text: import numpy as np
  import tensorflow as tf
  import os
  from absl import app
  from absl import flags
  from pcbigan import PCBIGAN
  import sys
  
  flags = tf.app.flags
  
  flags.DEFINE_integer("epochs",1000,"epochs per trainingstep")
  flags.DEFINE_float("learning_rate",0.00005,"learning rate for the model")
  flags.DEFINE_integer("z_dim",126,"dimension of the noise Input")
  flags.DEFINE_bool("training",True,"running training of the poincloud gan")
  flags.DEFINE_string("checkpoint_dir","C:/Users/Andreas/Desktop/punktwolkenplot/pointgan/checkpoint/","where to save the model")
  flags.DEFINE_string("sample_dir","samples","where the samples are stored")
  flags.DEFINE_integer("number_point",2048,"number of points in each pointcloud")
  flags.DEFINE_integer("pointcloud_dim",1028,"number of input")
  flags.DEFINE_integer("iterations",100000,"number of patches")
  flags.DEFINE_integer("batch_size",32,"size of the batch")
  flags.DEFINE_float("beta1",0.5,"adam beta1")
  flags.DEFINE_float("beta2",0.5,"adam beta2")
  FLAGS = flags.FLAGS
  
  def _main(argv):
  	print("initializing Params")
  	if not os.path.exists(FLAGS.checkpoint_dir):
  		os.makedirs(FLAGS.checkpoint_dir)
  	if not os.path.exists(FLAGS.sample_dir):
  		os.makedirs(FLAGS.sample_dir)
  	if FLAGS.training == True:
  			pcbigan = PCBIGAN(FLAGS.training,FLAGS.epochs,FLAGS.checkpoint_dir,FLAGS.learning_rate,FLAGS.z_dim,FLAGS.batch_size,FLAGS.beta1,FLAGS.beta2,FLAGS.pointcloud_dim)
  			pcbigan.train()
  	else:
  		if not pgpggan.load(FLAGS.checkpoint_dir):
  			print("first train your model")
  
  if __name__ == '__main__':
  	print('Starting the Programm....')
  	app.run(_main)
  