GRUCell
class torch.nn.GRUCell(input_size, hidden_size, bias=True, device=None, dtype=None)
A gated recurrent unit (GRU) cell
  \begin{array}{ll} r = \sigma(W_{ir} x + b_{ir} + W_{hr} h +
  b_{hr}) \\ z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\
  n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\ h' =
  (1 - z) * n + z * h \end{array}

where \sigma is the sigmoid function, and * is the Hadamard
   product.
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"

Inputs: input, hidden
      * input : tensor containing input features
  * **hidden** : tensor containing the initial hidden state for
    each element in the batch. Defaults to zero if not provided.

Outputs: h'