code: def __init__(
      self,
      model: torch.nn.Module,
      config_list: List[Dict],
      evaluator: Evaluator,
      teacher_model: torch.nn.Module,
      teacher_predict: Callable[[Any, torch.nn.Module], torch.Tensor],
      origin_loss_lambda: float = 1.0,
      existed_wrappers: Dict[str, ModuleWrapper] | None = None,
  ):
      assert (
          model is not teacher_model
      ), "Student model and teacher model should not be the same."
      super().__init__(
          model=model,
          config_list=config_list,
          evaluator=evaluator,
          existed_wrappers=existed_wrappers,
      )
  
      self.teacher_model = teacher_model
      self.teacher_predict = teacher_predict
      self.origin_loss_lambda = origin_loss_lambda
      self._set_default_link()
      self._set_default_lambda()
      (
          self._teacher_module_wrappers,
          target_spaces,
      ) = self._register_teacher_wrappers()
      self._teacher_target_spaces: _DISTILLATION_TARGET_SPACES = target_spaces  # type: ignore
      self._teacher_is_wrapped = False
      self.wrap_teacher_model()
  
imports: from __future__ import annotations
  from collections import defaultdict
  import logging
  from typing import Any, Callable, Dict, List, overload
  import torch
  import torch.nn.functional
  from torch.utils._pytree import tree_map
  from ..base.compressor import Compressor, Distiller, _DISTILLATION_TARGET_SPACES
  from ..base.wrapper import ModuleWrapper, register_wrappers
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 3 to create a new TeacherModelBasedDistiller object from inherited base classes: Distiller with example: obj = TeacherModelBasedDistiller(model, config_list, evaluator, teacher_model, teacher_predict, origin_loss_lambda, existed_wrappers)