code: def __init__(self, *args: Any, **kwargs: Any) -> None:
      super().__init__(*args, **kwargs)
      self.skip_add_relu = nn.quantized.FloatFunctional()
      self.relu1 = nn.ReLU(inplace=False)
      self.relu2 = nn.ReLU(inplace=False)
  
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 1 to create a new QuantizableBottleneck object from inherited base classes: Bottleneck with example: obj = QuantizableBottleneck()