code: def __init__(
      self,
      model: torch.nn.Module,
      config_list: List[Dict],
      evaluator: Evaluator | None = None,
      existed_wrappers: Dict[str, ModuleWrapper] | None = None,
  ):
      ...
  
imports: from __future__ import annotations
  import logging
  from typing import Dict, List, Literal, Tuple, overload
  import torch
  from .tools import _DATA, _METRICS, _MASKS, active_sparse_targets_filter, norm_metrics, fpgm_metrics, generate_sparsity
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import PruningTargetSpace
  from ..base.wrapper import ModuleWrapper
  from ..utils.evaluator import Evaluator
label_desc: Use image node_id 2 to create a new _NormPruner object from inherited base classes: Pruner with example: obj = _NormPruner(model, config_list, evaluator, existed_wrappers)