Normalize UInt16 images for Pix2Pix implementation

Question

i'm trying to use this implementation of Pix2Pix algorithm https://github.com/mrzhu-cool/pix2pix-pytorch. In this implementation they are using small UInt8 (max color value : 255) images so they are normalizing their images like this :

Image.MAX_IMAGE_PIXELS = None
        a = Image.open(join(self.a_path, self.image_filenames[index])).convert('RGB')
        b = Image.open(join(self.b_path, self.image_filenames[index])).convert('RGB')
        a = a.resize((286, 286), Image.BICUBIC)
        b = b.resize((286, 286), Image.BICUBIC)

        a = transforms.ToTensor()(a)
        b = transforms.ToTensor()(b)
        w_offset = random.randint(0, max(0, 286 - 256 - 1))
        h_offset = random.randint(0, max(0, 286 - 256 - 1))
    
        a = a[:, h_offset:h_offset + 256, w_offset:w_offset + 256]
        b = b[:, h_offset:h_offset + 256, w_offset:w_offset + 256]
    
        a = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(a)
        b = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(b)

        if random.random() < 0.5:
            idx = [i for i in range(a.size(2) - 1, -1, -1)]
            idx = torch.LongTensor(idx)
            a = a.index_select(2, idx)
            b = b.index_select(2, idx)

        if self.direction == "a2b":
            return a, b
        else:
            return b, a

But in my case, I want to use UInt16 images (max color value : 65535) , so, if I want to normalize UInt16 images I just need to replace 256 by 65536 or do I need to do other things ?