code: def impose_product(mass, weights, zsum=False, zmass=1.0):
      """impose a product on a list of points
  
      Inputs:
          mass -- target product of weights
          weights -- a list of sample weights
          zsum -- use counterbalance when mass = 0.0
          zmass -- member scaling when mass = 0.0"""
      from numpy import prod as product
  
      weights = asarray(
          list(weights)
      )  # XXX: faster to use x = array(x, copy=True) ?
      w = float(product(weights))
      n = len(weights)
      if not w:  # XXX: is this the best behavior?
          from numpy import inf
  
          return list(weights * inf)  # protect against ZeroDivision
      if float(mass):
          if w / mass < 0.0:
              return list(
                  -weights / (-w / mass) ** (1.0 / n)
              )  # FIXME: not "mean-preserving"
          return list(
              weights / (w / mass) ** (1.0 / n)
          )  # FIXME: not "mean-preserving"
      # force selected member to satisfy product = 0.0
      if not zsum:
          return list(weights * 0.0)  # FIXME: not "mean-preserving"
      zsum = -1
      p, weights[zsum] = weights[zsum], 0.0
      w = w / p
      n = n - 1
      mass = zmass
      if w / mass >= 0.0:
          return list(weights[:-1] / (w / mass) ** (1.0 / n)) + [
              0.0
          ]  # FIXME: not "mean-preserving"
      return list(-weights[:-1] / (-w / mass) ** (1.0 / n)) + [0.0]
  
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 54 for calling a global function with example usage: impose_product(mass, weights, zsum, zmass) and returns: unknown, list, list, list, unknown, list