code: def __init__(self, bottom_width=4, ch=512, wscale=0.02):
      w = chainer.initializers.Normal(wscale)
      super(Discriminator, self).__init__()
      with self.init_scope():
          self.c0_0 = L.Convolution2D(3, ch // 8, 3, 1, 1, initialW=w)
          self.c0_1 = L.Convolution2D(
              ch // 8, ch // 4, 4, 2, 1, initialW=w
          )
          self.c1_0 = L.Convolution2D(
              ch // 4, ch // 4, 3, 1, 1, initialW=w
          )
          self.c1_1 = L.Convolution2D(
              ch // 4, ch // 2, 4, 2, 1, initialW=w
          )
          self.c2_0 = L.Convolution2D(
              ch // 2, ch // 2, 3, 1, 1, initialW=w
          )
          self.c2_1 = L.Convolution2D(
              ch // 2, ch // 1, 4, 2, 1, initialW=w
          )
          self.c3_0 = L.Convolution2D(
              ch // 1, ch // 1, 3, 1, 1, initialW=w
          )
          self.l4 = L.Linear(
              bottom_width * bottom_width * ch, 1, initialW=w
          )
          self.bn0_1 = L.BatchNormalization(ch // 4, use_gamma=False)
          self.bn1_0 = L.BatchNormalization(ch // 4, use_gamma=False)
          self.bn1_1 = L.BatchNormalization(ch // 2, use_gamma=False)
          self.bn2_0 = L.BatchNormalization(ch // 2, use_gamma=False)
          self.bn2_1 = L.BatchNormalization(ch // 1, use_gamma=False)
          self.bn3_0 = L.BatchNormalization(ch // 1, use_gamma=False)
  
file_path: tensorboardX/examples/chainer/extension_logger/net.py
imports: from __future__ import print_function
  import numpy
  import chainer
  from chainer import cuda
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 1 to create a new Discriminator object from inherited base classes: chainer with example: obj = Discriminator(bottom_width, ch, wscale)