code: def test_uniform():
      low = -10
      high = 10
      hd = Uniform(low, high)
  
      samples = hd_rvs_many(hd)
  
      samples_mean = np.abs(np.mean(samples))
      assert samples_mean < 1.0
      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
  
      assert hd.min() == low
      assert hd.max() == high
      assert abs(hd.mean() - (10 - 10) / 2) < 1e-6
      assert abs(hd.var() - 1 / 12 * (high - low) ** 2) < 1e-6
      assert (
          abs(hd.std() - math.sqrt(1 / 12 * (high - low) ** 2)) < 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 8 for calling a global function with example usage: test_uniform() without return types