code: def _register_factors_optimization(self, evaluator: Evaluator):
      scaling_factors = []
      for _, target_scaling_factor in self.scaling_factors.items():
          for _, scaling_factor in target_scaling_factor.items():
              scaling_factors.append(scaling_factor)
  
      if not scaling_factors:
          return
  
      params = [{"params": scaling_factors}]
      optimizer = Adam(params, 1e-2)
  
      evaluator.patch_optimizer_step(
          before_step_tasks=[optimizer.step],
          after_step_tasks=[optimizer.zero_grad],
      )
  
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 7 for calling the SlimPruner obj's underlying member method code with example usage: obj._register_factors_optimization(evaluator) without return types