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 step(self, actions):
      # Take the first action.
      action = actions[0]
      obs, reward, done, truncated, info = self._env.step(action)
      # Fake additional data points to the obs.
      obs = np.hstack((obs, [8.0, 6.0]))
      return obs, reward, done, truncated, info
  
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 2 for calling the MyCartPole obj's underlying member method code with example usage: obj.step(actions) and returns: obs,  reward,  done,  truncated,  info