Hello, I’m studying how the autograd in PyTorch works, and I find something confusing:

I’ve learned that if the backward() is called more than once, there will be an runtime error since the graph has been freed:

```
x = torch.tensor([1., 2.], requires_grad=True)
y = torch.tensor([3., 4.], requires_grad=True)
z = (x * y).sum()
z.backward()
z.backward() # <-- this will raise an RuntimeError
```

However, I find that if the graph contains only additions, then the backward() can be called multiple times without errors:

```
x = torch.tensor([1., 2.], requires_grad=True)
y = torch.tensor([3., 4.], requires_grad=True)
z = (x + y).sum()
z.backward()
z.backward() # <-- this will NOT cause any error
```

Why is there such a difference?