code: def get_dist(self, mean=None, logstd=None):
      if mean is None:
          mean = torch.rand(size=(self.batch_size, self.num_actions))
      if logstd is None:
          logstd = torch.rand(size=(self.batch_size, self.num_actions))
      logits = (mean, logstd)
      dist = DiagGaussianDistribution(logits)
      return dist
  
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 2 for calling the DiagGaussianDistributionTest obj's underlying member method code with example usage: obj.get_dist(mean, logstd) and returns: dist