Based on the paper 'Attention is All You Need,' practice writing a transformer program, focusing on

In the field of natural language processing, a key component of Transformer models is the Positional Encoding layer. This layer is used to describe the position or order of entities in a sequence, giving each position a unique representation. In Transformer models, simply using index values to represent positions is insufficient because the index values can become very large for long sequences. Additionally, normalizing index values between 0 and 1 can cause issues, especially when the sequence length varies, as the normalization approach would change.

To address these issues, Transformer models employ a clever encoding scheme using positional encoding vectors. The output of the positional encoding layer is a matrix where each row represents the encoding of an object in the sequence, containing information about both the object itself and its position.