RNNCell
class torch.nn.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', device=None, dtype=None)
An Elman RNN cell with tanh or ReLU non-linearity.
  h' = \tanh(W_{ih} x + b_{ih}  +  W_{hh} h + b_{hh})

If "nonlinearity" is 'relu', then ReLU is used in place of tanh.
Parameters:
      * input_size (int) -- The number of expected features in
        the input x
  * **hidden_size** (*int*) -- The number of features in the
    hidden state *h*

  * **bias** (*bool*) -- If "False", then the layer does not use
    bias weights *b_ih* and *b_hh*. Default: "True"

  * **nonlinearity** (*str*) -- The non-linearity to use. Can be
    either "'tanh'" or "'relu'". Default: "'tanh'"

Inputs: input, hidden
      * input: tensor containing input features
  * **hidden**: tensor containing the initial hidden state
    Defaults to zero if not provided.

Outputs: h'