code: def sample(model_path):
      pred = OfflinePredictor(
          PredictConfig(
              session_init=SmartInit(model_path),
              model=Model(),
              input_names=["z_code", "z_noise"],
              output_names=["gen/viz"],
          )
      )
  
      # sample all one-hot encodings (10 times)
      z_cat = np.tile(np.eye(10), [10, 1])
      # sample continuos variables from -2 to +2 as mentioned in the paper
      z_uni = np.linspace(-2.0, 2.0, num=100)
      z_uni = z_uni[:, None]
  
      IMG_SIZE = 400
  
      while True:
          # only categorical turned on
          z_noise = np.random.uniform(-1, 1, (100, NOISE_DIM))
          zc = np.concatenate((z_cat, z_uni * 0, z_uni * 0), axis=1)
          o = pred(zc, z_noise)[0]
          viz1 = viz.stack_patches(o, nr_row=10, nr_col=10)
          viz1 = cv2.resize(viz1, (IMG_SIZE, IMG_SIZE))
  
          # show effect of first continous variable with fixed noise
          zc = np.concatenate((z_cat, z_uni, z_uni * 0), axis=1)
          o = pred(zc, z_noise * 0)[0]
          viz2 = viz.stack_patches(o, nr_row=10, nr_col=10)
          viz2 = cv2.resize(viz2, (IMG_SIZE, IMG_SIZE))
  
          # show effect of second continous variable with fixed noise
          zc = np.concatenate((z_cat, z_uni * 0, z_uni), axis=1)
          o = pred(zc, z_noise * 0)[0]
          viz3 = viz.stack_patches(o, nr_row=10, nr_col=10)
          viz3 = cv2.resize(viz3, (IMG_SIZE, IMG_SIZE))
  
          canvas = viz.stack_patches(
              [viz1, viz2, viz3],
              nr_row=1,
              nr_col=3,
              border=5,
              bgcolor=(255, 0, 0),
          )
  
          viz.interactive_imshow(canvas)
  
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 6 for calling a global function with example usage: sample(model_path) without return types