class_docstr: Vectorized Coin Game environment.
code: def step(self, actions: Iterable):
      actions = self._from_RLlib_API_to_list(actions)
      self.step_count_in_current_episode += 1
  
      (
          self.red_pos,
          self.blue_pos,
          rewards,
          self.coin_pos,
          observation,
          self.red_coin,
          red_pick_any,
          red_pick_red,
          blue_pick_any,
          blue_pick_blue,
      ) = vectorized_step_wt_numba_optimization(
          actions,
          self.batch_size,
          self.red_pos,
          self.blue_pos,
          self.coin_pos,
          self.red_coin,
          self.grid_size,
          self.asymmetric,
          self.max_steps,
          self.both_players_can_pick_the_same_coin,
      )
  
      if self.output_additional_info:
          self._accumulate_info(
              red_pick_any, red_pick_red, blue_pick_any, blue_pick_blue
          )
  
      obs = self._get_obs_invariant_to_the_player_trained(observation)
  
      obs, rewards = self._optional_unvectorize(obs, rewards)
  
      return self._to_RLlib_API(obs, rewards)
  
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 7 for calling the VectorizedCoinGame obj's underlying member method code with example usage: obj.step(actions) and returns: self