Thanks for replying.
The second ques was to clarify the dilemma. Normalize can not be implemented on a non-Tensor but applying it after ToTensor() changes the value of [0, 1] to [2.6 to -2.11] or something like that.
For example, the one used [here](torchvision.transforms.transforms — Torchvision 0.10.0 documentation)%2C)
 transform = transforms.Compose([
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
         transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)))

If I use like that and visualize the patch fed to the network as input looks like:

Without normalization:

Visualizing the inputs with the following codes:
    unloader = T.ToPILImage()
    def tensor_to_PIL(tensor):
        image = tensor.cpu().clone()
        image = image.squeeze(0)
        image = unloader(image)
        return image
    #
    # print(x.shape, y.shape, torch.unique(y), torch.max(x), torch.min(x), torch.unique(y[:, 0, ...]))
    Pimage = tensor_to_PIL(x[32, ...]) # x size --> 64, 224, 224, 3 
    plt.imshow(Pimage)