I don’t understand how I would adapt that in my code, please support me
          train_transform = transforms.Compose([
                    transforms.RandomRotation(10),      # rotate +/- 10 degrees
                    transforms.RandomHorizontalFlip(),  # reverse 50% of images
                    transforms.Resize(image),             # resize shortest side to 224 pixels
                    transforms.CenterCrop(image),         # crop longest side to 224 pixels at center
                    transforms.ToTensor(),
                    transforms.Normalize([0.485, 0.456, 0.406],
                                         [0.229, 0.224, 0.225])
                ])
            
                test_transform = transforms.Compose([
                    transforms.Resize(image),
                    transforms.CenterCrop(image),
                    transforms.ToTensor(),
                    transforms.Normalize([0.485, 0.456, 0.406],
                                         [0.229, 0.224, 0.225])
                ])
                
                inv_normalize = transforms.Normalize(
                mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
                std=[1/0.229, 1/0.224, 1/0.225]
                )
                
                print("RGB")
                
            
            train_data = datasets.ImageFolder(os.path.join(root, 'train_real'), transform=train_transform)
            test_data = datasets.ImageFolder(os.path.join(root, 'validation'), transform=test_transform)
            
            
            
            torch.manual_seed(42)
            

            train_loader = DataLoader(train_data, batch_size=10,num_workers=2, pin_memory=False,shuffle=True)
            test_loader = DataLoader(test_data, batch_size=10, num_workers=2,pin_memory=False,shuffle=True)
            
            #obtiene los labels o clases del dataset
            class_names = train_data.classes
            
            print(class_names)
            print(f'Training images available: {len(train_data)}')
            print(f'Testing images available:  {len(test_data)}')