code: def point_asdict(search_space, point_as_list):
      """Convert the list representation of a point from a search space
      to the dictionary representation, where keys are dimension names
      and values are corresponding to the values of dimensions in the list.
  
      .. seealso:: :class:`skopt.utils.point_aslist`
  
      Parameters
      ----------
      search_space : dict
          Represents search space. The keys are dimension names (strings)
          and values are instances of classes that inherit from the class
          :class:`skopt.space.Dimension` (Real, Integer or Categorical)
  
      point_as_list : list
          list with parameter values.The order of parameters in the list
          is given by sorted(params_space.keys()).
  
      Returns
      -------
      params_dict : OrderedDict
          dictionary with parameter names as keys to which
          corresponding parameter values are assigned.
  
      Examples
      --------
      >>> from guild._skopt.space.space import Real, Integer
      >>> from guild._skopt.utils import point_asdict
      >>> search_space = {'name1': Real(0,1),
      ...                 'name2': Integer(2,4), 'name3': Real(-1,1)}
      >>> point_as_list = [0.66, 3, -0.15]
      >>> point_asdict(search_space, point_as_list)
      OrderedDict([('name1', 0.66), ('name2', 3), ('name3', -0.15)])
      """
      params_dict = OrderedDict()
      for k, v in zip(sorted(search_space.keys()), point_as_list):
          params_dict[k] = v
      return params_dict
  
imports: from copy import deepcopy
  from functools import wraps
  from sklearn.utils import check_random_state
  import numpy
  from scipy.optimize import OptimizeResult
  from scipy.optimize import minimize
  from sklearn.base import is_regressor
  from sklearn.ensemble import GradientBoostingRegressor
  from joblib import dump
  from joblib import load
  from collections import OrderedDict
  from .learning import ExtraTreesRegressor
  from .learning import GaussianProcessRegressor
  from .learning import GradientBoostingQuantileRegressor
  from .learning import RandomForestRegressor
  from .learning.gaussian_process.kernels import ConstantKernel
  from .learning.gaussian_process.kernels import HammingKernel
  from .learning.gaussian_process.kernels import Matern
  from .sampler import Sobol, Lhs, Hammersly, Halton, Grid
  from .sampler import InitialPointGenerator
  from .space import Space, Categorical, Integer, Real, Dimension
label_desc: Use image node_id 14 for calling a global function with example usage: point_asdict(search_space, point_as_list) and returns: params_dict