How do I get the Latent Space Representation from a LSTM based Autoencoder?

Question

I'm trying to get the latent space from an autoencoder in order to build a plot and watch its behavior. I don't really know if I can get it from the RepeatVector or if I have to add a Dense layer.

Here is my code:

model = Sequential()
input_shape = (X_train.shape[1], X_train.shape[2]) 
model.add(LSTM(16, activation='relu', return_sequences=True, input_shape=input_shape)) #Encoder
model.add(LSTM(4, activation='relu', return_sequences=False)) #Encoder
model.add(RepeatVector(X_train.shape[1])) #Latent
model.add(LSTM(4, activation='relu', return_sequences=True)) #Decoder
model.add(LSTM(16, activation='relu', return_sequences=False)) #Decoder
model.add(TimeDistributed(Dense(X_train.shape[2]))) #Decoder

How do I get the Latent Space Representation?