code: def solve(
      constraints,
      guess=None,
      nvars=None,
      solver=None,
      lower_bounds=None,
      upper_bounds=None,
      termination=None,
      tightrange=None,
      cliprange=None,
  ):
      """Use optimization to find a solution to a set of constraints.
  
      Inputs:
          constraints -- a constraints solver function or a penalty function
  
      Additional Inputs:
          guess -- list of parameter values proposed to solve the constraints.
          lower_bounds -- list of lower bounds on solution values.
          upper_bounds -- list of upper bounds on solution values.
          nvars -- number of parameter values.
          solver -- the mystic solver to use in the optimization.
          termination -- the mystic termination to use in the optimization.
          tightrange -- if True, impose bounds and constraints concurrently.
          cliprange -- if True, bounding constraints clip exterior values.
  
      NOTE: The resulting constraints will likely be more expensive to evaluate
          and less accurate than writing the constraints solver from scratch.
  
      NOTE: The ensemble solvers are available, using the default NestedSolver,
          where the keyword 'guess' can be used to set the number of solvers.
  
      NOTE: The default solver is 'diffev', with npop=min(40, ndim*5). The default
          termination is ChangeOverGeneration(), and the default guess is randomly
          selected points between the upper and lower bounds.
      """
      npts = 8
      if type(guess) is int:
          npts, guess = guess, None
  
      ndim = (
          1  # XXX: better, increase in while loop catching IndexError ?
      )
      if nvars is not None:
          ndim = nvars
      elif guess is not None:
          ndim = len(guess)
      elif lower_bounds is not None:
          ndim = len(lower_bounds)
      elif upper_bounds is not None:
          ndim = len(upper_bounds)
  
      def cost(x):
          return 1.0
  
      # XXX: don't allow solver string as a short-cut?
      ensemble = False
      if solver is None or solver == "diffev":
          from mystic.solvers import (
              DifferentialEvolutionSolver as TheSolver,
          )
  
          solver = TheSolver(ndim, min(40, ndim * 5))
      elif solver == "diffev2":
          from mystic.solvers import (
              DifferentialEvolutionSolver2 as TheSolver,
          )
  
          solver = TheSolver(ndim, min(40, ndim * 5))
      elif (
          solver == "fmin_powell"
      ):  # XXX: better as the default? (it's not random)
          from mystic.solvers import (
              PowellDirectionalSolver as TheSolver,
          )
  
          solver = TheSolver(ndim)
      elif solver == "fmin":
          from mystic.solvers import (
              NelderMeadSimplexSolver as TheSolver,
          )
  
          solver = TheSolver(ndim)
      elif solver == "buckshot":
          from mystic.solvers import BuckshotSolver as TheSolver
  
          solver = TheSolver(
              ndim, max(8, npts)
          )  # XXX: needs better default?
          ensemble = True
      elif solver == "lattice":
          from mystic.solvers import LatticeSolver as TheSolver
  
          solver = TheSolver(
              ndim, max(8, npts)
          )  # XXX: needs better default?
          ensemble = True
      elif solver == "sparsity":
          from mystic.solvers import SparsitySolver as TheSolver
  
          solver = TheSolver(
              ndim, max(8, npts)
          )  # XXX: needs better default?
          ensemble = True
  
      if termination is None:
          from mystic.termination import ChangeOverGeneration as COG
  
          termination = COG()
      if not ensemble:
          if guess is not None:
              solver.SetInitialPoints(
                  guess
              )  # XXX: nice if 'diffev' had methods
          else:
              solver.SetRandomInitialPoints(lower_bounds, upper_bounds)
      if (
          lower_bounds or upper_bounds
      ):  # XXX: disable/hardwire tight, clip?
          kwds = dict(tight=tightrange, clip=cliprange)
          solver.SetStrictRanges(lower_bounds, upper_bounds, **kwds)
      if hasattr(constraints, "iter") and hasattr(constraints, "error"):
          solver.SetPenalty(constraints)  # i.e. is a penalty function
      else:  # is a constraints solver
          solver.SetConstraints(constraints)
      from numpy import seterr
  
      settings = seterr(all="ignore")
      solver.Solve(cost, termination)
      seterr(**settings)
      soln = solver.bestSolution
  
      from numpy import ndarray, array
  
      if isinstance(soln, ndarray) and not isinstance(guess, ndarray):
          soln = soln.tolist()
      elif isinstance(guess, ndarray) and not isinstance(soln, ndarray):
          soln = array(soln)  # XXX: or always return a list ?
  
      return soln
  
imports: from mystic.math.measures import *
  from mystic.math import almostEqual
  from numpy import sum
  from numpy import asfarray, asarray, choose, zeros, ones, ndarray
  from numpy import shape, broadcast, empty, atleast_1d
  from numpy import round, abs
  from random import randrange, shuffle
  from numpy import array, intersect1d, inf, isnan, where, choose, clip
  from numpy.random import uniform, choice
  from numpy import maximum, minimum
  from numpy import round, abs
  from mystic.tools import synchronized, connected
  from mystic.math.discrete import product_measure
label_desc: Use image node_id 10 for calling a global function with example usage: solve(constraints, guess, nvars, solver, lower_bounds, upper_bounds, termination, tightrange, cliprange) and returns: soln, int