Hello there!

I was wondering what the correct way is to combine individual losses of elements of a dataset to perform mini-batch gradient descent.

I have an unsupervised loss function myLossFunction that can only process one element of the dataset at a time. So I would like to loop over the predictions and calculate the losses one by one.

- But how do I then combine the losses? Just torch.mean them?
- And is it required to call loss.backward() after calculating each individual loss or is it fine where it is in the example code?

```
dataloader = DataLoader(dataset, batch_size=10)
for batch in dataloader:
predictions = model(batch)
losses = []
for predictions in predictions:
losses.append(myLossFunction(prediction))
# what do I do here to combine the losses?
optimizer.zero_grad()
loss.backward()
optimizer.step()
```

Cheers!