code: def compute_distill_loss(self):
      distill_loss = 0
      for _, ts in self._target_spaces.items():
          for target_name, target_space in ts.items():
              stu_hs = target_space.hidden_state
              loss_list = []
              for link in target_space.link:
                  teacher_target_space = self._teacher_target_spaces[
                      link
                  ][target_name]
                  tea_hs = teacher_target_space.hidden_state
                  if stu_hs is not None and tea_hs is not None:
                      tea_hs = tea_hs.to(stu_hs.device)
                      if target_space.apply_method == "mse":
                          loss_list.append(
                              target_space.lambda_
                              * F.mse_loss(stu_hs, tea_hs)
                          )
                      elif target_space.apply_method == "kl":
                          loss_list.append(
                              target_space.lambda_
                              * F.kl_div(
                                  (stu_hs / 2).log_softmax(dim=-1),
                                  (tea_hs / 2).softmax(dim=-1),
                                  reduction="batchmean",
                              )
                              * (2**2)
                          )
              if loss_list:
                  distill_loss = distill_loss + min(loss_list)
      for _, ts in self._target_spaces.items():
          for _, target_space in ts.items():
              target_space.clean()
      for _, ts in self._teacher_target_spaces.items():
          for _, target_space in ts.items():
              target_space.clean()
      return distill_loss
  
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 1 for calling the DynamicLayerwiseDistiller obj's underlying member method code with example usage: obj.compute_distill_loss() and returns: distill_loss