\mathcal{U}(-\sqrt{k}, \sqrt{k}) where k =
     \frac{1}{\text{hidden_size}}
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.
Examples:
  >>> rnn = nn.GRUCell(10, 20)
  >>> input = torch.randn(6, 3, 10)
  >>> hx = torch.randn(3, 20)
  >>> output = []
  >>> for i in range(6):
  ...     hx = rnn(input[i], hx)
  ...     output.append(hx)
