code: def _constraint_solver(
      parameter: p.Parameter, budget: int
  ) -> p.Parameter:
      """Runs a suboptimization to solve the parameter constraints"""
      parameter_without_constraint = parameter.copy()
      parameter_without_constraint._constraint_checkers.clear()
      parameter_without_constraint.tabu_length = 0
      opt = registry["OnePlusOne"](
          parameter_without_constraint, num_workers=1, budget=budget
      )
      for _ in range(budget):
          cand = opt.ask()
          # Our objective function is minimum for the point the closest to
          # the original candidate under the constraints.
          penalty = sum(
              utils._float_penalty(func(cand.value))
              for func in parameter._constraint_checkers
          )
  
          # TODO: this may not scale well with dimension
          distance = np.tanh(
              np.sum(
                  cand.get_standardized_data(reference=parameter) ** 2
              )
          )
          # TODO: because of the return whenever constraints are satisfied, the first case never arises
          loss = distance if penalty <= 0 else penalty + distance + 1.0
          opt.tell(cand, loss)
          if penalty <= 0:  # constraints are satisfied
              break
      data = opt.recommend().get_standardized_data(
          reference=parameter_without_constraint
      )
      return parameter.spawn_child().set_standardized_data(data)
  
imports: import pickle
  import warnings
  from pathlib import Path
  from numbers import Real
  from collections import deque
  import numpy
  import nevergrad.common.typing
  from nevergrad.parametrization import parameter
  from nevergrad.common import tools
  from nevergrad.common import errors
  from nevergrad.common.decorators import Registry
  from .None import utils
  from .None import multiobjective
label_desc: Use image node_id 4 for calling a global function with example usage: _constraint_solver(parameter, budget) and returns: parameter