code: def vectorized_step_wt_numba_optimization(
      actions,
      batch_size,
      red_pos,
      blue_pos,
      coin_pos,
      red_coin,
      grid_size: int,
      asymmetric: bool,
      max_steps: int,
      both_players_can_pick_the_same_coin: bool,
  ):
      red_pos, blue_pos = move_players(
          batch_size, actions, red_pos, blue_pos, grid_size
      )
  
      (
          reward,
          generate,
          red_pick_any,
          red_pick_red,
          blue_pick_any,
          blue_pick_blue,
      ) = compute_reward(
          batch_size,
          red_pos,
          blue_pos,
          coin_pos,
          red_coin,
          asymmetric,
          both_players_can_pick_the_same_coin,
      )
  
      coin_pos = generate_coin(
          batch_size,
          generate,
          red_coin,
          red_pos,
          blue_pos,
          coin_pos,
          grid_size,
      )
  
      obs = generate_observations_wt_numba_optimization(
          batch_size, red_pos, blue_pos, coin_pos, red_coin, grid_size
      )
  
      return (
          red_pos,
          blue_pos,
          reward,
          coin_pos,
          obs,
          red_coin,
          red_pick_any,
          red_pick_red,
          blue_pick_any,
          blue_pick_blue,
      )
  
imports: import copy
  from collections import Iterable
  import numpy
  from numba import jit, prange
  from numba.typed import List
  from ray.rllib.examples.env.coin_game_non_vectorized_env import CoinGame
  from ray.rllib.utils import override
label_desc: Use image node_id 9 for calling a global function with example usage: vectorized_step_wt_numba_optimization(actions, batch_size, red_pos, blue_pos, coin_pos, red_coin, grid_size, asymmetric, max_steps, both_players_can_pick_the_same_coin) and returns: red_pos,  blue_pos,  reward,  coin_pos,  obs,  red_coin,  red_pick_any,  red_pick_red,  blue_pick_any,  blue_pick_blue