code: def test_observations_are_invariant_to_the_player_trained_in_reset():
      p_red_pos = [
          [0, 0],
          [0, 0],
          [1, 1],
          [1, 1],
          [0, 0],
          [1, 1],
          [2, 0],
          [0, 1],
          [2, 2],
          [1, 2],
      ]
      p_blue_pos = [
          [0, 0],
          [0, 0],
          [1, 1],
          [1, 1],
          [1, 1],
          [0, 0],
          [0, 1],
          [2, 0],
          [1, 2],
          [2, 2],
      ]
      p_red_act = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
      p_blue_act = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
      c_red_pos = [
          [1, 1],
          None,
          [0, 1],
          None,
          None,
          [2, 2],
          [0, 0],
          None,
          None,
          [2, 1],
      ]
      c_blue_pos = [
          None,
          [1, 1],
          None,
          [0, 1],
          [2, 2],
          None,
          None,
          [0, 0],
          [2, 1],
          None,
      ]
      max_steps, grid_size = 10, 3
      n_steps = max_steps
      envs = init_several_env(max_steps, grid_size)
  
      for env_i, env in enumerate(envs):
          _ = env.reset()
  
          step_i = 0
          overwrite_pos(
              env,
              p_red_pos[step_i],
              p_blue_pos[step_i],
              c_red_pos[step_i],
              c_blue_pos[step_i],
          )
  
          for _ in range(n_steps):
              step_i += 1
              actions = {
                  "player_red": p_red_act[step_i - 1],
                  "player_blue": p_blue_act[step_i - 1],
              }
              _, _, _, _, _ = env.step(actions)
  
              if step_i == max_steps:
                  break
  
              overwrite_pos(
                  env,
                  p_red_pos[step_i],
                  p_blue_pos[step_i],
                  c_red_pos[step_i],
                  c_blue_pos[step_i],
              )
  
imports: import random
  import numpy
  from ray.rllib.examples.env.coin_game_non_vectorized_env import CoinGame, AsymCoinGame
label_desc: Use image node_id 22 for calling a global function with example usage: test_observations_are_invariant_to_the_player_trained_in_reset() without return types