code: def compute_distill_loss(self):
      distill_loss = 0
      for module_name, ts in self._target_spaces.items():
          for target_name, target_space in ts.items():
              stu_hs = target_space.hidden_state
              link = (
                  target_space.link
                  if isinstance(target_space.link, str)
                  else target_space.link[0]
              )
              tea_hs = self._teacher_target_spaces[link][
                  target_name
              ].hidden_state
              if stu_hs is not None and tea_hs is not None:
                  if (
                      self.trans_linears[module_name][target_name]
                      is not None
                  ):
                      self.trans_linears[module_name][target_name].to(
                          stu_hs.device
                      )
                      stu_hs = self.trans_linears[module_name][
                          target_name
                      ](stu_hs)
                  tea_hs = tea_hs.to(stu_hs.device)
                  if target_space.apply_method == "mse":
                      distill_loss += target_space.lambda_ * F.mse_loss(
                          stu_hs, tea_hs
                      )
                  elif target_space.apply_method == "kl":
                      distill_loss += (
                          target_space.lambda_
                          * F.kl_div(
                              (stu_hs / 2).log_softmax(dim=-1),
                              (tea_hs / 2).softmax(dim=-1),
                              reduction="batchmean",
                          )
                          * (2**2)
                      )
      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 4 for calling the Adaptive1dLayerwiseDistiller obj's underlying member method code with example usage: obj.compute_distill_loss() and returns: distill_loss