code: def diffev2(
      cost,
      x0,
      npop=4,
      args=(),
      bounds=None,
      ftol=5e-3,
      gtol=None,
      maxiter=None,
      maxfun=None,
      cross=0.9,
      scale=0.8,
      full_output=0,
      disp=1,
      retall=0,
      callback=None,
      **kwds,
  ):
      """Minimize a function using Storn & Price's differential evolution.
  
      Uses Storn & Prices's differential evolution algorithm to find the minimum of a
      function of one or more variables. Mimics a ``scipy.optimize`` style interface.
  
      Args:
          cost (function): the function or method to be minimized: ``y = cost(x)``.
          x0 (ndarray): the initial guess parameter vector ``x`` if desired start
              is a single point, otherwise takes a list of (min,max) bounds that
              define a region from which random initial points are drawn.
          npop (int, default=4): size of the trial solution population.
          args (tuple, default=()): extra arguments for cost.
          bounds (list(tuple), default=None): list of pairs of bounds (min,max),
              one for each parameter.
          ftol (float, default=5e-3): acceptable relative error in ``cost(xopt)``
              for convergence.
          gtol (float, default=None): maximum iterations to run without improvement.
          maxiter (int, default=None): the maximum number of iterations to perform.
          maxfun (int, default=None): the maximum number of function evaluations.
          cross (float, default=0.9): the probability of cross-parameter mutations.
          scale (float, default=0.8): multiplier for mutations on the trial solution.
          full_output (bool, default=False): True if fval and warnflag are desired.
          disp (bool, default=True): if True, print convergence messages.
          retall (bool, default=False): True if allvecs is desired.
          callback (function, default=None): function called after each iteration. The
              interface is ``callback(xk)``, with ``xk`` the current parameter vector.
          handler (bool, default=False): if True, enable handling interrupt signals.
          id (int, default=None): the ``id`` of the solver used in logging.
          strategy (strategy, default=None): override the default mutation strategy.
          itermon (monitor, default=None): override the default GenerationMonitor.
          evalmon (monitor, default=None): override the default EvaluationMonitor.
          constraints (function, default=None): function ``xk' = constraints(xk)``,
              where ``xk`` is the current parameter vector, and ``xk'`` is a parameter
              vector that satisfies the encoded constraints.
          penalty (function, default=None): function ``y = penalty(xk)``, where ``xk``
              is the current parameter vector, and ``y' == 0`` when the encoded
              constraints are satisfied (and ``y' > 0`` otherwise).
          tightrange (bool, default=None): impose bounds and constraints concurrently.
          cliprange (bool, default=None): bounding constraints clip exterior values.
          map (function, default=None): a (parallel) map instance ``y = map(f, x)``.
  
      Returns:
          ``(xopt, {fopt, iter, funcalls, warnflag}, {allvecs})``
  
      Notes:
          - xopt (*ndarray*): the minimizer of the cost function
          - fopt (*float*): value of cost function at minimum: ``fopt = cost(xopt)``
          - iter (*int*): number of iterations
          - funcalls (*int*): number of function calls
          - warnflag (*int*): warning flag:
              - ``1 : Maximum number of function evaluations``
              - ``2 : Maximum number of iterations``
          - allvecs (*list*): a list of solutions at each iteration
      """
      invariant_current = (
          kwds["invariant_current"]
          if "invariant_current" in kwds
          else True
      )
      kwds["invariant_current"] = invariant_current
      return diffev(
          cost,
          x0,
          npop,
          args=args,
          bounds=bounds,
          ftol=ftol,
          gtol=gtol,
          maxiter=maxiter,
          maxfun=maxfun,
          cross=cross,
          scale=scale,
          full_output=full_output,
          disp=disp,
          retall=retall,
          callback=callback,
          **kwds,
      )
  
imports: from mystic.tools import wrap_function, unpair, isiterable
  from mystic.tools import wrap_bounds, wrap_penalty, reduced
  from mystic.abstract_solver import AbstractSolver
  from mystic.abstract_map_solver import AbstractMapSolver
  from numpy import asfarray, ravel
  from collections.abc import Callable
label_desc: Use image node_id 1 for calling a global function with example usage: diffev2(cost, x0, npop, args, bounds, ftol, gtol, maxiter, maxfun, cross, scale, full_output, disp, retall, callback) and returns: diffev