code: def test_logp(self):
      logp = self.dist.logp(actions=self.dist.sample())
      # check shape is [BATCHSIZE, ]
      self.assertTrue(logp.shape == (self.batch_size,))
      # range check of logp, the maximum log of probability should be smaller than zero
      self.assertTrue(torch.max(logp) <= 0)
  
      # test with IO
      # when we sample the action that exactly is mean, the logp of each dimension should be (-ln(std)-0.5*ln(2pi))
      logp_output = self.dist.logp(actions=self.dist.mean)
      logp_expect = torch.sum(
          -self.dist.logstd - 0.5 * np.log(2 * np.pi), dim=-1
      )
      self.assertTrue(self.torch_check_eq(logp_output, logp_expect))
  
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 6 for calling the DiagGaussianDistributionTest obj's underlying member method code with example usage: obj.test_logp() without return types