code: def forward(self, x: Tensor) -> GoogLeNetOutputs:
      x = self._transform_input(x)
      x = self.quant(x)
      x, aux1, aux2 = self._forward(x)
      x = self.dequant(x)
      aux_defined = self.training and self.aux_logits
      if torch.jit.is_scripting():
          if not aux_defined:
              warnings.warn(
                  "Scripted QuantizableGoogleNet always returns GoogleNetOutputs Tuple"
              )
          return GoogLeNetOutputs(x, aux2, aux1)
      else:
          return self.eager_outputs(x, aux2, aux1)
  
imports: import warnings
  from functools import partial
  from typing import Any, Optional, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
  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 ..googlenet import BasicConv2d, GoogLeNet, GoogLeNet_Weights, GoogLeNetOutputs, Inception, InceptionAux
  from .utils import _fuse_modules, _replace_relu, quantize_model
label_desc: Use image node_id 2 for calling the QuantizableGoogLeNet obj's underlying member method code with example usage: obj.forward(x) and returns: GoogLeNetOutputs, self