code: def ess_minimum(f, samples, weights=None, tol=0.0):
      """calculate the min of function for support on the given list of points
  
      ``ess_minimum(f,x,w) = min(f(support(x,w)))``
  
      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 minimum output value for a function at the given support points
      """
      if weights is None:
          return minimum(f, samples)
      return minimum(f, support(samples, weights, 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 7 for calling a global function with example usage: ess_minimum(f, samples, weights, tol) and returns: minimum, minimum