Gym pass Objects to Observation Space in Custom Enviorment


I want to create a custom enviorment where the agent learns to place small boxes into big boxes by certain criterias. The size and amount of the boxes will change after the model predicted the positions. So that I once can call my model to predict me the correct postions for a new given set of boxes.
I do not find any documentation/tutorial what so ever on how to create an observation space for this as I think I need to define my observation space as a List or Tuple of my elements.

I have to classes:

class BOXSmall:
    def __init__(self, points, id, r_angle, i_point, bb_id):
        self.points = points = id
        self.r_angle = r_angle
        self.i_point = i_point
        self.bb_id = bb_id

class BOXBig:
    def __init__(self, id, width, height): = id
        self.width = width
        self.height = height

And define them like this:

boxSmall1 = BOXSmall([(0, 0), (1, 1), (2, 2)], 1, 0, (0, 0), None)
boxSmall2 = BOXSmall([(3, 3), (4, 4), (5, 5)], 2, 0, (0, 0), None)
smallboxes = (boxSmall1, boxSmall2)
boxBig1 = BOXBig(1, 10, 10)
boxBig2 = BOXBig(2, 20, 20)
bigboxes = (boxBig1, boxBig2)

My custom enviroment looks like this:

class PlacmentEnv(gym.Env):
    def __init__(self,bs, bb):
        self.reward = None
        self.boxessmall = bs 
        self.boxesbig = bb

   self.observation_space = spaces.Tuple((
            spaces.Box(low=0, high=len(self.boxessmall), shape=(len(self.boxessmall),),),
            spaces.Box(low=0, high=len(self.boxesbig), shape=(len(self.boxesbig),),)

Sadly I always get the following error:

TypeError: float() argument must be a string or a number, not ‘BOXSmall’

Is there any way to pass this lists into an obersavtion space?

Thank you!

I tried to search the internet but without success.