class_docstr: Implements a random structured policy.
  
  :param action_spaces_dict: The action_spaces dict of the env (will sample from it).
class_docstr_tok: ["Implements","a","random","structured","policy",".",":","param","action_spaces_dict",":","The","action_spaces","dict","of","the","env","(","will","sample","from","it",")","."]
docstr: Seed the policy by setting the action space seeds.
docstr_tok: ["Seed","the","policy","by","setting","the","action","space","seeds","."]
code: def seed(self, seed: int) -> None:
      """Seed the policy by setting the action space seeds."""
      rng = np.random.RandomState(seed)
      for key, action_space in self.action_spaces_dict.items():
          action_space.seed(
              MazeSeeding.generate_seed_from_random_state(rng)
          )
      pass
  
imports: from typing import Union, Dict, Tuple, Sequence, Optional
  import numpy
  from gym import spaces
  from omegaconf import DictConfig
  from maze.core.agent.policy import Policy
  from maze.core.annotations import override
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.base_env import BaseEnv
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.observation_conversion import ObservationType
  from maze.core.env.structured_env import ActorID
  from maze.core.utils.config_utils import make_env
  from maze.core.utils.seeding import MazeSeeding
  from maze.core.wrappers.observation_normalization.observation_normalization_wrapper import ObservationNormalizationWrapper
  from maze.train.utils.train_utils import stack_numpy_dict_list
  from maze.utils.process import query_cpu
label_desc: Use image node_id 2 for calling the RandomPolicy obj's underlying member method code with example usage: obj.seed(seed) without return types