code: def _register_trigger(self, evaluator: Evaluator):
      self._current_step = 0
      self._is_first_step = True
      self._remaining_times = self.total_times
  
      def optimizer_task():
          self._current_step += 1
          if (
              self._is_first_step and self.first_step_gen
          ) or self._current_step == self.interval_steps:
              masks = self.generate_masks()
              self.update_masks(masks)
              if isinstance(self._remaining_times, int):
                  self._remaining_times -= 1
              debug_msg = f"{self.__class__.__name__} generate masks, remaining times {self._remaining_times}"
              _logger.debug(debug_msg)
          if self._current_step == self.interval_steps and (
              self._remaining_times == "unlimited"
              or self._remaining_times > 0
          ):  # type: ignore
              self._current_step = 0
          if self._is_first_step:
              self._is_first_step = False
  
      evaluator.patch_optimizer_step(
          before_step_tasks=[], after_step_tasks=[optimizer_task]
      )
  
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 8 for calling the _NormPruner obj's underlying member method code with example usage: obj._register_trigger(evaluator) without return types