code: def test_normal():
      hd_mean = 0.0
      hd_std = 1.0
      hd = Normal(hd_mean, hd_std)
  
      samples = hd_rvs_many(hd)
  
      samples_mean = np.abs(np.mean(samples))
      assert samples_mean < 0.1
      samples_std = np.std(samples)
      assert 0.9 < samples_std < 1.1
      assert abs(hd.pdf(-1.0) - 0.24197072451914337) < 1e-6
      assert abs(hd.pdf(0.0) - 0.3989422804014327) < 1e-6
      assert abs(hd.pdf(1.0) - 0.24197072451914337) < 1e-6
      assert abs(hd.cdf(-1.0) - 0.15865525393145707) < 1e-6
      assert abs(hd.cdf(0.0) - 0.5) < 1e-6
      assert abs(hd.cdf(1.0) - 0.8413447460685429) < 1e-6
  
      assert hd.min() == -np.inf
      assert hd.max() == np.inf
      assert abs(hd.mean() - hd_mean) < 1e-6
      assert abs(hd.var() - hd_std**2) < 1e-6
      assert abs(hd.std() - hd_std) < 1e-6
      # Verify that hd mean and variance also correspond to mean and variance of sampling.
      assert abs(hd.mean() - np.mean(samples)) < 5e-2
      assert abs(hd.var() - np.var(samples)) < 5e-2
  
imports: import random
  from collections import Counter
  import numpy
  import pytest
  from neuraxle.hyperparams.distributions import *
label_desc: Use image node_id 10 for calling a global function with example usage: test_normal() without return types