code: def sample_prior(batch_size):
      cat, _ = get_distributions(
          DIST_PRIOR_PARAM[:NUM_CLASS], DIST_PRIOR_PARAM[NUM_CLASS:]
      )
      sample_cat = tf.one_hot(cat.sample(batch_size), NUM_CLASS)
  
      """
      OpenAI official code actually models the "uniform" latent code as
      a Gaussian distribution, but obtain the samples from a uniform distribution.
      """
      sample_uni = tf.random_uniform([batch_size, NUM_UNIFORM], -1, 1)
      samples = tf.concat([sample_cat, sample_uni], axis=1)
      return samples
  
imports: import argparse
  import numpy
  import os
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import gradproc, optimizer, summary
  from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope, under_name_scope
  from tensorpack.utils import viz
  from GAN import GANModelDesc, GANTrainer
label_desc: Use image node_id 4 for calling a global function with example usage: sample_prior(batch_size) and returns: samples