code: def get_dict_distribution():
      """distribution test"""
      logits_0 = torch.from_numpy(np.random.randn(100, 3))
      logits_1 = torch.from_numpy(np.random.randn(100, 5))
  
      distribution_dict = {
          "action_0": CategoricalProbabilityDistribution(
              logits=logits_0,
              action_space=spaces.Discrete(9),
              temperature=1.0,
          ),
          "action_1": BernoulliProbabilityDistribution(
              logits=logits_1,
              action_space=spaces.MultiBinary(5),
              temperature=1.0,
          ),
      }
  
      return DictProbabilityDistribution(
          distribution_dict=distribution_dict
      )
  
file_path: maze/maze/test/distributions/test_dict.py
imports: import numpy
  import torch
  from gym import spaces
  from maze.distributions.bernoulli import BernoulliProbabilityDistribution
  from maze.distributions.categorical import CategoricalProbabilityDistribution
  from maze.distributions.dict import DictProbabilityDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: get_dict_distribution() and returns: DictProbabilityDistribution