code: def dimensions_aslist(search_space):
      """Convert a dict representation of a search space into a list of
      dimensions, ordered by sorted(search_space.keys()).
  
      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)
  
      Returns
      -------
      params_space_list: list
          list of skopt.space.Dimension instances.
  
      Examples
      --------
      >>> from guild._skopt.space.space import Real, Integer
      >>> from guild._skopt.utils import dimensions_aslist
      >>> search_space = {'name1': Real(0,1),
      ...                 'name2': Integer(2,4), 'name3': Real(-1,1)}
      >>> dimensions_aslist(search_space)[0]
      Real(low=0, high=1, prior='uniform', transform='identity')
      >>> dimensions_aslist(search_space)[1]
      Integer(low=2, high=4, prior='uniform', transform='identity')
      >>> dimensions_aslist(search_space)[2]
      Real(low=-1, high=1, prior='uniform', transform='identity')
  
      """
      params_space_list = [
          search_space[k] for k in sorted(search_space.keys())
      ]
      return params_space_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 13 for calling a global function with example usage: dimensions_aslist(search_space) and returns: params_space_list