code: def expected_variance(f, samples, weights=None, tol=0.0):
      """calculate the (weighted) expected variance of a function
  
      Args:
          f (func): a function that takes a list and returns a number
          samples (list): a list of sample points
          weights (list, default=None): a list of sample weights
          tol (float, default=0.0): a tolerance, where any ``weight <= tol`` is zero
  
      Returns:
          the weighted expected variance of f on a list of sample points
      """
      return _expected_moment(f, samples, weights, order=2, tol=tol)
  
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 12 for calling a global function with example usage: expected_variance(f, samples, weights, tol) and returns: _expected_moment