the Hadamard product.
In a multilayer LSTM, the input x^{(l)}_t of the l -th layer (l >=
   2) is the hidden state h^{(l-1)}_t of the previous layer multiplied
   by dropout \delta^{(l-1)}_t where each \delta^{(l-1)}_t is a
   Bernoulli random variable which is 0 with probability "dropout".
If "proj_size > 0" is specified, LSTM with projections will be
   used. This changes the LSTM cell in the following way. First, the
   dimension of h_t will be changed from "hidden_size" to "proj_size"
   (dimensions of W_{hi} will be changed accordingly). Second, the
   output hidden state of each layer will be multiplied by a learnable
   projection matrix: h_t = W_{hr}h_t. Note that as a consequence of
   this, the output of LSTM network will be of different shape as
   well. See Inputs/Outputs sections below for exact dimensions of all
   variables. You can find more details in
   https://arxiv.org/abs/1402.1128.
Parameters:
      * input_size -- The number of expected features in the input