code: def __init__(self, *args: Any, **kwargs: Any) -> None:
      kwargs["scale_activation"] = nn.Hardsigmoid
      super().__init__(*args, **kwargs)
      self.skip_mul = nn.quantized.FloatFunctional()
  
imports: from functools import partial
  from typing import Any, List, Optional, Union
  import torch
  from torch import nn, Tensor
  from torch.ao.quantization import DeQuantStub, QuantStub
  from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
  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 ..mobilenetv3 import _mobilenet_v3_conf, InvertedResidual, InvertedResidualConfig, MobileNet_V3_Large_Weights, MobileNetV3
  from .utils import _fuse_modules, _replace_relu
label_desc: Use image node_id 1 to create a new QuantizableSqueezeExcitation object from inherited base classes: SqueezeExcitation with example: obj = QuantizableSqueezeExcitation()