Saving LSTM hidden states while training and predicting for multi-class time series classification
There didn't seem to be any way to build a working trainable model while also returning the hidden states with return_sequences=True.

The fix I found was to build a predictor model and train it, and save the weights. Then I built a new model which ended with my LSTM layer, and fed it the trained weights. So, using return_sequences=True, I was able to predict on new data and get the data's representations at each hidden state.