I tried the code below from http://pytorch.org/docs/master/nn.html?highlight=lstm#torch.nn.LSTM 19
rnn = nn.LSTM(10, 20, 2)
i = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
c0 = torch.randn(2, 3, 20)
output, hn = rnn(i, (h0, c0))

But got this error
RuntimeError                              Traceback (most recent call last)
<ipython-input-2-573e79300cd2> in <module>()
      3 h0 = torch.randn(2, 3, 20)
      4 c0 = torch.randn(2, 3, 20)
----> 5 output, hn = rnn(i, (h0, c0))

~\Anaconda3\envs\dl\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    355             result = self._slow_forward(*input, **kwargs)
    356         else:
--> 357             result = self.forward(*input, **kwargs)
    358         for hook in self._forward_hooks.values():
    359             hook_result = hook(self, input, result)

~\Anaconda3\envs\dl\lib\site-packages\torch\nn\modules\rnn.py in forward(self, input, hx)
    202             flat_weight=flat_weight
    203         )
--> 204         output, hidden = func(input, self.all_weights, hx)
    205         if is_packed:
    206             output = PackedSequence(output, batch_sizes)

~\Anaconda3\envs\dl\lib\site-packages\torch\nn\_functions\rnn.py in forward(input, *fargs, **fkwargs)
    371 def RNN(*args, **kwargs):
    372     def forward(input, *fargs, **fkwargs):
--> 373         if cudnn.is_acceptable(input.data):
    374             func = CudnnRNN(*args, **kwargs)
    375         else:

~\Anaconda3\envs\dl\lib\site-packages\torch\tensor.py in data(self)
    405     @property
    406     def data(self):
--> 407         raise RuntimeError('cannot call .data on a torch.Tensor: did you intend to use autograd.Variable?')
    408 
    409     # Numpy array interface, to support `numpy.asarray(tensor) -> ndarray`

RuntimeError: cannot call .data on a torch.Tensor: did you intend to use autograd.Variable?