docs: def gaussian_prior_model_for_arguments(self, arguments):
          """
          Create a new instance of model mapper with a set of Gaussian priors based on tuples provided by a previous \
          nonlinear search.
  
          Parameters
          ----------
          arguments: [(float, float)]
              Tuples providing the mean and sigma of gaussians
  
          Returns
          -------
          new_model: ModelMapper
              A new model mapper populated with Gaussian priors
          """
          new_model = copy.deepcopy(self)
  
          model_arguments = {t.name: arguments[t.prior] for t in self.direct_prior_tuples}
  
          for tuple_prior_tuple in self.tuple_prior_tuples:
              setattr(new_model, tuple_prior_tuple.name,
                      tuple_prior_tuple.prior.gaussian_tuple_prior_for_arguments(arguments))
          for prior_tuple in self.direct_prior_tuples:
              setattr(new_model, prior_tuple.name, model_arguments[prior_tuple.name])
          for constant_tuple in self.constant_tuples:
              setattr(new_model, constant_tuple.name, constant_tuple.constant)
  
          for name, prior_model in self.direct_prior_model_tuples:
              setattr(new_model, name, prior_model.gaussian_prior_model_for_arguments(arguments))
  
          return new_model