class_docstr: Env in which the initial observation has to be repeated all the time.
  
  Runs for n steps.
  r=1 if action correct, -1 otherwise (max. R=100).
code: def step(self, action):
      if action == self.token:
          reward = 1
      else:
          reward = -1
      self.num_steps += 1
      done = truncated = self.num_steps >= self.episode_len
      return 0, reward, done, truncated, {}
  
file_path: ray/python/ray/rllib/examples/env/repeat_initial_obs_env.py
imports: import gymnasium
  from gymnasium.spaces import Discrete
  import random
label_desc: Use image node_id 3 for calling the RepeatInitialObsEnv obj's underlying member method code with example usage: obj.step(action) and returns: int,  reward,  done,  truncated,  dict