code: def register_scale(self):
      for module_name, ts in self._target_spaces.items():
          wrapper = self._module_wrappers[module_name]
          for target_name, _ in ts.items():
              if hasattr(wrapper, f"{target_name}_scale"):
                  delattr(wrapper, f"{target_name}_scale")
              # for deepspeed
              try:
                  device = next(wrapper.parameters()).device
              except StopIteration:
                  try:
                      device = next(wrapper.buffers()).device
                  except StopIteration:
                      # NOTE: this will have risk in model parallel
                      device = next(
                          self.bound_model.parameters()
                      ).device
              param = torch.nn.Parameter(
                  torch.Tensor([0.01]).to(device)
              )
              wrapper.register_parameter(f"{target_name}_scale", param)
  
imports: from __future__ import annotations
  import logging
  from typing import List, Dict, overload
  import torch
  from torch import Tensor
  from ..base.compressor import Compressor, Quantizer
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
  from ..base.target_space import TargetType
label_desc: Use image node_id 9 for calling the LsqQuantizer obj's underlying member method code with example usage: obj.register_scale() without return types