code: def test_dict_list_space_representation():
      """
      Tests whether the conversion of the dictionary and list representation
      of a point from a search space works properly.
      """
  
      chef_space = {
          "Cooking time": (0, 1200),  # in minutes
          "Main ingredient": [
              "cheese",
              "cherimoya",
              "chicken",
              "chard",
              "chocolate",
              "chicory",
          ],
          "Secondary ingredient": ["love", "passion", "dedication"],
          "Cooking temperature": (-273.16, 10000.0),  # in Celsius
      }
  
      opt = Optimizer(dimensions=dimensions_aslist(chef_space))
      point = opt.ask()
  
      # check if the back transformed point and original one are equivalent
      assert_equal(
          point,
          point_aslist(chef_space, point_asdict(chef_space, point)),
      )
  
imports: import pytest
  import tempfile
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_equal
  from numpy.testing import assert_raises
  import numpy
  from skopt import gp_minimize, forest_minimize
  from skopt import load
  from skopt import dump
  from skopt import expected_minimum, expected_minimum_random_sampling
  from skopt.benchmarks import bench1
  from skopt.benchmarks import bench3
  from skopt.learning import ExtraTreesRegressor
  from skopt import Optimizer
  from skopt import Space
  from skopt.space import Dimension
  from skopt.utils import point_asdict
  from skopt.utils import point_aslist
  from skopt.utils import dimensions_aslist
  from skopt.utils import has_gradients
  from skopt.utils import cook_estimator
  from skopt.utils import normalize_dimensions
  from skopt.utils import use_named_args
  from skopt.utils import check_list_types
  from skopt.utils import check_dimension_names
  from skopt.space import Real, Integer, Categorical
label_desc: Use image node_id 6 for calling a global function with example usage: test_dict_list_space_representation() without return types