code: def forward(self, x):
      # 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: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import trunc_normal_, create_classifier, Linear, ConvNormAct
  from ._builder import build_model_with_cfg
  from ._builder import resolve_pretrained_cfg
  from ._manipulate import flatten_modules
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
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