code: def impose_std(s, samples, weights=None):
      """impose a standard deviation on a list of (weighted) points
  
      Args:
          s (float): the target standard deviation
          samples (list): a list of sample points
          weights (list, default=None): a list of sample weights
  
      Returns:
          a list of sample points with the desired weighted standard deviation
  
      Notes:
          this function does not alter the weighted mean"""
      return impose_variance(s**2, samples, weights)
  
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 25 for calling a global function with example usage: impose_std(s, samples, weights) and returns: impose_variance