code: def test_can_update_scipy_distribution():
      p = Identity().set_hyperparams_space(
          HyperparameterSpace(
              {"rand_int_neuraxle": RandInt(2, 5)}  # neuraxle
          )
      )
  
      p.update_hyperparams_space(
          HyperparameterSpace(
              {
                  "rand_int_scipy": randint(low=2, high=5),  # scipy
                  "gamma_scipy": gamma(0.2),  # scipy
              }
          )
      )
  
      assert isinstance(
          p.get_hyperparams_space()["rand_int_scipy"],
          ScipyDiscreteDistributionWrapper,
      )
      assert isinstance(
          p.get_hyperparams_space()["gamma_scipy"],
          ScipyContinuousDistributionWrapper,
      )
      randint_sample = p.get_hyperparams_space()["rand_int_scipy"].rvs()
      gamma_sample = p.get_hyperparams_space()["gamma_scipy"].rvs()
      assert 5 >= randint_sample >= 2
      assert isinstance(gamma_sample, float)
  
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 22 for calling a global function with example usage: test_can_update_scipy_distribution() without return types