code: def test_quantized_uniform():
      low = -10
      high = 10
      hd = Quantized(Uniform(low, high))
  
      samples = hd_rvs_many(hd)
  
      for s in samples:
          assert type(s) == int
      samples_mean = np.abs(np.mean(samples))
      assert samples_mean < 1.0
      assert min(samples) >= -10.0
      assert max(samples) <= 10.0
  
      assert abs(hd.pdf(-10) - 1 / 40) < 1e-6
      assert abs(hd.pdf(-9) - 1 / 20) < 1e-6
      assert abs(hd.pdf(0) - 1 / 20) < 1e-6
      assert abs(hd.pdf(9) - 1 / 20) < 1e-6
      assert abs(hd.pdf(10) - 1 / 40) < 1e-6
  
      assert abs(hd.cdf(-10) - 1 / 40) < 1e-6
      assert abs(hd.cdf(-9) - 1.5 / 20) < 1e-6
      assert abs(hd.cdf(0) - 10.5 / 20) < 1e-6
      assert abs(hd.cdf(9) - 19.5 / 20) < 1e-6
      assert abs(hd.cdf(9.2) - 19.5 / 20) < 1e-6
      assert hd.cdf(10) == 1.0
  
      assert hd.min() == low
      assert hd.max() == high
      assert abs(hd.mean() - 0.0) < 1e-6
      assert abs(hd.var() - 33.50000000000001) < 1e-6
      assert abs(hd.std() - 5.787918451395114) < 1e-6
      # Verify that hd mean and variance also correspond to mean and variance of sampling.
      assert abs(hd.mean() - np.mean(samples)) < 1e-1
      assert abs((hd.var() - np.var(samples)) / hd.var()) < 1e-1
  
imports: import random
  from collections import Counter
  import numpy
  import pytest
  from neuraxle.hyperparams.distributions import *
label_desc: Use image node_id 6 for calling a global function with example usage: test_quantized_uniform() without return types