Ok ok I understand now
So basically there’s an embedder that takes in the input, resizes it into smaller vector, then this smaller vector is passed to another layer (a dense layer) that then tries to predict the actual vector that was initially inputed.
Hmmmm🤔
One hot encoding might actually be the way forward…but then 272 length one hot vector  might be too sparse
Well try encoding ur data with number labels still so that the shape can go from (5, 272) to (5, 1) then the input will be 1 and the output will be a probability score vector of length = 272 so that the index with the highest probability will be the number of that category.
Eg:
Output: [0.8, 0.1, 0.1] will belong to category 0 coz the highest value is at the 0th index.
Sth like that.
How about that?
What do u think?