code: def __init__(self, in_channels, num_classes, conv_block=None):
      super(InceptionAux, self).__init__()
      conv_block = conv_block or ConvNormAct
      self.conv0 = conv_block(in_channels, 128, kernel_size=1)
      self.conv1 = conv_block(128, 768, kernel_size=5)
      self.conv1.stddev = 0.01
      self.fc = Linear(768, num_classes)
      self.fc.stddev = 0.001
  
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 1 to create a new InceptionAux object from inherited base classes: nn with example: obj = InceptionAux(in_channels, num_classes, conv_block)