Adding array-api-compat fallback

[amalia-k510]

This PR adds array-api-compat as a fallback in the singledispatch functions across the stats and conv modules so that Array API-compatible arrays (JAX, PyTorch, and others) work out of the box without needing to register each backend individually. The approach is: in each fallback, try array_api_compat.array_namespace(x) first. If it recognizes the array, dispatch through the standard Array API; if not, fall through to the existing numpy path. This touches _generic_ops.py (sum/min/max), _is_constant.py, _power.py, and conv/_to_dense.py. array-api-compat is added as a dependency in pyproject.toml.

Tests are in tests/test_jax.py covering all the stats functions and to_dense with JAX arrays. One thing to flag: _mean_var.py didn't need changes since it calls mean() and power() internally which already go through the fixed dispatchers, at least in my understanding, but JAX requires jax_enable_x64 for the dtype=np.float64 calls to work.

[codecov]

Codecov Report

❌ Patch coverage is 41.66667% with 14 lines in your changes missing coverage. Please review.
✅ Project coverage is 93.71%. Comparing base (395b37e) to head (5373050).

Files with missing lines	Patch %	Lines
src/fast_array_utils/stats/_is_constant.py	0.00%	11 Missing ⚠️
src/fast_array_utils/conv/_to_dense.py	40.00%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #159      +/-   ##
==========================================
- Coverage   96.78%   93.71%   -3.07%     
==========================================
  Files          19       19              
  Lines         466      493      +27     
==========================================
+ Hits          451      462      +11     
- Misses         15       31      +16     

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[codspeed-hq]

Merging this PR will degrade performance by 26.04%

❌ 17 regressed benchmarks
✅ 215 untouched benchmarks

⚠️ Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Benchmark	BASE	HEAD	Efficiency
❌	test_stats_benchmark[numpy.ndarray-1d-all-int32-mean_var]	120.8 ms	162.1 ms	-25.5%
❌	test_stats_benchmark[numpy.ndarray-2d-all-float32-mean_var]	123.4 ms	146.9 ms	-15.99%
❌	test_stats_benchmark[numpy.ndarray-2d-all-int32-mean_var]	120.8 ms	162.1 ms	-25.5%
❌	test_stats_benchmark[numpy.ndarray-1d-all-float32-mean_var]	123.4 ms	146.9 ms	-15.98%
❌	test_stats_benchmark[numpy.ndarray-2d-ax0-float64-mean_var]	170.1 ms	189.2 ms	-10.12%
❌	test_stats_benchmark[numpy.ndarray-2d-ax0-float32-mean_var]	120.1 ms	143.6 ms	-16.37%
❌	test_stats_benchmark[numpy.ndarray-2d-ax1-float32-mean_var]	123.6 ms	147 ms	-15.96%
❌	test_stats_benchmark[numpy.ndarray-2d-ax0-int32-mean_var]	117.5 ms	158.8 ms	-26.04%
❌	test_stats_benchmark[numpy.ndarray-2d-ax1-int32-mean_var]	121 ms	162.3 ms	-25.47%
❌	test_stats_benchmark[scipy.sparse.csc_array-1d-all-float32-mean_var]	123.5 ms	147 ms	-15.96%
❌	test_stats_benchmark[scipy.sparse.csc_array-1d-all-int32-mean_var]	120.9 ms	162.2 ms	-25.47%
❌	test_stats_benchmark[scipy.sparse.csc_array-2d-all-float32-mean_var]	123.5 ms	147 ms	-15.95%
❌	test_stats_benchmark[scipy.sparse.csc_array-2d-all-int32-mean_var]	120.9 ms	162.2 ms	-25.48%
❌	test_stats_benchmark[scipy.sparse.csr_array-1d-all-float32-mean_var]	123.5 ms	147 ms	-15.95%
❌	test_stats_benchmark[scipy.sparse.csr_array-2d-all-int32-mean_var]	120.9 ms	162.2 ms	-25.47%
❌	test_stats_benchmark[scipy.sparse.csr_array-1d-all-int32-mean_var]	120.9 ms	162.2 ms	-25.47%
❌	test_stats_benchmark[scipy.sparse.csr_array-2d-all-float32-mean_var]	123.5 ms	147 ms	-15.95%

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_{Comparing amalia-k510:array-api-implementation (5373050) with main (395b37e)}