code: def define_G(output_nc, ngf, which_model_netG, use_dropout=False):
      if which_model_netG == "resnet_9blocks":
          netG = ResnetGenerator(
              output_nc, ngf, use_dropout=use_dropout, n_blocks=9
          )
      elif which_model_netG == "resnet_6blocks":
          netG = ResnetGenerator(
              output_nc, ngf, use_dropout=use_dropout, n_blocks=6
          )
      elif which_model_netG == "unet_128":
          netG = UnetGenerator(
              output_nc, 7, ngf, use_dropout=use_dropout
          )
      elif which_model_netG == "unet_256":
          netG = UnetGenerator(
              output_nc, 8, ngf, use_dropout=use_dropout
          )
      else:
          raise NotImplementedError(
              "Generator model name [%s] is not recognized"
              % opt.which_model_netG
          )
  
      return netG
  
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 for calling a global function with example usage: define_G(output_nc, ngf, which_model_netG, use_dropout) and returns: netG