(4hidden_size)*
  * **bias_hh** -- the learnable hidden-hidden bias, of shape
    *(4*hidden_size)*

Note:
 All the weights and biases are initialized from
 \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.LSTMCell(10, 20)  # (input_size, hidden_size)
  >>> input = torch.randn(2, 3, 10)  # (time_steps, batch, input_size)
  >>> hx = torch.randn(3, 20)  # (batch, hidden_size)
  >>> cx = torch.randn(3, 20)
  >>> output = []
  >>> for i in range(input.size()[0]):
  ...     hx, cx = rnn(input[i], (hx, cx))
  ...     output.append(hx)
  >>> output = torch.stack(output, dim=0)
