code: def test_sample(self):
      # check shape is [BATCH_SIZE, NUM_ACTIOINS]
      sample_actions = self.dist.sample()
      self.assertTrue(
          len(sample_actions.shape) == 2
          and sample_actions.shape[0] == self.batch_size
          and sample_actions.shape[1] == self.num_actions
      )
  
      # test with IO
      mean = paddle.zeros(shape=(self.batch_size, self.num_actions))
      log_std = paddle.ones(shape=(self.batch_size, self.num_actions))
      logits = (mean, log_std)
      dist = DiagGaussianDistribution(logits)
      set_random_seed(12)
      # the standard gaussian variable sampled is fixed when the seed is given
      randn_fixed = paddle.randn(shape=mean.shape)
      set_random_seed(12)
      gaussian_sample = dist.sample()
      fixed_gaussian = randn_fixed * dist.std + dist.mean
      self.assertTrue(
          self.paddle_check_eq(fixed_gaussian, gaussian_sample)
      )
  
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 4 for calling the DiagGaussianDistributionTest obj's underlying member method code with example usage: obj.test_sample() without return types