code: def _test_loguniform(hd: ScipyLogUniform):
      samples = hd.rvs_many(size=100)
      samples_mean = np.mean(samples)
      assert samples_mean < 1.5
      assert min(samples) >= 0.001
      assert max(samples) <= 10.0
      assert hd.pdf(0.0001) == 0.0
      assert np.isclose(hd.pdf(2), 0.054286810237906484)
      assert hd.pdf(10.1) == 0.0
      assert hd.cdf(0.0001) == 0.0
      assert (
          abs(
              hd.cdf(2)
              - (math.log2(2) - math.log2(0.001))
              / (math.log2(10) - math.log2(0.001))
          )
          < 1e-6
      )
      assert hd.cdf(10.1) == 1.0
  
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 14 for calling a global function with example usage: _test_loguniform(hd) without return types