code: def __init__(self, ndf=64, n_layers=3, use_sigmoid=False):
      super(NLayerDiscriminator, self).__init__()
      self.model = nn.HybridSequential()
      kw = 4
      padw = 1
      with self.name_scope():
          self.model.add(
              nn.Conv2D(ndf, kernel_size=kw, strides=2, padding=padw),
              nn.LeakyReLU(0.2),
          )
  
          nf_mult = 1
          for n in range(1, n_layers):
              nf_mult = min(2**n, 8)
              self.model.add(
                  nn.Conv2D(
                      ndf * nf_mult,
                      kernel_size=kw,
                      strides=2,
                      padding=padw,
                  ),
                  nn.InstanceNorm(),
                  nn.LeakyReLU(0.2),
              )
  
          nf_mult = min(2**n_layers, 8)
          self.model.add(
              nn.Conv2D(
                  ndf * nf_mult, kernel_size=kw, strides=1, padding=padw
              ),
              nn.InstanceNorm(),
              nn.LeakyReLU(0.2),
          )
          self.model.add(
              nn.Conv2D(1, kernel_size=kw, strides=1, padding=padw)
          )
          if use_sigmoid:
              self.model.add(nn.Activation("sigmoid"))
  
imports: import argparse
  import random
  from mxnet import gluon, image, autograd
  from mxnet.gluon.data.vision import transforms
  from mxnet.base import numeric_types
  import gluoncv
  from gluoncv import utils
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import nn
  import mxnet.ndarray
  import mxnet
  import os
  import numpy
  from mxboard import SummaryWriter
label_desc: Use image node_id 1 to create a new NLayerDiscriminator object from inherited base classes: gluon.nn with example: obj = NLayerDiscriminator(ndf, n_layers, use_sigmoid)