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)