Support torch validation and add to spmd tests

[ethanglaser]

Description

---

Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

Performance

• I have measured performance for affected algorithms using scikit-learn_bench and provided at least a summary table with measured data, if performance change is expected.
• I have provided justification why performance and/or quality metrics have changed or why changes are not expected.
• I have extended the benchmarking suite and provided a corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.

[ethanglaser]

/intelci: run

[codecov]

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✅ All modified and coverable lines are covered by tests.

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[icfaust]

should probably be documented to prevent confusion

[icfaust]

I assume the dpc runtime mismatch issue between torch and dpnp is solved? otherwise this may make things difficult. good addition

[david-cortes-intel]

/intelci: run

[david-cortes-intel]

The CI error:

        elif target_df == "torch":
            if hasattr(torch, "xpu") and torch.xpu.is_available():
                return torch.as_tensor(obj, device="xpu", *args, **kwargs)
            else:
>               return torch.as_tensor(obj, device="cpu", *args, **kwargs)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E               TypeError: as_tensor(): argument 'dtype' must be torch.dtype, not type

[ethanglaser]

I assume the dpc runtime mismatch issue between torch and dpnp is solved? otherwise this may make things difficult. good addition

If installing both torch and dpnp without oneapi deps, there are no issues

[yuejiaointel]

It seems we are testing np.float32/np.float64 dtypes in convert_to_dataframe and torch does not take this, maybe we need to conver to torch dtype before passing to as_tensor

[icfaust]

I assume the dpc runtime mismatch issue between torch and dpnp is solved? otherwise this may make things difficult. good addition

If installing both torch and dpnp without oneapi deps, there are no issues

the release schedules are not matching which caused previous issues in one of the oneAPI minor releases. may break things for a bit at 2026.0 release w. r. t. torch