`torch.compile` (inductor) crashes on in-place complex foreach-divide (public `torch.foreach.div_` and private `torch._foreach_div_`)

### 🐛 Describe the bug

In-place foreach-divide on a list of **complex** tensors runs correctly in eager but crashes
`torch.compile` (inductor) with an internal invariant assertion. This happens with **both** the
public beta `torch.foreach.div_` and the private `torch._foreach_div_` — same error:

```
torch._inductor.exc.InductorError: AssertionError: is not an OpOverload
```

## Minimal repro

```python
import torch

def fn(tensors):
torch.foreach.div_(tensors, 1j) # PUBLIC beta API; in-place foreach divide by a complex scalar

tensors = [torch.tensor([1 + 2j, 3 + 4j], dtype=torch.complex64)]

fn(tensors) # eager: OK -> [2-1j, 4-3j]
torch.compile(fn, backend="inductor")( # compiled: InductorError (getitem is not an OpOverload)
[torch.tensor([1 + 2j, 3 + 4j], dtype=torch.complex64)]
)

# The private torch._foreach_div_(tensors, 1j) crashes identically (same InductorError),
# so this is not specific to the private API.
```

Scope (what does / doesn't crash) — same for the public `torch.foreach.div_` and private `torch._foreach_div_`:

| variant | dtype | compiled |
|---|---|---|
| in-place `div_` | complex64 | **CRASH** |
| out-of-place `div` | complex64 | OK |
| in-place `div_` | float32 | OK |

Reproduces on **CPU and CUDA**. Also crashes for an in-place divide by a complex tensor list
(not just a scalar).

## Expected behavior

Compiling an in-place complex `_foreach_div_` should match eager (or raise a graceful error),
not hit an internal inductor invariant. The out-of-place and float paths already compile, so
the in-place complex lowering is the gap.

## Actual behavior

Eager divides in place correctly (`[2-1j, 4-3j]`). Under inductor the compilation fails with
`AssertionError: is not an OpOverload` before running.

### Versions

```
PyTorch version: 2.15.0a0+gite67e43b
Is debug build: True
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04.3) 12.3.0
Clang version: 17.0.6
CMake version: version 4.0.0
Libc version: glibc-2.35

Python version: 3.12.13 | packaged by conda-forge (64-bit runtime)
Python platform: Linux-6.8.0-138-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.6.20
GPU 0: NVIDIA RTX A6000
Nvidia driver version: 580.82.07
cuDNN version: 9.3.0

[pip3] numpy==2.3.5
[pip3] torch==2.15.0a0+gite67e43b
[pip3] triton==3.7.1
```

cc @ezyang @anjali411 @dylanbespalko @mruberry @nikitaved @amjames @crcrpar @mcarilli @janeyx99 @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @aakhundov @coconutruben @jataylo

Run the minimal repro through torch.compile with backend="inductor", testing both torch.foreach.div_ and torch._foreach_div_ alongside the listed eager, out-of-place, and float variants. Trace the inductor lowering for in-place complex foreach division; done means CPU and CUDA compilation succeeds without the getitem assertion and matches eager results.

Written by the indexing model from the issue text.