text: import torch
  import torch.nn as nn
  import torch.optim as optim
  from torch.autograd import Variable
  import torch.autograd as autograd
  
  import torchvision.datasets as dset
  import torchvision.transforms as transforms
  from torch.utils.data import DataLoader
  from torchvision.utils import save_image
  
  import numpy as np
  from model import *
  import argparse
  
  # parse args
  parser = argparse.ArgumentParser()
  parser.add_argument("input_label",type=int, default=0)
  args = parser.parse_args()
  
  # load model
  model_path = './save_model/md-80-900.ckpt'
  g = G().cuda()
  g.load_state_dict(torch.load(model_path))
  
  # hyperparam
  sz_con = 54
  sz_dis = 10
  size_latent = sz_con + sz_dis
  
  # input
  input_label = args.input_label
  idx = np.tile(np.repeat([input_label], 10), 10) # [0,1,2,3,...]
  # idx = np.tile(np.repeat([0,1,2,3,4,5,6,7,8,9], 1), 10)
  one_hot = np.zeros((100, 10))
  one_hot[range(100), idx] = 1
  fix_noise = torch.Tensor(100, sz_con).uniform_(-1, 1)
  z = torch.cat([torch.Tensor(one_hot), fix_noise], 1).view(-1, size_latent, 1, 1).cuda()
  
  g = g.eval() # NOTE: must add this line to avoid BatchNorm recalculation
  x_save = g(z)
  save_image(x_save.data, './_demo.png', nrow=10)