viable/strict/1775044984: Skip failing tests on xpu with complex dtype on windows (#165049)

Intel engineer Adam Stachowicz has pushed a critical fix to the PyTorch machine learning framework, addressing a Windows-specific bug in Intel's XPU (discrete GPU) support. The issue, tracked as PyTorch pull request #165049, originates in how Microsoft's Visual C++ compiler (MSVC) handles complex number trigonometry. Specifically, when calculating the hyperbolic sine (`sinh`) of complex infinity, MSVC returns `(infinity, infinity)` instead of the ISO C standard (9899) mandated result of `(infinity, NaN)`. This deviation causes tests that use NumPy—which correctly follows the ISO standard—as a reference to fail.

The fix is pragmatic: it skips the failing tests on Windows for the XPU backend, mirroring an existing workaround already in place for CPU operations. The core problem lies in compiler-level mathematical libraries, not PyTorch's or Intel's code. As noted in the related Intel `torch-xpu-ops` issue, the Windows compiler's implementation of these complex functions is "not conformant" with the international standard. This patch ensures developers using Intel GPUs on Windows for AI workloads won't encounter false test failures due to this underlying compiler discrepancy, maintaining development velocity while the deeper compiler issue persists.