Migrating cuda-python samples from cuda-samples - #2266

Resolves conflicts caused by concurrent changes on main:

- Relicense (#2293) updated SPDX headers on cuda_bindings/examples/*.py
  and cuda_bindings/cuda/bindings/_example_helpers/*. All of those files
  were removed on this branch, so main's edits are discarded via git rm.
- Bindings-side test_examples.py, cuda_core/examples/pytorch_example.py
  and cuda_core/tests/example_tests/test_basic_examples.py were removed
  on this branch; main's edits to them are discarded.
- scripts/run_tests.sh was removed on main (#2271); accept the deletion.
- ci/tools/run-tests: keep this branch's optional sample-deps install
  (nvtx, pillow) but pick up main's rename CUDA_VER_MINOR ->
  TEST_CUDA_MAJOR_MINOR.
- Four new texture / GL-interop examples added to cuda_core/examples/
  on main (gl_interop_fluid.py, gl_interop_fluid_numba_cuda_mlir.py,
  gl_interop_mipmap_lod.py, texture_sample.py) are migrated into their
  own directories under samples/ with README.md and requirements.txt.
  Added a DISPLAY guard to glInteropFluid and glInteropMipmapLod so they
  waive with exit code 2 on headless runners, matching glInteropPlasma.
- ruff.toml: ignore RUF059 under samples/** (matches the pre-existing
  ignore for examples/**).
- .spdx-ignore: drop the obsolete cuda_bindings/examples/* line.

Verified end-to-end:
- pixi install -e samples: solves cleanly.
- pytest cuda_core/tests/example_tests/test_samples.py: 46 passed,
  9 skipped (all legitimate: no DISPLAY, no P2P, sub-Hopper GPU, or
  intentionally omitted deps), 0 failed. 55 samples collected.
- ruff check + format: clean.
- toolshed/check_spdx.py: clean on edited files.

…tSignalAnalysis

CuPy's FFT backend requires libcufft (shipped by nvidia-cufft-cu12 on PyPI).
Adding it to the PEP 723 inline script block and requirements.txt lets the
sample runner automatically waive the test when cuFFT is not installed,
rather than crashing with an ImportError at runtime.

…umpyVsCupy

cp.dot() requires libcublasLt (shipped by nvidia-cublas-cu12 on PyPI).
Adding it to the PEP 723 inline script block and requirements.txt lets the
sample runner automatically waive the test when cuBLAS is not installed.

…gram

cp.random requires libcurand which is not installed in the cuda-13x
wheel CI environment. The random data is only used as histogram input,
so generating it on CPU via numpy and uploading with cp.asarray() is
equivalent and removes the curand dependency.

…cess is unavailable

Per CUDA docs (cuMemPoolCreate): managed memory pools require all devices to
have non-zero concurrentManagedAccess; otherwise CUDA_ERROR_NOT_SUPPORTED is
returned. This attribute is 0 on Windows, WSL, and some Linux VM environments.

Check device.properties.concurrent_managed_access before attempting to create
the ManagedMemoryResource and exit with the runner waiver code when it is False,
mirroring the existing Windows waiver already in the sample.

…cess is unavailable

Managed memory pools (cuMemPoolCreate) require all devices to have non-zero
concurrentManagedAccess; otherwise CUDA_ERROR_NOT_SUPPORTED is returned.
This attribute is 0 on Windows, WSL, and some Linux VM environments.

Move the concurrent_managed_access check to just after device info is printed
and simplify the message, keeping it alongside the existing Windows waiver.

NVIDIA WSL User Guide states that only legacy CUDA IPC APIs are supported
from driver R510; the newer IPC memory pool API (cuMemPoolCreate with POSIX
FD handles) is not supported on WSL2. Detect WSL by checking for 'microsoft'
in /proc/sys/kernel/osrelease and return False from check_ipc_support() so
the sample exits cleanly with a waiver instead of crashing with
CUDA_ERROR_INVALID_VALUE.

…rrent managed access is unavailable

Both samples use ManagedMemoryResource which requires concurrent managed
memory access (concurrentManagedAccess != 0). On WSL and Windows this
attribute is 0, causing CUDA_ERROR_NOT_SUPPORTED at pool creation.

Add concurrent_managed_access check after device init, consistent with
the existing Windows waiver and the fix applied to blurImageUnifiedMemory.

…UDA context

nvmlSystemGetProcessName returns NVML_ERROR_NOT_FOUND when the process
has not yet established a CUDA context (i.e. no cudaMalloc or equivalent
has been called). This is expected NVML behavior per the API docs.

Guard the get_process_name() call with a try/except NotFoundError so
the sample gracefully prints N/A instead of crashing.