code: def _register_loss_patch(self, evaluator: Evaluator):
      def loss_patch(original_loss, batch):
          with torch.no_grad():
              self.teacher_predict(batch, self.teacher_model)
          return (
              self.origin_loss_lambda * original_loss
              + self.compute_distill_loss()
          )
  
      evaluator.patch_loss(loss_patch)
  
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 10 for calling the TeacherModelBasedDistiller obj's underlying member method code with example usage: obj._register_loss_patch(evaluator) and returns: unknown