code: def from_compressor(
      cls,
      compressor: Compressor,
      new_config_list: List[Dict],
      teacher_model: torch.nn.Module,
      teacher_predict: Callable[[Any, torch.nn.Module], torch.Tensor],
      origin_loss_lambda: float = 1.0,
      evaluator: Evaluator | None = None,
  ):
      return super().from_compressor(
          compressor,
          new_config_list,
          evaluator=evaluator,
          teacher_model=teacher_model,
          teacher_predict=teacher_predict,
          origin_loss_lambda=origin_loss_lambda,
      )
  
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 4 for calling the TeacherModelBasedDistiller obj's underlying member method code with example usage: obj.from_compressor(cls, compressor, new_config_list, teacher_model, teacher_predict, origin_loss_lambda, evaluator) and returns: super