code: def test_wildcards():
      EXPECTED_WILDCARDS = [
          "*b__c",
          "*b*c",
          "*q",
          "a__c",
          "*e*c",
          "*test",
          "*param",
          "*u",
          "*alpha",
          "*g",
          "*other",
          "*could_also_be_as_fixed",
          "scipy__gaussian",
          "*poisson",
          "*scipy__gaussian",
      ]
  
      wildcards: FlatDict = HYPE_SPACE.to_wildcards()
  
      for wc, ewc in zip(wildcards.keys(), EXPECTED_WILDCARDS):
          assert (
              wc == ewc
          ), f"{wc} != {ewc}, but should be equal as expected."
      for wv, ewv in zip(
          wildcards.values(), HYPE_SPACE.to_flat_dict().values()
      ):
          assert (
              wv == ewv
          ), f"{str(wv)} != {str(ewv)}, but should remain the same."
  
file_path: Neuraxle/testing_neuraxle/hyperparams/test_space.py
imports: import copy
  from collections import OrderedDict
  import pytest
  import scipy
  from neuraxle.hyperparams.distributions import Boolean, Choice, FixedHyperparameter, HyperparameterDistribution, LogNormal, LogUniform, Normal, PriorityChoice, Quantized, RandInt, Uniform
  from neuraxle.hyperparams.scipy_distributions import Gaussian, Poisson
  from neuraxle.hyperparams.space import FlatDict, HyperparameterSamples, HyperparameterSpace, RecursiveDict
label_desc: Use image node_id 4 for calling a global function with example usage: test_wildcards() without return types