code: def __init__(self):
      super(GoogLeNet, self).__init__()
      with self.init_scope():
          self.conv1 = L.Convolution2D(None, 64, 7, stride=2, pad=3)
          self.conv2_reduce = L.Convolution2D(None, 64, 1)
          self.conv2 = L.Convolution2D(None, 192, 3, stride=1, pad=1)
          self.inc3a = L.Inception(None, 64, 96, 128, 16, 32, 32)
          self.inc3b = L.Inception(None, 128, 128, 192, 32, 96, 64)
          self.inc4a = L.Inception(None, 192, 96, 208, 16, 48, 64)
          self.inc4b = L.Inception(None, 160, 112, 224, 24, 64, 64)
          self.inc4c = L.Inception(None, 128, 128, 256, 24, 64, 64)
          self.inc4d = L.Inception(None, 112, 144, 288, 32, 64, 64)
          self.inc4e = L.Inception(None, 256, 160, 320, 32, 128, 128)
          self.inc5a = L.Inception(None, 256, 160, 320, 32, 128, 128)
          self.inc5b = L.Inception(None, 384, 192, 384, 48, 128, 128)
          self.loss3_fc = L.Linear(None, 1000)
  
          self.loss1_conv = L.Convolution2D(None, 128, 1)
          self.loss1_fc1 = L.Linear(None, 1024)
          self.loss1_fc2 = L.Linear(None, 1000)
  
          self.loss2_conv = L.Convolution2D(None, 128, 1)
          self.loss2_fc1 = L.Linear(None, 1024)
          self.loss2_fc2 = L.Linear(None, 1000)
  
imports: import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 1 to create a new GoogLeNet object from inherited base classes: chainer with example: obj = GoogLeNet()