code: def test_can_set_scipy_distribution():
      space = HyperparameterSpace(
          {
              "rand_int_scipy": randint(low=2, high=5),  # scipy
              "rand_int_neuraxle": RandInt(2, 5),  # neuraxle
              "gamma_scipy": gamma(0.2),  # scipy
          }
      )
  
      p = Identity().set_hyperparams_space(space)
  
      rand_int_scipy = p.get_hyperparams_space()["rand_int_scipy"]
      assert isinstance(
          rand_int_scipy, ScipyDiscreteDistributionWrapper
      )
      for _ in range(20):
          randint_sample = rand_int_scipy.rvs()
          assert randint_sample in rand_int_scipy
  
      gamma_scipy = p.get_hyperparams_space()["gamma_scipy"]
      assert isinstance(gamma_scipy, ScipyContinuousDistributionWrapper)
      for _ in range(20):
          gamma_sample = gamma_scipy.rvs()
          assert isinstance(gamma_sample, float)
          assert gamma_sample in gamma_scipy
  
imports: import math
  import os
  from collections import Counter
  import joblib
  import numpy
  import pytest
  from neuraxle.base import Identity
  from neuraxle.hyperparams.distributions import Choice, LogNormal, LogUniform, Normal, PriorityChoice, RandInt, Uniform, get_index_in_list_with_bool
  from neuraxle.hyperparams.scipy_distributions import Gaussian, Histogram, Poisson, ScipyContinuousDistributionWrapper, ScipyDiscreteDistributionWrapper, ScipyLogUniform, StdMeanLogNormal
  from neuraxle.hyperparams.space import HyperparameterSpace
  from scipy.stats import gamma, norm, randint, uniform
label_desc: Use image node_id 21 for calling a global function with example usage: test_can_set_scipy_distribution() without return types