code: def __init__(self, model: Module):
      super(_QuantWrapper, self).__init__()
      qconfig = model.qconfig if hasattr(model, "qconfig") else None
      self.quant = QuantStub(qconfig)
      self.model = model
      self.dequant = DeQuantStub()
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/quantizations/pytorch.py
imports: import copy
  from typing import List, Tuple, Union
  from loguru import logger
  from nebullvm.core.models import DeviceType, Device, QuantizationType
  from nebullvm.optional_modules.torch import torch, Module, symbolic_trace, QuantStub, DeQuantStub, GraphModule, default_dynamic_qconfig, prepare_fx, convert_fx, ScriptModule
  from nebullvm.tools.transformations import MultiStageTransformation, HalfPrecisionTransformation
  from nebullvm.tools.utils import check_module_version
label_desc: Use image node_id 1 to create a new _QuantWrapper object from inherited base classes: Module with example: obj = _QuantWrapper(model)