code: def test_entropy(self):
      # check shape is [BATCHSIZE, ]
      entropy = self.dist.entropy()
      self.assertTrue(entropy.shape == (self.batch_size,))
  
      # test with IO
      # when the std is 1/sqrt(2*pi*e) the entropy output expect to be zero
      mean = torch.rand(size=(self.batch_size, self.num_actions))
      std = torch.ones(
          size=(self.batch_size, self.num_actions)
      ) * np.sqrt(1 / (2 * np.pi * np.e))
      logstd = torch.log(std)
      dist = self.get_dist(mean, logstd)
      entropy = dist.entropy()
      self.assertTrue(self.torch_check_eq(entropy, 0))
  
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 5 for calling the DiagGaussianDistributionTest obj's underlying member method code with example usage: obj.test_entropy() without return types