code: def test_probtypes():
      """
      test probability distribution types
      """
      np.random.seed(0)
  
      pdparam_diag_gauss = np.array(
          [-0.2, 0.3, 0.4, -0.5, 0.1, -0.5, 0.1, 0.8]
      )
      diag_gauss = DiagGaussianProbabilityDistributionType(
          pdparam_diag_gauss.size // 2
      )
      validate_probtype(diag_gauss, pdparam_diag_gauss)
  
      pdparam_categorical = np.array([-0.2, 0.3, 0.5])
      categorical = CategoricalProbabilityDistributionType(
          pdparam_categorical.size
      )
      validate_probtype(categorical, pdparam_categorical)
  
      nvec = np.array([1, 2, 3])
      pdparam_multicategorical = np.array(
          [-0.2, 0.3, 0.5, 0.1, 1, -0.1]
      )
      multicategorical = MultiCategoricalProbabilityDistributionType(
          nvec
      )
      validate_probtype(multicategorical, pdparam_multicategorical)
  
      pdparam_bernoulli = np.array([-0.2, 0.3, 0.5])
      bernoulli = BernoulliProbabilityDistributionType(
          pdparam_bernoulli.size
      )
      validate_probtype(bernoulli, pdparam_bernoulli)
  
imports: import numpy
  import tensorflow
  import stable_baselines.common.tf_util
  from stable_baselines.common.distributions import DiagGaussianProbabilityDistributionType, CategoricalProbabilityDistributionType, MultiCategoricalProbabilityDistributionType, BernoulliProbabilityDistributionType
label_desc: Use image node_id 1 for calling a global function with example usage: test_probtypes() without return types