[Bug Skip]: test_numpy_ref_linalg_tensorinv_xpu_float64 — Mismatched elements (66.7%) vs NumPy reference on PVC

## Bug description

`TestCommonXPU.test_numpy_ref_linalg_tensorinv_xpu_float64` fails with a `Tensor-likes are not close!` assertion when comparing `torch.linalg.tensorinv` output against the NumPy reference (`np.linalg.tensorinv`). The error indicates a numerical accuracy issue in the XPU implementation of `linalg.tensorinv`.

## Error

```
Exception: Tensor-likes are not close!

Mismatched elements: 96 / 144 (66.7%)
Greatest absolute difference: 1.6307882880566502 at index (0, 0, 0, 1, 2) (up to 1e-07 allowed)
Greatest relative difference: 107.4749653215916 at index (1, 0, 0, 1, 1) (up to 1e-07 allowed)

Caused by reference input at index 0: SampleInput(input=Tensor[size=(2, 2, 3, 12, 1), device="xpu:0", dtype=torch.float64], args=(), kwargs={'ind': '3'}, broadcasts_input=False, name='')
```

## To reproduce

```
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=0 python test/test_ops.py TestCommonXPU.test_numpy_ref_linalg_tensorinv_xpu_float64
```

## Environment

- **Platform:** PVC (Intel Data Center GPU Max)
- **CI run:** https://hud.pytorch.org/pr/pytorch/pytorch/179550
- **Date:** 2026-07-10
- **PyTorch PR:** https://github.com/pytorch/pytorch/pull/179550

## Additional context

`linalg.tensorinv` depends on SVD or linear solve internally. This may be a numerical accuracy issue in the oneMKL gesvd or related linear algebra path on XPU. The OpInfo for `linalg.tensorinv` has no existing XPU skips — only MPS entries.

Start by running the reproducer in test/test_ops.py: TestCommonXPU.test_numpy_ref_linalg_tensorinv_xpu_float64 with PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=0. Trace the XPU linalg.tensorinv path, including its SVD or linear-solve and oneMKL gesvd-related behavior, then compare against the NumPy reference. Done means the test passes for the reported PVC float64 input within the stated tolerance.

Written by the indexing model from the issue text.