for position one and you have a different vector for position two and you have a different vector for position three right so um but across all of the data points these vectors are going to be the same so the vector one is always going to be that same vector for all of the data points so the model somehow must learn independent of the data point what it means to be in position one so the model must learn how it wants to fill that vector that's called a learned positional embeddings we've seen this in many models so far it usually works