code: def __init__(
      self,
      model: torch.nn.Module,
      config_list: List[Dict],
      evaluator: Evaluator | None = None,
      existed_wrappers: Dict[str, ModuleWrapper] | None = None,
  ):
      super().__init__(
          model=model,
          config_list=config_list,
          evaluator=evaluator,
          existed_wrappers=existed_wrappers,
      )
      self._target_spaces: _DISTILLATION_TARGET_SPACES
  
imports: from __future__ import annotations
  from collections import defaultdict
  from copy import deepcopy
  import logging
  from typing import Any, Dict, List, Literal, Callable
  import torch
  from torch.utils._pytree import tree_map
  from .config import trans_legacy_config_list, default_config_schema
  from .target_space import TargetSpace, TargetType, DistillationTargetSpace, PruningTargetSpace, QuantizationTargetSpace
  from .wrapper import ModuleWrapper, register_wrappers
  from ..utils import Evaluator, Scaling, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 1 to create a new Distiller object from inherited base classes: Compressor with example: obj = Distiller(model, config_list, evaluator, existed_wrappers)