RNNCell
class torch.ao.nn.quantized.dynamic.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', dtype=torch.qint8)
An Elman RNN cell with tanh or ReLU non-linearity. A dynamic
   quantized RNNCell module with floating point tensor as inputs and
   outputs. Weights are quantized to 8 bits. We adopt the same
   interface as torch.nn.RNNCell, please see
   https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell for
   documentation.
Examples:
  >>> rnn = nn.RNNCell(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)
