code: def generate_observations_wt_numba_optimization(
      batch_size, red_pos, blue_pos, coin_pos, red_coin, grid_size
  ):
      obs = np.zeros((batch_size, grid_size, grid_size, 4))
      for i in prange(batch_size):
          obs[i, red_pos[i][0], red_pos[i][1], 0] = 1
          obs[i, blue_pos[i][0], blue_pos[i][1], 1] = 1
          if red_coin[i]:
              obs[i, coin_pos[i][0], coin_pos[i][1], 2] = 1
          else:
              obs[i, coin_pos[i][0], coin_pos[i][1], 3] = 1
      return obs
  
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 8 for calling a global function with example usage: generate_observations_wt_numba_optimization(batch_size, red_pos, blue_pos, coin_pos, red_coin, grid_size) and returns: obs