code: def _test_randint(hd):
      samples = hd.rvs_many(size=100)
      samples_mean = np.abs(np.mean(samples))
      invprob = 1 / (10 + 10 + 1)
      assert -5.0 < samples_mean < 5.0
      assert min(samples) >= -10.0
      assert max(samples) <= 10.0
      assert hd.pdf(-11) == 0.0
      assert hd.pdf(-10) == invprob
      assert hd.pdf(0) == invprob
      assert hd.pdf(5) == invprob
      assert hd.pdf(10) == invprob
      assert hd.pdf(11) == 0.0
      assert hd.cdf(-10.1) == 0.0
      assert hd.cdf(-10) == invprob
      assert hd.cdf(5) == 16 * invprob
      assert abs(hd.cdf(10) - 1.0) < 1e-6
      assert hd.cdf(10.1) == 1.0
      assert hd.min() == -10
      assert hd.mean() == 0
      assert hd.std() > 2
      assert hd.max() == 10
  
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 10 for calling a global function with example usage: _test_randint(hd) without return types