code: def point_aslist(search_space, point_as_dict):
      """Convert a dictionary representation of a point from a search space to
      the list representation. The list of values is created from the values of
      the dictionary, sorted by the names of dimensions used as keys.
  
      .. seealso:: :class:`skopt.utils.point_asdict`
  
      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_dict : dict
          dict with parameter names as keys to which corresponding
          parameter values are assigned.
  
      Returns
      -------
      point_as_list : list
          list with point values.The order of
          parameters in the list is given by sorted(params_space.keys()).
  
      Examples
      --------
      >>> from skopt.space.space import Real, Integer
      >>> from skopt.utils import point_aslist
      >>> search_space = {'name1': Real(0,1),
      ...                 'name2': Integer(2,4), 'name3': Real(-1,1)}
      >>> point_as_dict = {'name1': 0.66, 'name2': 3, 'name3': -0.15}
      >>> point_aslist(search_space, point_as_dict)
      [0.66, 3, -0.15]
      """
      point_as_list = [
          point_as_dict[k] for k in sorted(search_space.keys())
      ]
      return point_as_list
  
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 15 for calling a global function with example usage: point_aslist(search_space, point_as_dict) and returns: point_as_list