code: def generator(self, imgs):
      # imgs: input: 256x256xch
      # U-Net structure, it's slightly different from the original on the location of relu/lrelu
      with argscope(BatchNorm, training=True), argscope(
          Dropout, is_training=True
      ):
          # always use local stat for BN, and apply dropout even in testing
          with argscope(
              Conv2D, kernel_size=4, strides=2, activation=BNLReLU
          ):
              e1 = Conv2D(
                  "conv1", imgs, NF, activation=tf.nn.leaky_relu
              )
              e2 = Conv2D("conv2", e1, NF * 2)
              e3 = Conv2D("conv3", e2, NF * 4)
              e4 = Conv2D("conv4", e3, NF * 8)
              e5 = Conv2D("conv5", e4, NF * 8)
              e6 = Conv2D("conv6", e5, NF * 8)
              e7 = Conv2D("conv7", e6, NF * 8)
              e8 = Conv2D("conv8", e7, NF * 8, activation=BNReLU)  # 1x1
          with argscope(
              Conv2DTranspose,
              activation=BNReLU,
              kernel_size=4,
              strides=2,
          ):
              return (
                  LinearWrap(e8)
                  .Conv2DTranspose("deconv1", NF * 8)
                  .Dropout()
                  .ConcatWith(e7, 3)
                  .Conv2DTranspose("deconv2", NF * 8)
                  .Dropout()
                  .ConcatWith(e6, 3)
                  .Conv2DTranspose("deconv3", NF * 8)
                  .Dropout()
                  .ConcatWith(e5, 3)
                  .Conv2DTranspose("deconv4", NF * 8)
                  .ConcatWith(e4, 3)
                  .Conv2DTranspose("deconv5", NF * 4)
                  .ConcatWith(e3, 3)
                  .Conv2DTranspose("deconv6", NF * 2)
                  .ConcatWith(e2, 3)
                  .Conv2DTranspose("deconv7", NF * 1)
                  .ConcatWith(e1, 3)
                  .Conv2DTranspose(
                      "deconv8", OUT_CH, activation=tf.tanh
                  )()
              )
  
imports: import argparse
  import functools
  import glob
  import numpy
  import os
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
  from tensorpack.tfutils.summary import add_moving_summary
  from tensorpack.utils.gpu import get_num_gpu
  from tensorpack.utils.viz import stack_patches
  from GAN import GANModelDesc, GANTrainer
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.generator(imgs) and returns: LinearWrap