code: def fuse_model(self, is_qat: Optional[bool] = None) -> None:
      for m in self.modules():
          if type(m) is Conv2dNormActivation:
              modules_to_fuse = ["0", "1"]
              if len(m) == 3 and type(m[2]) is nn.ReLU:
                  modules_to_fuse.append("2")
              _fuse_modules(m, modules_to_fuse, is_qat, inplace=True)
          elif type(m) is QuantizableSqueezeExcitation:
              m.fuse_model(is_qat)
  
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 3 for calling the QuantizableMobileNetV3 obj's underlying member method code with example usage: obj.fuse_model(is_qat) without return types