Honor the boundary parameter on the cupy backend in bilateral()

[brendancol]

Closes #3625

• The cupy backend dropped boundary: in the _bilateral dispatcher, cupy_func was the only entry not wrapped with partial(..., boundary=boundary), so 'nearest', 'reflect', and 'wrap' all behaved as 'nan'. Edge-ring results diverged from the other three backends by up to ~30% of a standard deviation on random data.
• Adds _bilateral_cupy_boundary, mirroring _bilateral_numpy_boundary (_pad_array already handles cupy arrays via cupy.pad), and wires it through the dispatcher.
• Adds test_bilateral_boundary_modes_gpu, which compares cupy against numpy for all four modes. The existing assert_boundary_mode_correctness helper only covers numpy vs dask+numpy, which is why this went unnoticed.
• Updates the accuracy-sweep state CSV for the bilateral module.

Backend coverage: numpy, dask+numpy, and dask+cupy are unchanged; cupy now matches numpy to ~5e-14 for every boundary mode.

Test plan:

• pytest xrspatial/tests/test_bilateral.py on a CUDA host: 23 passed (cupy and dask+cupy paths execute, not skipped)
• New GPU boundary test fails without the fix (max diff 2.9 for 'nearest') and passes with it

[brendancol]

PR Review: Honor the boundary parameter on the cupy backend in bilateral()

Blockers (must fix before merge)

None.

Suggestions (should fix, not blocking)

• xrspatial/tests/test_bilateral.py:337 -- the new parity test covers numpy vs cupy for all four modes, but dask+cupy is still only exercised at the default boundary='nan' (test_bilateral_dask_gpu). The dask+cupy path forwards boundary through _boundary_to_dask, so it works today, but nothing pins it. Extending the new test (or the existing dask+cupy test) to loop over VALID_BOUNDARY_MODES would lock all four backends for a few extra lines.

Nits (optional improvements)

• xrspatial/tests/test_bilateral.py:345 -- the for-loop over modes means the first failing mode hides the rest. @pytest.mark.parametrize('mode', VALID_BOUNDARY_MODES) would report each mode separately. The loop does match the style of assert_boundary_mode_correctness, so this is a style call.

What looks good

• _bilateral_cupy_boundary (xrspatial/bilateral.py:188) mirrors _bilateral_numpy_boundary exactly: same pad-filter-slice structure, same 'nan' short-circuit. _pad_array already dispatches to cupy.pad for cupy inputs, so no new GPU code was needed.
• The result[radius:-radius, radius:-radius] slice is safe: radius >= 1 is guaranteed because sigma_spatial is validated against sqrt(finfo.tiny) and ceil(2*sigma) of any accepted value is at least 1, and the extent clamp never drops below 1.
• The dispatcher change (xrspatial/bilateral.py:228) is the minimal fix: cupy_func now gets the same partial(..., boundary=boundary) treatment as the other three entries.
• The regression test fails on main (max diff ~2.9 for 'nearest' on data with std 10) and passes here; cupy now matches numpy to ~5e-14 per the PR body.
• State CSV diff is a single-row change, no whole-file churn.

Checklist

• Algorithm matches reference/paper (pad-filter-crop equivalent to the numpy wrapper)
• All implemented backends produce consistent results (numpy/cupy verified for all modes on a CUDA host; dask+numpy via existing boundary test; dask+cupy at default mode only, see suggestion)
• NaN handling is correct (unchanged; 'nan' path still short-circuits to the clamped-window kernel)
• Edge cases are covered by tests (existing single-cell, all-NaN, oversize-sigma tests still pass)
• Dask chunk boundaries handled correctly (untouched by this PR)
• No premature materialization or unnecessary copies (one pad copy, same as the numpy path)
• Benchmark exists or is not needed (bug fix, no perf-relevant change; no bilateral benchmark exists today)
• README feature matrix updated (not applicable, no backend support change at the matrix level)
• Docstrings present and accurate (public docstring already documents the boundary modes this PR makes true on cupy)

[brendancol]

PR Review: follow-up pass (commit 77e2e75)

Re-review after the review follow-ups. Both earlier findings are addressed:

• Suggestion (dask+cupy boundary coverage): fixed. New test_bilateral_boundary_modes_dask_gpu compares dask+cupy against numpy for every mode in VALID_BOUNDARY_MODES, chunked (15, 15) so chunk seams cross the kernel footprint. rtol 1e-4 matches the existing test_bilateral_dask_gpu tolerance.
• Nit (parametrize): fixed. Both GPU tests now use @pytest.mark.parametrize('mode', VALID_BOUNDARY_MODES) with a module-level import, so each mode reports independently.

Decorator stacking is right (@dask_array_available plus @cuda_and_cupy_available on the dask+cupy test, matching test_bilateral_dask_gpu). Suite is 30 passed on a CUDA host.

No new findings.