Second order computations for nn.Upsample

Hi

I need to compute the approximate hessian for a decoder network. The decoder consists of conv2d and upsample layers. Currently, backpack does not supports nn.Upsample. Since it is a non-parametric layer, it might not be too difficult to implement?

Here I define my model and a data point.

```
from backpack import backpack
from backpack.extensions import DiagGGNExact

model = torch.nn.Sequential(
torch.nn.Conv2d(1,8, kernel_size=3, padding=1),
torch.nn.MaxPool2d(2),
torch.nn.ReLU(),
torch.nn.Conv2d(8,8, kernel_size=3, padding=1),
torch.nn.Upsample(scale_factor=2, mode="nearest"),
torch.nn.ReLU(),
torch.nn.Conv2d(8,1, kernel_size=3, padding=1),
torch.nn.Flatten(),
)
lossfunc = torch.nn.MSELoss()

model = extend(model)
lossfunc = extend(lossfunc)

X = torch.zeros(1,1,8,8)
print(model(X).shape)

b = X.shape[0]
loss = lossfunc(model(X), X.view(b, -1))

with backpack(DiagGGNExact()):
loss.backward()

for param in model.parameters():
print(param.diag_ggn_exact)
```

will return this error

```
NotImplementedError: Extension saving to diag_ggn_exact does not have an extension for Module
```

Could you help implement this feature?