Hi,

I need to calculate the hessian with respect to activations (intermediate values) for a small network like this? Is there a way to do that using pytorch?

class Net(nn.Module):

```
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2,2)
self.s1 = nn.Sigmoid()
self.fc2 = nn.Linear(2,2)
self.s2 = nn.Sigmoid()
self.fc1.weight = torch.nn.Parameter(torch.Tensor([[0.15,0.2],[0.250,0.30]]))
self.fc1.bias = torch.nn.Parameter(torch.Tensor([0.35]))
self.fc2.weight = torch.nn.Parameter(torch.Tensor([[0.4,0.45],[0.5,0.55]]))
self.fc2.bias = torch.nn.Parameter(torch.Tensor([0.6]))
def forward(self, x):
x= self.fc1(x)
x = self.s1(x)
x= self.fc2(x)
x = self.s2(x)
return x
net = Net()
data = torch.Tensor([0.05,0.1])
out = net(data)
target = torch.Tensor([0.01,0.99]) # a dummy target, for example
criterion = nn.MSELoss()
loss = criterion(out, target); loss
```