code: def test_lop(self):
      sample_action = np.random.choice(
          a=range(self.num_actions), size=self.batch_size
      )
      sample_action = paddle.to_tensor(sample_action)
      logp = self.dist.logp(sample_action)
      # check shape is [BATCHSIZE, ]
      self.assertEqual(
          logp.shape,
          [
              self.batch_size,
          ],
      )
  
      # the logp output of the uniform distribution should be log(1/num_actions)
      uniform_logits = paddle.ones(
          shape=(self.batch_size, self.num_actions)
      )
      dist = self.get_dist(uniform_logits)
      act_smp = dist.sample()
      logp_out = dist.logp(act_smp)
      exp_out = np.log(1 / self.num_actions) + 1e-6
      exp_out = paddle.ones_like(logp_out) * exp_out
      self.assertTrue(self.paddle_check_eq(logp_out, exp_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 6 for calling the CategoricalDistributionTest obj's underlying member method code with example usage: obj.test_lop() without return types