code: def impose_variance(v, samples, weights=None):  # ,tol=0):
      """impose a variance on a list of (weighted) points
  
      Args:
          v (float): the target variance
          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 variance
  
      Notes:
          this function does not alter the weighted mean"""
      m = mean(samples, weights)
      samples = asarray(
          list(samples)
      )  # XXX: faster to use x = array(x, copy=True) ?
      sv = variance(samples, weights)  # ,m)
      if not sv:  # protect against ZeroDivision when variance = 0
          if not v:  # variance is to be 0
              return [float(i) for i in samples]  # samples.tolist()
          from numpy import nan
  
          return [nan] * len(
              samples
          )  # XXX: better to space pts evenly across range?
      from numpy import sqrt, seterr
  
      err = seterr(invalid="ignore")
      scale = sqrt(float(v) / sv)
      seterr(**err)
      samples = (
          samples * scale
      )  # NOTE: not "mean-preserving", until the next line
      return impose_mean(m, 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 24 for calling a global function with example usage: impose_variance(v, samples, weights) and returns: impose_mean, unknown, unknown