text: import os
  from Model.auto_encoder import create_auto_encoder
  from Utils.reporting import *
  from Data_Utils.generate_samples import *
  from Data_Utils.data_generator import DataGenerator
  
  
  def train(args):
  
      #setup data
      random_img = get_random_sample_img(args)
      random_img_samples = get_random_samples(args,100)
  
      data_generator = DataGenerator(args,shuffle=True)
  
      encoder,decoder,auto_encoder = create_auto_encoder(dims=args.img_width, latent_dim = args.latent_dim)
  
      for i,epoch in enumerate(range(args.num_epochs)):
  
          print(f'training epoch {str(i)}')
  
          auto_encoder.fit(
              data_generator,
              epochs=1,
              shuffle=True,
          )
  
          if(i%args.print_freq == 0):
              generate_test_image(auto_encoder,random_img,args.output_test_img_dir,i)
              generate_test_image_grid(auto_encoder,random_img_samples,args.output_test_img_dir,i)
  
          if(i%args.save_freq == 0):
              encoder_save_path = os.path.join(args.saved_model_dir,f"{i:04d}_encoder_model.h5")
              encoder.save(encoder_save_path)
  
              decoder_save_path = os.path.join(args.saved_model_dir,f"{i:04d}_decoder_model.h5")
              decoder.save(decoder_save_path)
  
      generate_test_image(auto_encoder,random_img,args.output_test_img_dir,args.num_epochs)
      generate_test_image_grid(auto_encoder,random_img_samples,args.output_test_img_dir,args.num_epochs)
  
      encoder_save_path = os.path.join(args.saved_model_dir,f"{args.num_epochs:04d}_encoder_model.h5")
      encoder.save(encoder_save_path)
  
      decoder_save_path = os.path.join(args.saved_model_dir,f"{args.num_epochs:04d}_decoder_model.h5")
      decoder.save(decoder_save_path)
  
  