code: def test_entropy(self):
      # check shape is [BATCHSIZE, ]
      entropy = self.dist.entropy()
      self.assertEqual(
          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 = paddle.rand(shape=(self.batch_size, self.num_actions))
      std = paddle.ones(
          shape=(self.batch_size, self.num_actions)
      ) * np.sqrt(1 / (2 * np.pi * np.e))
      log_std = paddle.log(std)
      dist = self.get_dist(mean, log_std)
      entropy = dist.entropy()
      self.assertTrue(
          self.paddle_check_eq(
              entropy, paddle.zeros_like(entropy, dtype="float32")
          )
      )
  
imports: import random
  import numpy
  import paddle
  import unittest
  import copy
  from parl.core.paddle.policy_distribution import DiagGaussianDistribution, CategoricalDistribution, SoftCategoricalDistribution
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