code: def forward(self, x: Tensor) -> Tensor:
      # N x 768 x 17 x 17
      x = F.avg_pool2d(x, kernel_size=5, stride=3)
      # N x 768 x 5 x 5
      x = self.conv0(x)
      # N x 128 x 5 x 5
      x = self.conv1(x)
      # N x 768 x 1 x 1
      # Adaptive average pooling
      x = F.adaptive_avg_pool2d(x, (1, 1))
      # N x 768 x 1 x 1
      x = torch.flatten(x, 1)
      # N x 768
      x = self.fc(x)
      # N x 1000
      return x
  
imports: import warnings
  from collections import namedtuple
  from functools import partial
  from typing import Any, Callable, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 2 for calling the InceptionAux obj's underlying member method code with example usage: obj.forward(x) and returns: x