How to define Output Dimension in NCP/LTC network?

[Mariam-124]

Hi , I am trying to use a multivariant LST model for stock price prediction , and that model defines output dimension which is different from input dimension.
But I am unable to understand how to add output dimension in NCP.

Previous model:
model = Sequential()
model.add(LSTM(64, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))
model.add(LSTM(32, activation='relu', return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(trainY.shape[1]))

My LTC Model is

wiring = wirings.AutoNCP(8,1) # 8 neurons in total, 1 output (motor neuron)
model = keras.models.Sequential(
[
keras.layers.InputLayer(input_shape=(None, 5)),
# here we could potentially add layers before and after the LTC network
LTC(wiring, return_sequences=True),
]
)
model.compile(
optimizer=keras.optimizers.Adam(0.01), loss='mean_squared_error'
)

model.summary()

as I want my output to be 2 dimensional but this gives same output dimensions as input dimensions a 3

so please guide me as examples in docs only shows same input output dimensions