text: #sys.path.pop(0)  # remove parent folder from search path
  #os.path.realpath(__file__)
  #sys.path.append(os.path.dirname(os.path.dirname()))
  
  #data_path = '../results/VAE_fashion-mnist_64_62'
  #result_path = '../results/VAE_fashion-mnist_64_62'
  #if not tf.gfile.Exists(data_path+"/global_index_cluster_data.npy"):
  #    _,global_index = concatenate_data_from_dir(data_path,num_labels=num_labels,num_clusters=num_cluster)
  #else:global_index = np.load(data_path+"/global_index_cluster_data.npy",allow_pickle=True)
  ## type(global_index)  numpy.ndarray
  #len(global_index.item().get(str(1)))
  #
  #a = global_index.item().get(str(0))
  #b = global_index.item().get(str(1))
  #np.append(a, b)
  #np.concatenate((a, b))
  #gen = (a for a in [[2,4], [1,5,7]])
  #print(list(gen))  # can only be executed once
  #import sys
  #map(sys.stdout.write, gen)
  #from itertools import chain
  #gen2 = chain(gen)
  #list(gen2).ravel()
  #import config
  ### smaller data, for debug, but this smaller data should find its intersection with the z data
  #X, y = utils_parent.load_mnist("fashion-mnist")
  #from sklearn.model_selection import train_test_split
  #X_1, X_2, Y_1, Y_2 = train_test_split(X, y, stratify=y, test_size=0.01)
  #y.take([1], axis = 0).shape
  #y[[1,2,3], ]
  #X[1,]
  #X[1].shape
  #
  
  