code: def get_dist(self, logits=None):
      if logits is None:
          logits = torch.rand(size=(self.batch_size, self.num_actions))
      dist = CategoricalDistribution(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 CategoricalDistributionTest obj's underlying member method code with example usage: obj.get_dist(logits) and returns: dist