code: def __init__(self, *args: Any, **kwargs: Any) -> None:
      super().__init__(*args, inverted_residual=QuantizableInvertedResidual, **kwargs)  # type: ignore[misc]
      self.quant = torch.ao.quantization.QuantStub()
      self.dequant = torch.ao.quantization.DeQuantStub()
  
imports: from functools import partial
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torchvision.models import shufflenetv2
  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 ..shufflenetv2 import ShuffleNet_V2_X0_5_Weights, ShuffleNet_V2_X1_0_Weights, ShuffleNet_V2_X1_5_Weights, ShuffleNet_V2_X2_0_Weights
  from .utils import _fuse_modules, _replace_relu, quantize_model
label_desc: Use image node_id 1 to create a new QuantizableShuffleNetV2 object from inherited base classes: shufflenetv2 with example: obj = QuantizableShuffleNetV2()