code: def __init__(self, isize, nz, nc, ndf, ngpu):
      super(MLP_D, self).__init__()
      self.ngpu = ngpu
  
      self.main = gluon.nn.Sequential()
      with self.main.name_scope():
          self.main.add(
              nn.Dense(
                  units=ndf,
                  in_units=nc * isize * isize,
                  activation="relu",
              )
          )
          # Z goes into a linear of size: ndf
          self.main.add(
              nn.Dense(units=ndf, in_units=ndf, activation="relu")
          )
          self.main.add(
              nn.Dense(units=ndf, in_units=ndf, activation="relu")
          )
          self.main.add(nn.Dense(units=1, in_units=ndf))
  
      self.nc = nc
      self.isize = isize
      self.nz = nz
  
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 MLP_D object from inherited base classes: nn with example: obj = MLP_D(isize, nz, nc, ndf, ngpu)