class_docstr: New GoogLeNet of BatchNormalization version.
code: def __init__(self):
      super(GoogLeNetBN, self).__init__()
      with self.init_scope():
          self.conv1 = L.Convolution2D(
              None, 64, 7, stride=2, pad=3, nobias=True
          )
          self.norm1 = L.BatchNormalization(64)
          self.conv2 = L.Convolution2D(None, 192, 3, pad=1, nobias=True)
          self.norm2 = L.BatchNormalization(192)
          self.inc3a = L.InceptionBN(
              None, 64, 64, 64, 64, 96, "avg", 32
          )
          self.inc3b = L.InceptionBN(
              None, 64, 64, 96, 64, 96, "avg", 64
          )
          self.inc3c = L.InceptionBN(
              None, 0, 128, 160, 64, 96, "max", stride=2
          )
          self.inc4a = L.InceptionBN(
              None, 224, 64, 96, 96, 128, "avg", 128
          )
          self.inc4b = L.InceptionBN(
              None, 192, 96, 128, 96, 128, "avg", 128
          )
          self.inc4c = L.InceptionBN(
              None, 160, 128, 160, 128, 160, "avg", 128
          )
          self.inc4d = L.InceptionBN(
              None, 96, 128, 192, 160, 192, "avg", 128
          )
          self.inc4e = L.InceptionBN(
              None, 0, 128, 192, 192, 256, "max", stride=2
          )
          self.inc5a = L.InceptionBN(
              None, 352, 192, 320, 160, 224, "avg", 128
          )
          self.inc5b = L.InceptionBN(
              None, 352, 192, 320, 192, 224, "max", 128
          )
          self.out = L.Linear(None, 1000)
  
          self.conva = L.Convolution2D(None, 128, 1, nobias=True)
          self.norma = L.BatchNormalization(128)
          self.lina = L.Linear(None, 1024, nobias=True)
          self.norma2 = L.BatchNormalization(1024)
          self.outa = L.Linear(None, 1000)
  
          self.convb = L.Convolution2D(None, 128, 1, nobias=True)
          self.normb = L.BatchNormalization(128)
          self.linb = L.Linear(None, 1024, nobias=True)
          self.normb2 = L.BatchNormalization(1024)
          self.outb = L.Linear(None, 1000)
  
imports: import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 1 to create a new GoogLeNetBN object from inherited base classes: chainer with example: obj = GoogLeNetBN()