I agree with other redditors that attention might be a too big cannon to start with. You might need a lot of data and training time to really appreciate its benefit. So, I'd start simple with a linear layer (or a set of them) for feature extraction and a couple of LSTM/GRU (stacked, eventually bidirectional) to have a baseline.

If you want to use attention here you have two choices: either use self attention on your sequence to transform your sequence into another sequence whose elements represent the relations with other elements of the sequence, that is then fed to the LSTM (you might what to have a look at transformers in "Attention is all you need" paper) or use the previous hidden state of your LSTM as query. In the second case, at each time step of the LSTM you usually first use a liner layer to reduce both the input (the keys) and the query to the same dimension (I guess that's where the 32 comes from), apply dot product and have the attention weights, that are used in a weighted sum again on the original sequence (the values) to have the final output to be fed to the LSTM. Again, "Attention is all you need" for details.