code: def __init__(
      self,
      output_nc,
      ngf=64,
      use_dropout=False,
      n_blocks=6,
      padding_type="reflect",
  ):
      assert n_blocks >= 0
      super(ResnetGenerator, self).__init__()
      self.output_nc = output_nc
      self.ngf = ngf
      self.model = nn.HybridSequential()
      with self.name_scope():
          self.model.add(
              nn.ReflectionPad2D(3),
              nn.Conv2D(ngf, kernel_size=7, padding=0),
              nn.InstanceNorm(),
              nn.Activation("relu"),
          )
  
          n_downsampling = 2
          for i in range(n_downsampling):
              mult = 2**i
              self.model.add(
                  nn.Conv2D(
                      ngf * mult * 2,
                      kernel_size=3,
                      strides=2,
                      padding=1,
                  ),
                  nn.InstanceNorm(),
                  nn.Activation("relu"),
              )
  
          mult = 2**n_downsampling
          for i in range(n_blocks):
              self.model.add(
                  ResnetBlock(
                      ngf * mult,
                      padding_type=padding_type,
                      use_dropout=use_dropout,
                  )
              )
  
          for i in range(n_downsampling):
              mult = 2 ** (n_downsampling - i)
              self.model.add(
                  nn.Conv2DTranspose(
                      int(ngf * mult / 2),
                      kernel_size=3,
                      strides=2,
                      padding=1,
                      output_padding=1,
                  ),
                  nn.InstanceNorm(),
                  nn.Activation("relu"),
              )
          self.model.add(
              nn.ReflectionPad2D(3),
              nn.Conv2D(output_nc, kernel_size=7, padding=0),
              nn.Activation("tanh"),
          )
  
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 ResnetGenerator object from inherited base classes: gluon.nn with example: obj = ResnetGenerator(output_nc, ngf, use_dropout, n_blocks, padding_type)