RNN
class torch.nn.RNN(args, *kwargs)
Applies a multi-layer Elman RNN with \tanh or \text{ReLU} non-
   linearity to an input sequence.
For each element in the input sequence, each layer computes the
   following function:
  h_t = \tanh(x_t W_{ih}^T + b_{ih} + h_{t-1}W_{hh}^T + b_{hh})

where h_t is the hidden state at time t, x_t is the input at time
   t, and h_{(t-1)} is the hidden state of the previous layer at
   time t-1 or the initial hidden state at time 0. If
   "nonlinearity" is "'relu'", then \text{ReLU} is used instead of
   \tanh.
Parameters:
      * input_size -- The number of expected features in the input
        x
  * **hidden_size** -- The number of features in the hidden state
    *h*

  * **num_layers** -- Number of recurrent layers. E.g., setting
    "num_layers=2" would mean stacking two RNNs together to form a
    *stacked RNN*, with the second RNN taking in outputs of the
