code: def build_graph(self, input, output):
      input, output = input / 128.0 - 1, output / 128.0 - 1
  
      with argscope(
          [Conv2D, Conv2DTranspose],
          kernel_initializer=tf.truncated_normal_initializer(
              stddev=0.02
          ),
      ):
          with tf.variable_scope("gen"):
              fake_output = self.generator(input)
          with tf.variable_scope("discrim"):
              real_pred = self.discriminator(input, output)
              fake_pred = self.discriminator(input, fake_output)
  
      self.build_losses(real_pred, fake_pred)
      errL1 = tf.reduce_mean(
          tf.abs(fake_output - output), name="L1_loss"
      )
      self.g_loss = tf.add(
          self.g_loss, LAMBDA * errL1, name="total_g_loss"
      )
      add_moving_summary(errL1, self.g_loss)
  
      # tensorboard visualization
      if IN_CH == 1:
          input = tf.image.grayscale_to_rgb(input)
      if OUT_CH == 1:
          output = tf.image.grayscale_to_rgb(output)
          fake_output = tf.image.grayscale_to_rgb(fake_output)
  
      visualize_tensors(
          "input,output,fake",
          [input, output, fake_output],
          max_outputs=max(30, BATCH),
      )
  
      self.collect_variables()
  
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 4 for calling the Model obj's underlying member method code with example usage: obj.build_graph(input, output) without return types