You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: This work presents the design and implementation of GNMT, a production NMTsystem at Google, that aims toprovide solutions to the above problems. In our implementation, therecurrent networks are Long Short-Term Memory (LSTM)RNNs [23, 17]. Our LSTM RNNs have 8layers, with residual connections between layers to encourage gradientflow [21]. For parallelism, we connect the attention fromthe bottom layer of the decoder network to the top layer of theencoder network. To improve inference time, we employ low-precisionarithmetic for inference, which is further accelerated by specialhardware (Google’s Tensor Processing Unit, or TPU). To effectivelydeal with rare words, we use sub-word units (also known as“wordpieces”) [35] for inputs and outputs inour system. Using wordpieces gives a good balance between theflexibility of single characters and the efficiency of full words fordecoding, and also sidesteps the need for special treatment of unknownwords. Our beam search technique includes a length normalization procedure todeal efficiently with the problem of comparing hypotheses of differentlengths during decoding, and a coverage penalty to encourage the modelto translate all of the provided input. \begin{split}\mathbf{c}_{t}^{i},\mathbf{m}_{t}^{i}&=\mathrm{LSTM}_{i}(\mathbf{c}_{t-1}^{i},\mathbf{m}_{t-1}^{i},\mathbf{x}_{t}^{i-1};\mathbf{W}^{i})\\\mathbf{x}_{t}^{i}&=\mathbf{m}_{t}^{i}+\mathbf{x}_{t}^{i-1}\\\mathbf{c}_{t}^{i+1},\mathbf{m}_{t}^{i+1}&=\mathrm{LSTM}_{i+1}(\mathbf{c}_{t-1}^{i+1},\mathbf{m}_{t-1}^{i+1},\mathbf{x}_{t}^{i};\mathbf{W}^{i+1})\end{split}(6)Residual connections greatly improve the gradient flow in the backwardpass, which allows us to train very deep encoder and decodernetworks. In most of our experiments, we use 8 LSTM layers for the encoderand decoder, though residual connections can allow us to trainsubstantially deeper networks (similar to what was observedin [45]). In all experiments, our models consist of 8 encoder layers and 8 decoder layers.(Since the bottom encoder layer is actually bi-directional, in total there are9 logically distinct LSTM passes in the encoder.)The attention network is a simple feedforward network with one hidden layer with 1024 nodes.All of the models use 1024 LSTM nodes per encoder and decoder layers.

Question: In the model architecture described in this paper, how many residual connections are used?
Authors used 8 LSTM layers for the encoder, and 8 LSTM layers for the decoder with residual connections for both networks, each layer has 1024 node. though it's not very clear how many residual connections are but a possible answer is 16384.