code: def from_compressor(
      cls,
      compressor: Compressor,
      new_config_list: List[Dict],
      training_steps: int,
      regular_scale: float = 1.0,
      evaluator: Evaluator | None = None,
  ):
      return super().from_compressor(
          compressor,
          new_config_list,
          training_steps=training_steps,
          regular_scale=regular_scale,
          evaluator=evaluator,
      )
  
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 4 for calling the SlimPruner obj's underlying member method code with example usage: obj.from_compressor(cls, compressor, new_config_list, training_steps, regular_scale, evaluator) and returns: super