code: def forward(self, x: Tensor) -> Tensor:
      x = self.quant(x)
      # Ensure scriptability
      # super(QuantizableResNet,self).forward(x)
      # is not scriptable
      x = self._forward_impl(x)
      x = self.dequant(x)
      return x
  
imports: from functools import partial
  from typing import Any, List, Optional, Type, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet, ResNet18_Weights, ResNet50_Weights, ResNeXt101_32X8D_Weights, ResNeXt101_64X4D_Weights
  from ...transforms._presets import ImageClassification
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _IMAGENET_CATEGORIES
  from .._utils import _ovewrite_named_param, handle_legacy_interface
  from .utils import _fuse_modules, _replace_relu, quantize_model
label_desc: Use image node_id 2 for calling the QuantizableResNet obj's underlying member method code with example usage: obj.forward(x) and returns: x