class_docstr: A mock CartPole environment.
  
  Gives 2 additional observation states and takes 2 discrete actions.
class_docstr_tok: ["A","mock","CartPole","environment",".","Gives","2","additional","observation","states","and","takes","2","discrete","actions","."]
code: def __init__(self):
      self._env = gym.make("CartPole-v1")
      self.observation_space = gym.spaces.Box(
          low=-10, high=10, shape=(6,)
      )
      self.action_space = gym.spaces.MultiDiscrete(nvec=[2, 2])
  
imports: import gymnasium
  import numpy
  import os
  import tempfile
  from typing import Dict
  from ray.rllib.connectors.connector import ConnectorContext
  from ray.rllib.connectors.action.lambdas import register_lambda_action_connector
  from ray.rllib.connectors.agent.lambdas import register_lambda_agent_connector
  from ray.rllib.examples.connectors.prepare_checkpoint import create_appo_cartpole_checkpoint
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.policy import local_policy_inference
  from ray.rllib.utils.typing import PolicyOutputType, StateBatches, TensorStructType
label_desc: Use image node_id 1 to create a new MyCartPole object from inherited base classes: gym with example: obj = MyCartPole()