not this image and also put it through the network maybe also augment it first and then you have Z 3 so now you have the outputs of 2 things that are supposed to come from the same image and one thing that's supposed to come from a different image and now your loss is simply going to be make those two things close together and push those two things apart or those 3 actually so the loss and this is this is the contrastive loss of self supervised learning as you know you don't need any labels right here you simply say the things that come from the