Thunder's vjp doesn't compute the result for variadic arguments

Nobody has claimed this yet.

## 🐛 Bug

```py
import torch
import thunder

def f(*args):
a, b = args
c = a + b
return c

def ff(*args):
return thunder.core.transforms.value_and_grad(f)(*args)

a = torch.ones(3)
b = torch.ones(3)
cff = thunder.jit(ff)
print(cff(a, b)) # Returns (tensor([2., 2., 2.]), (None, None))
```
There should be ones instead of Nones.

It's probably happening because "unpacking" operations are ignored in https://github.com/Lightning-AI/lightning-thunder/blob/f7b2e1590fd0d98985131c54653b2a95ba4729ed/thunder/core/transforms.py#L2638

Start with thunder/core/transforms.py around line 2638 and run the supplied PyTorch repro for a variadic function passed to value_and_grad and thunder.jit. Done means the result contains tensors of ones instead of (None, None), with coverage for this variadic-argument case.

Written by the indexing model from the issue text.