keras: LSTM many to many mapping problem

I want to implement the forth architecture from the left (first many to many):

I my case the length of input and outputs aren’t equal (I mean that number of blue and red units aren’t equal)! I have n samples to train the NN then the shape of input is nn_prev1 and the out shape is nn_nxt1. My model is as follows:

batch_size = 50 
n_prev = 100
n_nxt = 5
print("creating model...")
unit_number = n_nxt
model = Sequential()
model.add(LSTM(unit_number,
           batch_input_shape=(batch_size, n_prev, 1),
           forget_bias_init='one',
           return_sequences=True,
           stateful=True))
model.add(Dropout(0.2))
model.add(LSTM(unit_number,
           batch_input_shape=(batch_size, n_prev, 1),
           forget_bias_init='one',
           return_sequences=False,
           stateful=True))
model.add(Dropout(0.2))
model.add(Dense(n_nxt))
model.compile(loss='mse', optimizer='rmsprop')


print('Training')
numIteration = len(X_train)/batch_size
for i in range(epochs):
    print('Epoch', i, '/', epochs)
    for j in range(numIteration):
        print('Batch', j, '/',numIteration,'Epoch', i)
        model.train_on_batch(X_train[j*batch_size:j*batch_size+batch_size,], y_train[j*batch_size:j*batch_size+batch_size,])

    model.reset_states()


print('Predicting')
predicted_output = model.predict(X_test, batch_size=batch_size)

But I think I have implemented the third one (because return_sequences=False in the second LSTM layer, according to this article: https://github.com/Vict0rSch/deep_learning/tree/master/keras/recurrent ). I think I have one output from the second LSTM layer like third architecture (from the left) and then it is extended to five outputs by Dense layer. When I set return_sequences=True in the second LSTM layer I get the following error:

Traceback (most recent call last):
 File "/home/mina/Documents/research/one cores (LSTM) seq2seq  one core vector2vector  train on batch statefull 15/KerasLSTM.py", line 259, in <module>
main()
 File "/home/mina/Documents/research/one cores (LSTM) seq2seq  one core vector2vector  train on batch statefull 15/KerasLSTM.py", line 200, in main
   model.add(Dense(n_nxt))
 File "/usr/local/lib/python2.7/dist-packages/keras/layers/containers.py", line 68, in add
self.layers[-1].set_previous(self.layers[-2])
 File "/usr/local/lib/python2.7/dist-packages/keras/layers/core.py", line 85, in set_previous
str(layer.output_shape))
AssertionError: Incompatible shapes: layer expected input with ndim=2 but previous layer has output_shape (1, 100, 5)

Can I omit dense layer? As I understood If I don’t set return_sequences=True I wil have the third architecture, is it right? Is there any problem that the length of the input is different from the output length?