code: def _single_compress(self, max_steps: None, max_epochs: None):
      assert (
          max_steps is None and max_epochs is None
      ), f"{self.__class__.__name__} do not support training aware pruning under single compress mode."
      masks = self.generate_masks()
      self.update_masks(masks)
  
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 9 for calling the _NormPruner obj's underlying member method code with example usage: obj._single_compress(max_steps, max_epochs) without return types