code: def __init__(self, slate_size: int) -> None:
      self.slate_size = slate_size
      self.action_space = gym.spaces.MultiDiscrete(
          [self.slate_size] * self.slate_size
      )
      self.observation_space = gym.spaces.Dict(
          {
              "user": gym.spaces.Box(low=0, high=1, shape=(1,)),
              "doc": gym.spaces.Dict(
                  {
                      str(k): gym.spaces.Box(
                          low=0, high=1, shape=(self.slate_size, 3)
                      )
                      for k in range(self.slate_size)
                  }
              ),
          }
      )
      self.response_space = gym.spaces.Dict({})
      self._doc_sampler = np.random.RandomState()
      self._reward_prng = np.random.RandomState()
  
imports: from collections import namedtuple
  from typing import List, Optional
  import gym
  import numpy
  from gym.utils import seeding
  from gym.wrappers.time_limit import TimeLimit
  from reagent.core.dataclasses import dataclass
  from reagent.gym.envs.env_wrapper import EnvWrapper
  from reagent.gym.envs.recsim import RecsimObsPreprocessor
  from reagent.gym.envs.wrappers.recsim import ValueWrapper
  from scipy.special import expit, logit
label_desc: Use image node_id 1 to create a new ToyVMEnv object from inherited base classes: gym with example: obj = ToyVMEnv(slate_size)