code: def impose_weight_norm(samples, weights, mass=1.0):
      """normalize the weights for a list of (weighted) points
        (this function is 'mean-preserving')
  
      Inputs:
          samples -- a list of sample points
          weights -- a list of sample weights
          mass -- float target of normalized weights"""
      m = mean(samples, weights)
      wts = normalize(
          weights, mass
      )  # NOTE: not mean-preserving, until next line
      return impose_mean(m, samples, wts), wts
  
imports: from mystic.math.stats import *
  from mystic.math.samples import *
  from mystic.math.integrate import *
  from mystic.symbolic import generate_solvers, generate_constraint, solve
  from mystic.symbolic import generate_conditions, generate_penalty
  from mystic.math import almostEqual
  from mystic.math.distance import Lnorm
  from numpy import asarray
label_desc: Use image node_id 33 for calling a global function with example usage: impose_weight_norm(samples, weights, mass) and returns: impose_mean,  wts