code: def __init__(self, output_nc, num_downs, ngf=64, use_dropout=False):
      super(UnetGenerator, self).__init__()
  
      # construct unet structure
      unet_block = UnetSkipConnectionBlock(
          ngf * 8, ngf * 8, submodule=None, innermost=True
      )
      for i in range(num_downs - 5):
          unet_block = UnetSkipConnectionBlock(
              ngf * 8,
              ngf * 8,
              submodule=unet_block,
              use_dropout=use_dropout,
          )
      unet_block = UnetSkipConnectionBlock(
          ngf * 4, ngf * 8, submodule=unet_block
      )
      unet_block = UnetSkipConnectionBlock(
          ngf * 2, ngf * 4, submodule=unet_block
      )
      unet_block = UnetSkipConnectionBlock(
          ngf, ngf * 2, submodule=unet_block
      )
      unet_block = UnetSkipConnectionBlock(
          output_nc, ngf, submodule=unet_block, outermost=True
      )
  
      self.model = unet_block
  
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 UnetGenerator object from inherited base classes: gluon.nn with example: obj = UnetGenerator(output_nc, num_downs, ngf, use_dropout)