code: def test_kl(self):
      # check shape is [BATCHSIZE, ]
      dist2 = self.get_dist()
      kl = self.dist.kl(dist2)
      self.assertTrue(
          len(kl.shape) == 1 and kl.shape[0] == self.batch_size
      )
  
      # kl of the same distribution should be zero
      same_dist = copy.deepcopy(self.dist)
      kl = self.dist.kl(same_dist)
      self.assertTrue(torch.count_nonzero(kl) == 0)
  
      # test with IO
      # two dist that share same mean but different std which satisfy std1 = 0.5 * std2
      # the kl expect to be ln2-(3/8)*num_actions
      mean = torch.rand(size=(self.batch_size, self.num_actions))
      std2 = torch.rand(size=(self.batch_size, self.num_actions))
      std1 = 0.5 * std2
      dist1 = self.get_dist(mean=mean, logstd=torch.log(std1))
      dist2 = self.get_dist(mean=mean, logstd=torch.log(std2))
      kl = dist1.kl(dist2)
      single_kl_expect = np.log(2) - (3 / 8)
      expect_output = self.num_actions * single_kl_expect
      self.assertTrue(self.torch_check_eq(expect_output, kl))
  
imports: import random
  import copy
  import numpy
  import torch
  import unittest
  from parl.core.torch.policy_distribution import DiagGaussianDistribution, CategoricalDistribution, SoftCategoricalDistribution, SoftMultiCategoricalDistribution
label_desc: Use image node_id 7 for calling the DiagGaussianDistributionTest obj's underlying member method code with example usage: obj.test_kl() without return types