code: def __init__(self, n_hidden, bottom_width=4, ch=512, wscale=0.02):
      super(Generator, self).__init__()
      self.n_hidden = n_hidden
      self.ch = ch
      self.bottom_width = bottom_width
  
      with self.init_scope():
          w = chainer.initializers.Normal(wscale)
          self.l0 = L.Linear(
              self.n_hidden,
              bottom_width * bottom_width * ch,
              initialW=w,
          )
          self.dc1 = L.Deconvolution2D(ch, ch // 2, 4, 2, 1, initialW=w)
          self.dc2 = L.Deconvolution2D(
              ch // 2, ch // 4, 4, 2, 1, initialW=w
          )
          self.dc3 = L.Deconvolution2D(
              ch // 4, ch // 8, 4, 2, 1, initialW=w
          )
          self.dc4 = L.Deconvolution2D(ch // 8, 3, 3, 1, 1, initialW=w)
          self.bn0 = L.BatchNormalization(
              bottom_width * bottom_width * ch
          )
          self.bn1 = L.BatchNormalization(ch // 2)
          self.bn2 = L.BatchNormalization(ch // 4)
          self.bn3 = L.BatchNormalization(ch // 8)
  
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 Generator object from inherited base classes: chainer with example: obj = Generator(n_hidden, bottom_width, ch, wscale)