def METHOD_NAME(root_path, domain, batch_size, kwargs, train_val_split=.5, rand_split=True):
    kwargs_fin = dict(shuffle=True, drop_last=True)
    kwargs_fin.update(kwargs)
    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    transform = transforms.Compose(
        [ResizeImage(256),
         transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
         normalize])
    data = datasets.ImageFolder(root=os.path.join(
        root_path, domain), transform=transform)
    if train_val_split <= 0:
        train_loader = torch.utils.data.DataLoader(
                data, batch_size=batch_size, **kwargs_fin)
        return train_loader
    else:
        train_loader, val_loader = load_train_valid_split(
            data, batch_size, kwargs_fin, val_ratio=1.-train_val_split, rand_split=rand_split)
        return train_loader, val_loader