code: def fuse_model(self, is_qat: Optional[bool] = None) -> None:
      for m in self.modules():
          if type(m) is Conv2dNormActivation:
              _fuse_modules(m, ["0", "1", "2"], is_qat, inplace=True)
          if type(m) is QuantizableInvertedResidual:
              m.fuse_model(is_qat)
  
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 3 for calling the QuantizableMobileNetV2 obj's underlying member method code with example usage: obj.fuse_model(is_qat) without return types