {1}".format(
                        name, sample.name))
                    is_norm = True
                    # scale the entire sample
                    sample *= var.value
                    # add an OverallSys for the error
                    osys = OverallSys(name,
                        low=1. - var.error / var.value,
                        high=1. + var.error / var.value)
                    sample.AddOverallSys(osys)
                    # remove the NormFactor
                    sample.RemoveNormFactor(name)
            if not is_norm:
                nargs.append(var)
        # modify the nominal shape and systematics
        for sample in clone.samples:
            # check that hist is not NULL
            if sample.hist is None:
                raise RuntimeError(
                    "sample {0} does not have a "
                    "nominal histogram".format(sample.name))
            nominal = sample.hist.Clone(shallow=True)
            for var in nargs:
                name = var.name.replace('alpha_', '')
                if not sample.has_sys(name):
                    continue
                log.info("applying snapshot of {0} on sample {1}".format(
                    name, sample.name))
                low, high = sample.sys_hist(name)
                # modify nominal
                val = var.value
                if val > 0:
                    sample.hist += (high - nominal) * val
                elif val < 0:
                    sample.hist += (nominal - low) * val
                # TODO:
                # modify OverallSys
                # modify HistoSys
        return clone