code: def register_qat_apply_method(self):
      if self.current_step < self.quant_start_step:
          for _, ts in self._target_spaces.items():
              for _, target_space in ts.items():
                  target_space.apply_method = "bypass"
      elif self.current_step == self.quant_start_step:
          for _, ts in self._target_spaces.items():
              for _, target_space in ts.items():
                  target_space.apply_method = "qat_clamp_round"
      else:
          pass
  
imports: from __future__ import annotations
  from typing import Dict, List, overload
  import torch
  from torch import Tensor
  from ..base.compressor import Compressor, Quantizer
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 5 for calling the QATQuantizer obj's underlying member method code with example usage: obj.register_qat_apply_method() without return types