test_memleak_when_graph_input_has_tensor_attr_xpu - AssertionError: Scalars are not equal!

### 🐛 Describe the bug

1 UT fails on Windows LNL with "AssertionError: Scalars are not equal"
Version | Status |
-- | -- |
pytorch_2.15_nightly_20260903 | Passed |
pytorch_2.15_nightly_20260907 | Failed |

### Affected Test Cases

```
..\..\..\..\test\dynamo\test_repros.py::ReproTestsDeviceXPU::test_memleak_when_graph_input_has_tensor_attr_xpu
```

Error log

```
____ ReproTestsDeviceXPU.test_memleak_when_graph_input_has_tensor_attr_xpu ____
[gw1] win32 -- Python 3.12.14 C:\Users\gta\miniforge3\envs\pytorch_2.15_nightly_20260907\python.exe
Traceback (most recent call last):
File "C:\Users\gta\repositories\pytorch\pytorch\test\dynamo\test_repros.py", line 9243, in test_memleak_when_graph_input_has_tensor_attr
self.assertEqual(mem_before, mem_after)
File "C:\Users\gta\miniforge3\envs\pytorch_2.15_nightly_20260907\Lib\site-packages\torch\_dynamo\test_case.py", line 128, in assertEqual
return super().assertEqual(x, y, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\gta\miniforge3\envs\pytorch_2.15_nightly_20260907\Lib\site-packages\torch\testing\_internal\common_utils.py", line 4937, in assertEqual
raise error_metas.pop()[0].to_error( # type: ignore[index]
AssertionError: Scalars are not equal!

Expected 512 but got 1024.
Absolute difference: 512
Relative difference: 1.0
```

### Versions

Click to expand traceback
PyTorch version: 2.15.0.dev20260907+xpu
Is debug build: False
CUDA used to build PyTorch: None
ROCm SDK used to build PyTorch: N/A
HIP used to build PyTorch: N/A

OS: Microsoft Windows 11 Pro (10.0.26200 64-bit)
GCC version: Could not collect
Clang version: Could not collect
CMake version: version 3.31.6
Libc version: N/A

Python version: 3.12.14 | packaged by conda-forge | (main, Sep 2 2026, 23:18:20) [MSC v.1944 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-11-10.0.26200-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20260100
Intel GPU driver version:
* 32.0.101.8992 (20260831000000.******+***)
Intel GPU models onboard:
* Intel(R) Arc(TM) 140V GPU (16GB)
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Arc(TM) 140V GPU (16GB)', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero V2', type='gpu', device_id=0x64A0, uuid=8680a064-0400-0000-0002-000000000000, driver_version='1.15.39183+4', total_memory=16870MB, local_mem_size=128KB, last_level_cache_size=8192KB, max_compute_units=64, memory_clock_rate=0MHz, memory_bus_width=64-bit, gpu_eu_count=64, gpu_subslice_count=8, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1, is_integrated_gpu=1)
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: False
Caching allocator config: N/A

CPU:
Name: Intel(R) Core(TM) Ultra 7 258V
Manufacturer: GenuineIntel
Family: 774
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 2200
MaxClockSpeed: 2200
L2CacheSize: 14336
L2CacheSpeed: None
Revision: None

Versions of relevant libraries:
[pip3] dpcpp-cpp-rt==2026.1.0
[pip3] intel-cmplr-lib-rt==2026.1.0
[pip3] intel-cmplr-lib-ur==2026.1.0
[pip3] intel-cmplr-lic-rt==2026.1.0
[pip3] intel-opencl-rt==2026.1.0
[pip3] intel-openmp==2026.1.0
[pip3] intel-pti==1.0.1
[pip3] intel-sycl-rt==2026.1.0
[pip3] mkl==2026.1.0
[pip3] mkl-include==2024.2.0
[pip3] mkl-static==2024.2.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] onemkl-license==2026.1.0
[pip3] onemkl-sycl-blas==2026.1.0
[pip3] onemkl-sycl-dft==2026.1.0
[pip3] onemkl-sycl-lapack==2026.1.0
[pip3] onemkl-sycl-rng==2026.1.0
[pip3] onemkl-sycl-sparse==2026.1.0
[pip3] onnx==1.21.0
[pip3] onnx-ir==0.1.16
[pip3] onnxscript==0.6.2
[pip3] optree==0.13.0
[pip3] tbb==2023.1.0
[pip3] tbb-devel==2021.13.1
[pip3] tcmlib==1.5.0
[pip3] torch==2.15.0.dev20260907+xpu
[pip3] torchaudio==2.11.0.dev20260907+xpu
[pip3] torchvision==0.30.0.dev20260907+xpu
[pip3] triton-xpu==3.8.0+git1e2d42a0
[pip3] umf==1.1.0
[conda] dpcpp-cpp-rt 2026.1.0 pypi_0 pypi
[conda] intel-cmplr-lib-rt 2026.1.0 pypi_0 pypi
[conda] intel-cmplr-lib-ur 2026.1.0 pypi_0 pypi
[conda] intel-cmplr-lic-rt 2026.1.0 pypi_0 pypi
[conda] intel-opencl-rt 2026.1.0 pypi_0 pypi
[conda] intel-openmp 2026.1.0 pypi_0 pypi
[conda] intel-pti 1.0.1 pypi_0 pypi
[conda] intel-sycl-rt 2026.1.0 pypi_0 pypi
[conda] mkl 2026.1.0 pypi_0 pypi
[conda] mkl-include 2024.2.0 pypi_0 pypi
[conda] mkl-static 2024.2.0 pypi_0 pypi
[conda] numpy 1.26.4 pypi_0 pypi
[conda] onemkl-license 2026.1.0 pypi_0 pypi
[conda] onemkl-sycl-blas 2026.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2026.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2026.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2026.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2026.1.0 pypi_0 pypi
[conda] optree 0.13.0 pypi_0 pypi
[conda] tbb 2023.1.0 pypi_0 pypi
[conda] tbb-devel 2021.13.1 pypi_0 pypi
[conda] tcmlib 1.5.0 pypi_0 pypi
[conda] torch 2.15.0.dev20260907+xpu pypi_0 pypi
[conda] torchaudio 2.11.0.dev20260907+xpu pypi_0 pypi
[conda] torchvision 0.30.0.dev20260907+xpu pypi_0 pypi
[conda] triton-xpu 3.8.0+git1e2d42a0 pypi_0 pypi
[conda] umf 1.1.0 pypi_0 pypi

Start with test/dynamo/test_repros.py at ReproTestsDeviceXPU.test_memleak_when_graph_input_has_tensor_attr_xpu, especially the mem_before and mem_after checks around line 9243. Run this test on Windows with an XPU environment and compare the passing and failing nightly versions. Done means the regression is understood and the test correctly passes without the 512-byte memory discrepancy.

Written by the indexing model from the issue text.