code: def _test_uniform(hd):
      samples = hd.rvs_many(size=100)
      samples_mean = np.abs(np.mean(samples))
  
      assert samples_mean < 4.0
      assert min(samples) >= -10.0
      assert max(samples) <= 10.0
      assert hd.pdf(-10.1) == 0.0
      assert abs(hd.pdf(0) - 1 / (10 + 10)) < 1e-6
      assert hd.pdf(10.1) == 0.0
      assert hd.cdf(-10.1) == 0.0
      assert abs(hd.cdf(0) - (0 + 10) / (10 + 10)) < 1e-6
      assert hd.cdf(10.1) == 1.0
  
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 12 for calling a global function with example usage: _test_uniform(hd) without return types