code: def __init__(
      self,
      model: torch.nn.Module,
      config_list: List[Dict],
      evaluator: Evaluator,
      existed_wrappers: Dict[str, ModuleWrapper] | None = None,
      is_bias_correction: bool = False,
  ):
      super().__init__(model, config_list, evaluator, existed_wrappers)
      self.evaluator: Evaluator
      self.is_compressed = False
      self.is_bias_correction = is_bias_correction
      self.register_ptq_apply_method()
      self.register_track_func()
  
imports: from __future__ import annotations
  from typing import List, Dict, Union, overload
  import torch
  from torch import Tensor
  from ..base.compressor import Compressor, Quantizer
  from ..base.wrapper import ModuleWrapper
  from ..base.target_space import QuantizationTargetSpace
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 3 to create a new PtqQuantizer object from inherited base classes: Quantizer with example: obj = PtqQuantizer(model, config_list, evaluator, existed_wrappers, is_bias_correction)