Hello, I am trying to implement my own Bidirectional RNN Model where the *output* is defined as the *sum of hidden states from both directions*. I want to know if autograd can backprop through this summation.

```
def forward(self,x,state=None):
hidden_f = [] # forward direction hidden states
hidden_b = [] # backward direction hidden states
dimensions = x.size()
batch_sz, sequence_len = dimensions[0],dimensions[1]
hidden_sz = self.cell.hidden_size
if state == None:
h_n_f = torch.zeros(batch_sz,hidden_sz)
h_n_b = torch.zeros(batch_sz,hidden_sz)
else:
h_n_f = state[:,:,0]
h_n_b = state[:,:,1]
for i in range(sequence_len):
h_n_f = self.cell(x[:,i,:],h_n_f) # Calculate next hidden state in forward direction
hidden_f.append(h_n_f)
h_n_b = self.cell(x[:,-i-1,:],h_n_b) # Calculate next hidden state in backward direction
hidden_b.append(h_n_b)
# MY PROBLEMS START HERE
# ,requires_grad=True ==> leaf node moved to interior
output = torch.zeros(batch_sz,sequence_len,hidden_sz) # Recording the outputs
#output = variable(torch.zeros(batch_sz,sequence_len,hidden_sz))
# COULD BE PROBLEMATIC FOR BACKPROP
for i in range(sequence_len):
output[:,i,:] = hidden_f[i]+hidden_b[-i-1] # Outputs are defined as the sum of both direction
return output
```