Assuming your hidden dimension is the same size as your input dimension, you can do it like so:
rnn = torch.nn.RNN(16, 16)
x1 = torch.randn((1, 1, 16))

y1,h1 = rnn(x1)
y2,h2 = rnn(y1, h1)
...

The output of the RNN is a sequence of outputs produced and the last hidden state. The RNN layer can take in a sequence of inputs and a hidden state (default to all zeros for the first forward).