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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 constrain_norms(self, srcNames, cov_scale=1.0):
 """Constrain the normalizations of one or more sources by adding gaussian priors with sigma equal to the parameter error times a scaling factor."""


        # Get the covariance matrix

        for name in srcNames:
            par = self.like.normPar(name)

            err = par.error()
            val = par.getValue()

            if par.error() == 0.0 or not par.isFree():
                continue

            self.add_gauss_prior(name, par.getName(),
                                 val, err * cov_scale)