code: def compress(self, max_steps: int | None, max_epochs: int | None):
      if max_steps is not None:
          assert max_steps >= self.total_times * self.interval_steps
      else:
          warn_msg = (
              f"Using epochs number as training duration, "
              + "please make sure the total training steps larger than total_times * interval_steps."
          )
          _logger.warning(warn_msg)
      return super().compress(max_steps, max_epochs)
  
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 8 for calling the _ComboPruner obj's underlying member method code with example usage: obj.compress(max_steps, max_epochs) and returns: super