Hi,

I have two convolutions which have the same shared weight. One is using dilation=1, the other is using dilation=2.

Let `x`

be an input tensor, and `w`

the shared weight.

Is there anything I can do to save memory when calculating:

```
y = F.conv2d(x, weight=w, dilation=1) + F.conv2d(x, weight=w, dilation=2)
```

and doing the backward pass?

Right now the memory consumption is doubled which feels unnecessary.