class_docstr: Differential Evolution optimization.
      
class_docstr_tok: ["Differential","Evolution","optimization","."]
docstr: process and activate input settings
  
  Args:
      callback (function, default=None): function called after each iteration. The
          interface is ``callback(xk)``, with ``xk`` the current parameter vector.
      disp (bool, default=False): if True, print convergence messages.
      EvaluationMonitor (monitor, default=None): a monitor instance to capture
          each evaluation of cost.
      StepMonitor (monitor, default=None): a monitor instance to capture each
          iteration's best results.
      penalty (penalty, default=None): function of the form: ``y' = penalty(xk)``,
          with ``y = cost(xk) + y'`` and ``xk`` is the current parameter vector.
      constraints (constraint, default=None): function of the form:
          ``xk' = constraints(xk)``, where ``xk`` is the current parameter vector.
      strategy (strategy, default=Best1Bin): the mutation strategy for generating
          new trial solutions.
      CrossProbability (float, default=0.9): the probability of cross-parameter
          mutations.
      ScalingFactor (float, default=0.8): multiplier for mutations on the trial
          solution.
  
  Notes:
      ``callback`` and ``disp`` are 'sticky', in that once they are given, they
      remain set until they are explicitly changed. Conversely, the other inputs
      are not sticky, and are thus set for a one-time use.
          
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code: def _process_inputs(self, kwds):
      """process and activate input settings
  
      Args:
          callback (function, default=None): function called after each iteration. The
              interface is ``callback(xk)``, with ``xk`` the current parameter vector.
          disp (bool, default=False): if True, print convergence messages.
          EvaluationMonitor (monitor, default=None): a monitor instance to capture
              each evaluation of cost.
          StepMonitor (monitor, default=None): a monitor instance to capture each
              iteration's best results.
          penalty (penalty, default=None): function of the form: ``y' = penalty(xk)``,
              with ``y = cost(xk) + y'`` and ``xk`` is the current parameter vector.
          constraints (constraint, default=None): function of the form:
              ``xk' = constraints(xk)``, where ``xk`` is the current parameter vector.
          strategy (strategy, default=Best1Bin): the mutation strategy for generating
              new trial solutions.
          CrossProbability (float, default=0.9): the probability of cross-parameter
              mutations.
          ScalingFactor (float, default=0.8): multiplier for mutations on the trial
              solution.
  
      Notes:
          ``callback`` and ``disp`` are 'sticky', in that once they are given, they
          remain set until they are explicitly changed. Conversely, the other inputs
          are not sticky, and are thus set for a one-time use.
      """
      # allow for inputs that don't conform to AbstractSolver interface
      # NOTE: not sticky: callback, disp
      # NOTE: sticky: EvaluationMonitor, StepMonitor, penalty, constraints
      # NOTE: sticky: strategy, CrossProbability, ScalingFactor
      settings = super(
          DifferentialEvolutionSolver, self
      )._process_inputs(kwds)
      from mystic import strategy
  
      strategy = getattr(
          strategy, self.strategy, strategy.Best1Bin
      )  # XXX: None?
      settings.update(
          {"strategy": strategy}
      )  # mutation strategy (see mystic.strategy)
      probability = (
          self.probability
      )  # potential for parameter cross-mutation
      scale = self.scale  # multiplier for mutation impact
      [
          settings.update({i: j})
          for (i, j) in getattr(kwds, "iteritems", kwds.items)()
          if i in settings
      ]
      word = "CrossProbability"
      self.probability = kwds[word] if word in kwds else probability
      word = "ScalingFactor"
      self.scale = kwds[word] if word in kwds else scale
      self.strategy = getattr(
          settings["strategy"], "__name__", "Best1Bin"
      )
      return settings
  
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 6 for calling the DifferentialEvolutionSolver obj's underlying member method code with example usage: obj._process_inputs(kwds) and returns: settings