code: def test_entropy(self):
      # check shape is [BATCHSIZE, ]
      entropy = self.dist.entropy()
      self.assertTrue(
          len(entropy.shape) == 1
          and entropy.shape[0] == self.batch_size
      )
  
      # uniform distribution should output the maximum entropy, i.e, ln(num_action)
      uniform_logits = paddle.ones(
          shape=(self.batch_size, self.num_actions)
      )
      entropy_out = self.get_dist(uniform_logits).entropy()
      entropy_exp = np.log(self.num_actions)
      entropy_exp = paddle.ones_like(entropy_out) * entropy_exp
      self.assertTrue(self.paddle_check_eq(entropy_exp, entropy_out))
  
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 CategoricalDistributionTest obj's underlying member method code with example usage: obj.test_entropy() without return types