Attention takes in all tokens in the sequence and outputs a new representation of the current token in context. Each layer of the transformer adds more context to the token.

I haven't read this explanation in detail and although they have some nice animations, I wouldn't go to FT to explain machine learning concepts. Here are two well known explanations that might be better:

http://jalammar.github.io/illustrated-transformer/

http://nlp.seas.harvard.edu/annotated-transformer/.