code: def test_wrapped_sk_learn_distributions_should_be_able_to_use_sklearn_methods():
      wrapped_sklearn_distribution = Gaussian(
          min_included=0, max_included=10, null_default_value=0
      )
  
      assert np.isclose(
          wrapped_sklearn_distribution.logpdf(5), -13.418938533204672
      )
      assert np.isclose(
          wrapped_sklearn_distribution.logcdf(5), -0.6931477538632531
      )
      assert np.isclose(
          wrapped_sklearn_distribution.sf(5), 0.5000002866515718
      )
      assert np.isclose(
          wrapped_sklearn_distribution.logsf(5), -0.693146607256966
      )
      assert np.all(
          wrapped_sklearn_distribution.ppf(
              [0.0, 0.01, 0.05, 0.1, 1 - 0.10, 1 - 0.05, 1 - 0.01, 1.0],
              10,
          )
      )
      assert 8 < wrapped_sklearn_distribution.isf(q=0.5) > 8
      assert wrapped_sklearn_distribution.moment(2) > 50
      stats = wrapped_sklearn_distribution.stats()
      assert stats[0]
      assert stats[1]
      assert np.array_equal(
          wrapped_sklearn_distribution.entropy(),
          np.array(0.7094692666023363),
      )
      assert wrapped_sklearn_distribution.median()
      assert np.isclose(
          wrapped_sklearn_distribution.mean(), 5.398942280397029
      )
      assert np.isclose(
          wrapped_sklearn_distribution.std(), 4.620759921685375
      )
      assert np.isclose(
          wrapped_sklearn_distribution.var(), 21.351422253853833
      )
      assert np.isclose(
          wrapped_sklearn_distribution.expect(), 0.39894228040143276
      )
      interval = wrapped_sklearn_distribution.interval(
          alpha=[0.25, 0.50]
      )
      assert np.all(interval[0])
      assert np.all(interval[1])
      assert wrapped_sklearn_distribution.support() == (0, 10)
  
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 2 for calling a global function with example usage: test_wrapped_sk_learn_distributions_should_be_able_to_use_sklearn_methods() without return types