Thank you again for your answer. I understand that while I will be still able to learn the classifying distribution but I will be loosing the perspective of differences between images which is further emphasized by the plots. However, I do get the intuition why would we loose the value of difference between images?
I currently do the following
# img_tensor is of shape C x H x W
mean_vals= img_tensor.view(img_tensor.shape[0], -1).mean(dim=1)
std_vals = img_tensor.view(img_tensor.shape[0], -1).std(dim=1)
		
normalizer = transforms.Normalize(mean=mean_vals, std=std_vals) 
img_tensor = normalizer(img_tensor)    -- (1)

instead of
# Consider the images are from ImageNet
mean_vals = [0.485, 0.456, 0.406]
std_vals = [0.229, 0.224, 0.225]
		
normalizer = transforms.Normalize(mean=mean_vals, std=std_vals) 
img_tensor = normalizer(img_tensor)    -- (2)

You are saying the (1) would give me middle plot and (2) would give me the first or third plot.