class_docstr: Simple Env that yields a ts counter as observation (0-based).
  
  Actions have no effect.
  The episode length is always 15.
  Reward is always: current ts % 3.
code: def step(self, action):
      self.i += 1
      terminated = False
      truncated = self.i >= 15 + self.start_at_t
      return (
          self._get_obs(),
          float(self.i % 3),
          terminated,
          truncated,
          {},
      )
  
file_path: ray/python/ray/rllib/examples/env/debug_counter_env.py
imports: import gymnasium
  import numpy
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
label_desc: Use image node_id 3 for calling the DebugCounterEnv obj's underlying member method code with example usage: obj.step(action) and returns: self,  float,  terminated,  truncated,  dict