code: def __init__(self, isize, nz, nc, ngf, ngpu, n_extra_layers=0):
      super(DCGAN_G, self).__init__()
      self.ngpu = ngpu
      assert isize % 16 == 0, "isize has to be a multiple of 16"
  
      cngf, tisize = ngf // 2, 4
      while tisize != isize:
          cngf = cngf * 2
          tisize = tisize * 2
      with self.name_scope():
          main = nn.Sequential()
          # input is Z, going into a convolution
          main.add(
              nn.Conv2DTranspose(
                  in_channels=nz,
                  channels=cngf,
                  kernel_size=4,
                  strides=1,
                  padding=0,
                  use_bias=False,
                  prefix="initial.{0}-{1}.convt".format(nz, cngf),
              )
          )
          main.add(
              nn.BatchNorm(
                  in_channels=cngf,
                  prefix="initial.{0}.batchnorm".format(cngf),
              )
          )
          main.add(
              nn.LeakyReLU(0, prefix="initial.{0}.relu".format(cngf))
          )
  
          csize, cndf = 4, cngf
          while csize < isize // 2:
              main.add(
                  nn.Conv2DTranspose(
                      in_channels=cngf,
                      channels=cngf // 2,
                      kernel_size=4,
                      strides=2,
                      padding=1,
                      use_bias=False,
                      prefix="pyramid.{0}-{1}.convt".format(
                          cngf, cngf // 2
                      ),
                  )
              )
              main.add(
                  nn.BatchNorm(
                      in_channels=cngf // 2,
                      prefix="pyramid.{0}.batchnorm".format(cngf // 2),
                  )
              )
              main.add(
                  nn.LeakyReLU(
                      0, prefix="pyramid.{0}.relu".format(cngf // 2)
                  )
              )
              cngf = cngf // 2
              csize = csize * 2
  
          # Extra layers
          for t in range(n_extra_layers):
              main.add(
                  nn.Conv2D(
                      in_channels=cngf,
                      channels=cngf,
                      kernel_size=3,
                      strides=1,
                      padding=1,
                      use_bias=False,
                      prefix="extra-layers-{0}.{1}.conv".format(
                          t, cngf
                      ),
                  )
              )
              main.add(
                  nn.BatchNorm(
                      in_channels=cngf,
                      prefix="extra-layers-{0}.{1}.batchnorm".format(
                          t, cngf
                      ),
                  )
              )
              main.add(
                  nn.LeakyReLU(
                      0,
                      prefix="extra-layers-{0}.{1}.relu".format(
                          t, cngf
                      ),
                  )
              )
  
          main.add(
              nn.Conv2DTranspose(
                  in_channels=cngf,
                  channels=nc,
                  kernel_size=4,
                  strides=2,
                  padding=1,
                  use_bias=False,
                  activation="tanh",
                  prefix="final.{0}-{1}.convt".format(cngf, nc),
              )
          )
  
      self.main = main
  
imports: from __future__ import print_function
  import argparse
  from mxnet.image import image
  from mxnet.gluon.data.vision import transforms, CIFAR10, ImageFolderDataset
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import Trainer, nn
  from mxnet.base import numeric_types
  from mxnet.initializer import *
  from mxnet import autograd
  import mxnet
  import mxnet.gluon
  import numpy
  import math
  import os
  from mxboard import SummaryWriter
  from lsun import LSUN
label_desc: Use image node_id 1 to create a new DCGAN_G object from inherited base classes: nn with example: obj = DCGAN_G(isize, nz, nc, ngf, ngpu, n_extra_layers)