code: def __init__(
      self,
      model: torch.nn.Module,
      config_list: List[Dict],
      evaluator: Evaluator | None = None,
      existed_wrappers: Dict[str, ModuleWrapper] | None = None,
  ):
      super().__init__(model, config_list, evaluator, existed_wrappers)
      self.evaluator: Evaluator
  
      self.sparse_goals: Dict[
          str, Dict[str, Dict[str, float]]
      ] = defaultdict(dict)
      self._goals_initialized = False
      self._scheduled_keys = [
          "sparse_ratio",
          "sparse_threshold",
          "max_sparse_ratio",
          "min_sparse_ratio",
      ]
  
imports: from __future__ import annotations
  from collections import defaultdict
  import logging
  from typing import Dict, List
  import torch
  from ..base.compressor import Pruner
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
  from .basic_pruner import LevelPruner, L1NormPruner, L2NormPruner, FPGMPruner
  from .slim_pruner import SlimPruner
  from .taylor_pruner import TaylorPruner
label_desc: Use image node_id 1 to create a new ScheduledPruner object from inherited base classes: Pruner with example: obj = ScheduledPruner(model, config_list, evaluator, existed_wrappers)