code: def log_variables(self):
      tf.summary.scalar("Generator Loss", self.G_loss)
      tf.summary.scalar("Discriminator Loss (WP)", self.D_loss)
      tf.summary.scalar(
          "Discriminator Loss (Basic)", self.D_origin_loss
      )
      tf.summary.scalar("Interpolation %", self.alpha_transition)
  
      super(PGGAN, self).log_variables()
  
      return
  
imports: import math
  import numpy
  import tensorflow
  import os
  import imageio
  import keras
  import scipy
  from tqdm import tqdm
  from collections import defaultdict
  from deepneuro.utilities.util import add_parameter
  from deepneuro.models.blocks import generator, discriminator
  from deepneuro.models.cost_functions import wasserstein_loss
  from deepneuro.models.gan import GAN
  from deepneuro.models.ops import upscale2d, downscale2d
  from deepneuro.utilities.visualize import check_data
label_desc: Use image node_id 8 for calling the PGGAN obj's underlying member method code with example usage: obj.log_variables() without return types