Is there any solution making nearest interpolation do not return zero grad?

autograd
2

I see that nearest mode performs gradient backward correctly in the following example:

import torch, torch.nn.functionasl as F
x = torch.randn(1,1,5,5,requires_grad=True)
y = F.interpolate(x, size=(10,10), mode='nearest')
y.sum().backward()
print(x.grad)

Can you check if any other operation that you use breaks the computation graph or if the gradients are really 0 from the later layers?