code: def _register_scaling_facotrs(self):
      self.scaling_factors = defaultdict(dict)
      for module_name, ts in self._target_spaces.items():
          for target_name, target_space in ts.items():
              if is_active_target(target_space):
                  # TODO: add input / output
                  if target_space.type is TargetType.PARAMETER:
                      # TODO: here using a shrinked score to save memory, but need to test the speed.
                      scaling_factor = torch.ones_like(target_space.target)  # type: ignore
                      if target_space._scaler is not None:
                          scaling_factor = target_space._scaler.shrink(
                              scaling_factor, keepdim=True
                          )
                      target_space._wrapper.register_parameter(
                          SLIM_SCALING_FACTOR_PNAME.format(target_name),
                          torch.nn.Parameter(scaling_factor),
                      )
                      scaling_factor = target_space._get_wrapper_attr(
                          SLIM_SCALING_FACTOR_PNAME.format(target_name)
                      )
                      self.scaling_factors[module_name][
                          target_name
                      ] = scaling_factor
                  else:
                      raise NotImplementedError()
  
imports: from __future__ import annotations
  from collections import defaultdict
  import logging
  from typing import Dict, List, Literal, Tuple, overload
  import torch
  from torch.optim import Adam
  from .tools import _METRICS, _MASKS, generate_sparsity, is_active_target
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import TargetType
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 6 for calling the SlimPruner obj's underlying member method code with example usage: obj._register_scaling_facotrs() without return types