Add bessel_i0e and bessel_i1e ops

[robert-johansson]

Summary

Adds element-wise exponentially scaled modified Bessel functions of the first kind:

• bessel_i0e(x) = exp(-|x|) I₀(x)
• bessel_i1e(x) = exp(-|x|) I₁(x)

These are the numerically stable building blocks for Bessel-dependent distributions (von Mises, Rice, noncentral chi-squared) in probabilistic programming and physics applications. Relates to #21 (bessel functions requested).

• Metal: Cephes Chebyshev polynomial approximation (new kernel bessel.h)
• CPU: Same Cephes algorithm via SIMD math helpers
• CUDA: Same Cephes algorithm in device functors
• Autograd: grad(i0e) = i1e(x) - sign(x)·i0e(x), grad(i1e) = i0e(x) - (sign(x) + 1/x)·i1e(x) with safe x=0 handling
• Python bindings: mx.bessel_i0e() and mx.bessel_i1e() with docstrings

The algorithm is Cephes (public domain, Stephen Moshier) — the same implementation used by SciPy, JAX/XLA, PyTorch, and TensorFlow. Float32 relative error < 2e-7 across the full range.

Test plan

• C++ tests: 15 assertions covering accuracy vs known values, even/odd symmetry, large x (asymptotic), type promotion, float16, vectorized shapes, and gradient correctness for both ops
• Python tests: equivalent coverage with numpy comparison
• CUDA tests (no local hardware — relies on CI)

🤖 Generated with Claude Code