old_contents: from __future__ import print_function
  
  import argparse
  
  import numpy as np
  import torch
  from torch.autograd import Variable
  from torch.optim import Adam
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision import datasets
  
  from transformernet import TransformerNet
  from vgg16 import Vgg16
  import utils
  import os
  
  
  def main():
      parser = argparse.ArgumentParser(description="parser for fast-neural-style")
      parser.add_argument("--batch-size", "-b", type=int, default=4)
      parser.add_argument("--epochs", "-e", type=int, default=2)
      parser.add_argument("--cuda", type=int, default=0)
      parser.add_argument("--dataset", type=str, default="MSCOCO")
      parser.add_argument("--image-size", type=int, default=256)
      args = parser.parse_args()
  
  
      if args.cuda and not torch.cuda.is_available():
          print("WARNING: torch.cuda not available, using CPU.")
          args.cuda = 0
  
      if args.cuda:
          kwargs = {'num_workers': 1, 'pin_memory': False}
      else:
          kwargs = {}
  
      print("=====================")
      print("TEST MODE")
      print("using 1 worker")
      print("=====================")
  
      print("=====================")
      print("TORCH VERSION:", torch.__version__)
      print("BATCH SIZE:", args.batch_size)
      print("EPOCHS:", args.epochs)
      print("CUDA:", args.cuda)
      print("DATASET:", args.dataset)
      print("IMAGE SIZE:", args.image_size)
      print("=====================\n")
  
      transform = transforms.Compose([transforms.Scale(args.image_size),
                                      transforms.CenterCrop(args.image_size),
                                      transforms.ToTensor(),
                                      transforms.Lambda(lambda x: x.mul(255))])
      train_dataset = datasets.ImageFolder(args.dataset, transform)
      train_loader = DataLoader(train_dataset, batch_size=args.batch_size, **kwargs)
  
      for e in range(args.epochs):
          batch_id = 0
          for x in train_loader:
              if batch_id < 10 or batch_id % 500 == 0:
                  print("Processing batch:", batch_id)
              batch_id += 1
  
      print("\nDone :)")
  
  
  if __name__ == "__main__":
      main()
  
new_contents: from __future__ import print_function
  
  import argparse
  
  import torch
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision import datasets
  
  
  def main():
      parser = argparse.ArgumentParser(description="parser for fast-neural-style")
      parser.add_argument("--batch-size", "-b", type=int, default=4)
      parser.add_argument("--epochs", "-e", type=int, default=2)
      parser.add_argument("--cuda", type=int, default=0)
      parser.add_argument("--dataset", type=str, default="MSCOCO")
      parser.add_argument("--image-size", type=int, default=256)
      args = parser.parse_args()
  
  
      if args.cuda and not torch.cuda.is_available():
          print("WARNING: torch.cuda not available, using CPU.")
          args.cuda = 0
  
      if args.cuda:
          kwargs = {'num_workers': 1, 'pin_memory': False}
      else:
          kwargs = {}
  
      print("=====================")
      print("TEST MODE")
      print("=====================")
  
      print("=====================")
      print("TORCH VERSION:", torch.__version__)
      print("BATCH SIZE:", args.batch_size)
      print("EPOCHS:", args.epochs)
      print("CUDA:", args.cuda)
      print("DATASET:", args.dataset)
      print("IMAGE SIZE:", args.image_size)
      print("=====================\n")
  
      transform = transforms.Compose([transforms.Scale(args.image_size),
                                      transforms.CenterCrop(args.image_size),
                                      transforms.ToTensor(),
                                      transforms.Lambda(lambda x: x.mul(255))])
      train_dataset = datasets.ImageFolder(args.dataset, transform)
      train_loader = DataLoader(train_dataset, batch_size=args.batch_size, **kwargs)
  
      for e in range(args.epochs):
          batch_id = 0
          for x in train_loader:
              if batch_id < 10 or batch_id % 500 == 0:
                  print("Processing batch:", batch_id)
              batch_id += 1
  
      print("\nDone :)")
  
  
  if __name__ == "__main__":
      main()
  
subject: Remove commented code