code: def __init__(self, num_action_vec: List[int]) -> None:
      self.num_action_vec = num_action_vec
      self.dists = [
          torch.distributions.Categorical(torch.ones(n) / n)
          for n in self.num_action_vec
      ]
  
imports: from typing import List, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.scorers.discrete_scorer import apply_possible_actions_mask
label_desc: Use image node_id 1 to create a new MultiDiscreteRandomPolicy object from inherited base classes: Policy with example: obj = MultiDiscreteRandomPolicy(num_action_vec)