I need to roll a tensor and compute a norm for a definite number of times. This is pretty straightforward to do by using a for loop shown as shown bellow -

```
for i in range(k):
y = torch.roll(y, shifts, dims)
x = torch.norm(y - x, dim)
```

But for my use case, this tensor is huge in size and the entire set of operations needs to be done large number of times. Also I need to do this computation inside a loss function. So using a loop is taking quite some time in terms of computation and that is becoming a considerable bottleneck. Is there any way to avoid using the for loop to perform this task, perhaps using PyTorchâ€™s operations? Any suggestions would be highly appreciated.