code: def __init__(self, *args: Any, **kwargs: Any) -> None:
      super().__init__(*args, **kwargs)
      self.skip_add = nn.quantized.FloatFunctional()
  
imports: from functools import partial
  from typing import Any, Optional, Union
  from torch import nn, Tensor
  from torch.ao.quantization import DeQuantStub, QuantStub
  from torchvision.models.mobilenetv2 import InvertedResidual, MobileNet_V2_Weights, MobileNetV2
  from ...ops.misc import Conv2dNormActivation
  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 1 to create a new QuantizableInvertedResidual object from inherited base classes: InvertedResidual with example: obj = QuantizableInvertedResidual()