Hello everyone, I am doing a research project which requires to use sum of two loss functions.

Below is a snippet of my code:

```
for epoch in range(epochs):
e = epoch
model.train()
running_loss = 0
for i, data in enumerate(train_loader):
#scheduler.step()
inputs, labels = data['points'].to(device), data['labels'].to(device)
#Discriminator
#Real image
inputs2 = torch.cat((inputs, labels.type(torch.float64).reshape(1,2048,1)),axis=2)
output = disc(inputs2.transpose(1,2).float())
ls_real = disc_loss(output,num=1)
optimizerD.zero_grad()
ls_real.backward()
#Prediction
output = model(inputs.transpose(1,2),onehot)
output_pred = output
_,pred = torch.max(output.data,1)
#criterion = torch.nn.CrossEntropyLoss()
#loss = criterion(output, labels)
#Fake image
inputs2 = torch.cat((inputs, pred.type(torch.float64).reshape(1,2048,1)),axis=2)
output = disc(inputs2.transpose(1,2).float())
ls_fake = disc_loss(output, num=0)
ls_fake.backward(retain_graph=True)
ls_D = ls_real + ls_fake
optimizerD.step()
optimizer.zero_grad()
loss = main_loss(output_pred,labels)
loss = loss + ls_fake
loss.backward()
optimizer.step()
```

However, I get an error at **loss.backward()** after loss = loss + ls_fake with this error message:

**RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [256, 2]], which is output 0 of TBackward, is at version 11; expected version 9 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).**

The output of **loss** is different to **ls_fake**, so the model for **ls_fake** has two outputs and the model for **loss** has four outputs.

Please can someone help me with this problem? Thank you!