<ipython-input-61-78910da2943e> in forward(self, x)
     18         c0 = torch.randn(self.num_layers*2, int(x.size(0)), self.hidden_size).to(device)
     19         # Forward propagate LSTM
---> 20         out, (hn, cn)  = self.lstm(x, (h0, c0))#(h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size*2)
     21         # Decode the hidden state of the last time step
     22         out = self.fc(out[:, -1, :])

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
--> 495             hook_result = hook(self, input, result)
    496             if hook_result is not None:
    497                 raise RuntimeError(

/usr/local/lib/python3.6/dist-packages/torchsummary/torchsummary.py in hook(module, input, output)
     21             if isinstance(output, (list, tuple)):
     22                 summary[m_key]["output_shape"] = [
---> 23                     [-1] + list(o.size())[1:] for o in output
     24                 ]
     25             else:

/usr/local/lib/python3.6/dist-packages/torchsummary/torchsummary.py in <listcomp>(.0)
     21             if isinstance(output, (list, tuple)):
     22                 summary[m_key]["output_shape"] = [
---> 23                     [-1] + list(o.size())[1:] for o in output
     24                 ]
     25             else:

AttributeError: 'tuple' object has no attribute 'size'```