class_docstr: Cutting 2d environment state to dictionary observation.
  
  :param max_pieces_in_inventory: Size of the inventory. If inventory gets full, the oldest pieces get discarded.
  :param raw_piece_size: Size of a fresh raw (= full-size) piece
class_docstr_tok: ["Cutting","2d","environment","state","to","dictionary","observation",".",":","param","max_pieces_in_inventory",":","Size","of","the","inventory",".","If","inventory","gets","full",",","the","oldest","pieces","get","discarded",".",":","param","raw_piece_size",":","Size","of","a","fresh","raw","(","=","full-size",")","piece"]
docstr: Converts agent observation to core environment state (not required for this example).
docstr_tok: ["Converts","agent","observation","to","core","environment","state","(","not","required","for","this","example",")","."]
code: def space_to_maze(
      self, observation: Dict[str, np.ndarray]
  ) -> Cutting2DMazeState:
      """Converts agent observation to core environment state (not required for this example)."""
      raise NotImplementedError
  
file_path: maze/tutorials/tutorial_maze_env/part05_reward/space_interfaces/dict_observation_conversion.py
imports: import numpy
  from typing import Dict
  from gym import spaces
  from maze.core.annotations import override
  from maze.core.env.observation_conversion import ObservationConversionInterface
  from ..env.maze_state import Cutting2DMazeState
label_desc: Use image node_id 3 for calling the ObservationConversion obj's underlying member method code with example usage: obj.space_to_maze(observation) without return types