Positional encodings are used to determine the order of tokens in the input sequence and are added to the output of the self$-$attention layer before feeding it into the feed$-$forward neural networks.

Positional encodings are applied after the feed$-$forward neural networks to determine the order of tokens in the output sequence, ensuring correct alignment with the input sequence.

Positional encodings are used to adjust the weights of self$-$attention mechanisms, emphasizing the importance of certain tokens based on their position in the sequence.

Positional encodings are introduced before the self$-$attention layers to provide the model with information about the order of tokens in the input sequence.