code: def _patch_loss(self, evaluator: Evaluator):
      def loss_patch(original_loss, batch):
          reg_loss = torch.tensor(0.0, device=original_loss.device)
          count = 0
          for _, target_scaling_factor in self.scaling_factors.items():
              for _, scaling_factor in target_scaling_factor.items():
                  reg_loss = reg_loss + scaling_factor.norm(p=1)  # type: ignore
                  count += 1
          if count > 0:
              reg_loss = self.regular_scale * reg_loss / count
          return original_loss + reg_loss
  
      evaluator.patch_loss(loss_patch)
  
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 8 for calling the SlimPruner obj's underlying member method code with example usage: obj._patch_loss(evaluator) and returns: unknown