code: def forward(self, x: Tensor) -> Tensor:
      # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
      x = F.adaptive_avg_pool2d(x, (4, 4))
      # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
      x = self.conv(x)
      # N x 128 x 4 x 4
      x = torch.flatten(x, 1)
      # N x 2048
      x = self.relu(self.fc1(x))
      # N x 1024
      x = self.dropout(x)
      # N x 1024
      x = self.fc2(x)
      # N x 1000 (num_classes)
  
      return x
  
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 QuantizableInceptionAux obj's underlying member method code with example usage: obj.forward(x) and returns: x