In the latest **TensorFlow 2.1**, the `tensorflow.keras.layers` submodule contains `AdditiveAttention()` and `Attention()` layers, implementing Bahdanau and Luong's attentions, respectively. (docs [here](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AdditiveAttention) and [here](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Attention).)

These new type of layers require `query`, `value` and `key` inputs (the latest is optional though). However, Query, Value, Key vectors are something I've always read referred to Transformer architectures.

What do these vectors represent, when it comes to Bahdanau and Luong attention? For example, if I want to train an RNN model for a common task (let's say time series forecast), what would these inputs represent?

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EDIT:
I'm thinking about a seq2seq to make forecasts. The input would be a series of given length, and a series of external variables. The output would be the series shifted forward of n steps.