code: def _test_normal(hd):
      samples = hd.rvs_many(size=100)
      samples_mean = np.abs(np.mean(samples))
      assert 0.6 > samples_mean > 0.4
      samples_std = np.std(samples)
      assert 0.1 < samples_std < 0.6
      assert np.isclose(abs(hd.pdf(-1.0) - 0.24), 0.24)
      assert np.isclose(abs(hd.pdf(0.0) - 0.40), 0.31125636093539194)
      assert np.isclose(abs(hd.pdf(1.0)), 0.08874363906460808)
      assert np.isclose(abs(hd.cdf(-1.0) - 0.15), 0.15)
      assert np.isclose(abs(hd.cdf(+0.0) - 0.5), 0.5)
      assert np.isclose(abs(hd.cdf(+1.0) - 0.85), 0.15000000000000002)
  
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 16 for calling a global function with example usage: _test_normal(hd) without return types