First, project the input (aka embedding) and reshape to b, t, d. Then apply standard
transformer action. Finally, reshape to image.
When input is discrete: First, input (classes of latent pixels) is converted to embeddings and has positional
embeddings applied, see ImagePositionalEmbeddings. Then apply standard transformer action. Finally, predict
classes of unnoised image.
Note that it is assumed one of the input classes is the masked latent p