docstr: assuming input size 14x14
docstr_tok: ["assuming","input","size","14x14"]
code: def __init__(self, C, num_classes):
      """assuming input size 14x14"""
      super(AuxiliaryHeadImageNet, self).__init__()
      self.features = nn.Sequential(
          nn.ReLU(inplace=True),
          nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
          nn.Conv2d(C, 128, 1, bias=False),
          nn.BatchNorm2d(128),
          nn.ReLU(inplace=True),
          nn.Conv2d(128, 768, 2, bias=False),
          # NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
          # Commenting it out for consistency with the experiments in the paper.
          # nn.BatchNorm2d(768),
          nn.ReLU(inplace=True),
      )
      self.classifier = nn.Linear(768, num_classes)
  
imports: from operations import *
  from .utils import drop_path
label_desc: Use image node_id 1 to create a new AuxiliaryHeadImageNet object from inherited base classes: nn with example: obj = AuxiliaryHeadImageNet(C, num_classes)