def METHOD_NAME(pathfinding):
    def find_first_index(array_: np.ndarray, value_: float):
        indices = (array_ == value_).nonzero()
        return [x[0] for x in indices]
    env = BotBowlEnv(EnvConf(pathfinding=pathfinding))
    for _ in range(2):
        done = False
        spatial_obs, non_spatial_obs, mask = env.reset()
        while not done:
            # Spatial observation are within [0, 1]
            for layer, array in zip(env.env_conf.layers, spatial_obs):
                layer_name = layer.name()
                #array = layer.produce(env.game)
                max_val = np.max(array)
                min_val = np.min(array)
                assert max_val <= 1.0, \
                    f"['{layer_name}'][{find_first_index(array, max_val)}] is too high ({max_val})"
                assert min_val >= 0.0, \
                    f"['{layer_name}'][{find_first_index(array, min_val)}] is too low ({min_val})"
            max_val = np.max(non_spatial_obs)
            min_val = np.min(non_spatial_obs)
            assert min_val >= 0.0, \
                f"non_spatial_obs[{find_first_index(non_spatial_obs, min_val)}] is too low ({min_val})"
            assert max_val <= 1.0, \
                f"non_spatial_obs[{find_first_index(non_spatial_obs, max_val)}] is too high ({max_val})"
            aa = np.where(mask)[0]
            action_idx = np.random.choice(aa, 1)[0]
            (spatial_obs, non_spatial_obs, mask), reward, done, _ = env.step(action_idx)
    env.close()