code: def forward(self, *inputs: torch.Tensor):
      inputs = (self.quant(x) for x in inputs)
      outputs = self.model(*inputs)
      return tuple(self.dequant(x) for x in outputs)
  
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 2 for calling the _QuantWrapper obj's underlying member method code with example usage: obj.forward() and returns: tuple