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# 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)

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