class_docstr: Simple env in which the policy has to emit a tuple of equal actions.
  
  In each step, the agent observes a random number (0 or 1) and has to choose
  two actions a1 and a2.
  It gets +5 reward for matching a1 to the random obs and +5 for matching a2
  to a1. I.e., +10 at most per step.
  
  One way to effectively learn this is through correlated action
  distributions, e.g., in examples/autoregressive_action_dist.py
  
  There are 20 steps. Hence, the best score would be ~200 reward.
code: def __init__(self, _):
      self.observation_space = Discrete(2)
      self.action_space = Tuple([Discrete(2), Discrete(2)])
      self.last_observation = None
  
file_path: ray/python/ray/rllib/examples/env/correlated_actions_env.py
imports: import gymnasium
  from gymnasium.spaces import Discrete, Tuple
  import random
label_desc: Use image node_id 1 to create a new CorrelatedActionsEnv object from inherited base classes: gym with example: obj = CorrelatedActionsEnv(_)