code: def test_lognormal(seed):
      np.random.seed(seed)
  
      log2_space_mean = 0.0
      log2_space_std = 2.0
      hd = LogNormal(log2_space_mean, log2_space_std)
  
      samples = hd_rvs_many(hd)
  
      samples_median = np.median(samples)
      assert 0.9 < samples_median < 1.1
      samples_std = np.std(samples)
      assert 5 < samples_std < 8
      assert hd.pdf(0.0) == 0.0
      assert abs(hd.pdf(1.0) - 0.28777602476804065) < 1e-6
      assert abs(hd.pdf(5.0) - 0.029336304593386688) < 1e-6
      assert hd.cdf(0.0) == 0.0
      assert hd.cdf(1.0) == 0.5
      assert abs(hd.cdf(5.0) - 0.8771717397015799) < 1e-6
  
      assert hd.min() == 0
      assert hd.max() == np.inf
      assert abs(hd.mean() - 2.614063815405198) < 1e-6
      assert abs(hd.var() - 39.86106421503915) < 1e-6
      assert abs(hd.std() - 6.313561927710787) < 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()) < 2.5e-1
  
imports: import random
  from collections import Counter
  import numpy
  import pytest
  from neuraxle.hyperparams.distributions import *
label_desc: Use image node_id 14 for calling a global function with example usage: test_lognormal(seed) without return types