How to add attention mechanism to my sequence-to-sequence architecture in Keras?

Based on this blog entry, I have written a sequence to sequence deep learning model in Keras:

model = Sequential()
model.add(LSTM(hidden_nodes, input_shape=(n_timesteps, n_features)))
model.add(RepeatVector(n_timesteps))
model.add(LSTM(hidden_nodes, return_sequences=True))
model.add(TimeDistributed(Dense(n_features, activation='softmax')))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=30, batch_size=32)

It works reasonably well, but I intend to improve it by applying attention mechanism. The aforementioned blog post includes a variation of the architecture with it by relying on a custom attention code, but it doesn’t work my present TensorFlow/Keras versions, and anyway, to my best knowledge, recently a generic attention has been added to Keras — I was not able add it to my code, however.

Additionally, I tried to complicate my architecture above by adding 2-2 LSTM layers for the encoder and the decoder respectively instead of 1-1 with this:

model = Sequential()
model.add(LSTM(hidden_nodes, return_sequences=True, input_shape=(n_timesteps, n_features)))
model.add(LSTM(hidden_nodes, return_sequences=True))
model.add(RepeatVector(n_timesteps))
model.add(LSTM(hidden_nodes, return_sequences=True))
model.add(LSTM(hidden_nodes, return_sequences=True))
model.add(TimeDistributed(Dense(n_features, activation='softmax')))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=100, validation_split=0.15, batch_size=32)

but I get error message (in the 2nd or 3rd row, I assume):

ValueError: Input 0 of layer repeat_vector_17 is incompatible with the layer: expected ndim=2, found ndim=3. Full shape received: [None, 20, 128]

What could be the reason here?

model = Sequential()
model.add(LSTM(hidden_nodes, input_shape=(n_timesteps, n_features)))
##output shape is (n_features)

model.add(RepeatVector(n_timesteps))
##now shape becomes (n_timesteps,n_features)