docstr: assuming input size 7x7
docstr_tok: ["assuming","input","size","7x7"]
code: def __init__(self, C, size=5, num_classes=1000):
      """assuming input size 7x7"""
      super(AuxiliaryHeadImageNet, self).__init__()
      self.features = nn.Sequential(
          nn.ReLU(inplace=True),
          nn.AvgPool2d(
              size, 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)
  
file_path: nni/examples/nas/legacy/cdarts/aux_head.py
imports: import torch.nn
label_desc: Use image node_id 1 to create a new AuxiliaryHeadImageNet object from inherited base classes: nn with example: obj = AuxiliaryHeadImageNet(C, size, num_classes)