code: def _register_trans_linear(self):
      self.trans_linears = defaultdict(dict)
      for module_name, ts in self._target_spaces.items():
          for target_name, target_space in ts.items():
              # For performance reasons only one link is supported...
              assert (
                  isinstance(target_space.link, str)
                  or len(target_space.link) == 1
              ), f"only support set one link for target in {self.__class__.__name__}"
              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
              assert (
                  stu_hs is not None and tea_hs is not None
              ), "Please run AdaptiveShapeLayerwiseDistiller.track_forward(...) first before compress."
              if stu_hs.shape[-1] == tea_hs.shape[-1]:
                  self.trans_linears[module_name][target_name] = None
              else:
                  self.trans_linears[module_name][
                      target_name
                  ] = torch.nn.Linear(
                      stu_hs.shape[-1], tea_hs.shape[-1]
                  ).to(
                      stu_hs.device
                  )
  
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 2 for calling the Adaptive1dLayerwiseDistiller obj's underlying member method code with example usage: obj._register_trans_linear() without return types