Add a CoreML rung on macOS

The macOS ladder was the CPU provider alone, with CoreML listed as a gap.
It is now CoreML, then the CPU, then tract — unmeasured, since nobody here
has a Mac, and safe to ship unmeasured because the probe's clock rejects a
CoreML slower than the CPU and `attempt` refuses one that crashes.

- `Rung::CoreMl`, a compiling rung like TensorRT: an ML Program with every
  compute unit allowed, falling back to the CPU until each model's program
  is built. The embedder stays on the CPU, as on the Hexagon (§7).
- The cache is one directory per model and runtime version. CoreML keys a
  model committed from memory on its input and node names, not its
  weights (ONNX Runtime 1.29, coreml_execution_provider.cc), so two
  exports of one architecture would otherwise share a program.
- The fingerprint on macOS is the chip and the OS release, which ships
  CoreML.
- The desktop looks for the runtime in the bundle's Contents/Frameworks
  and Homebrew's prefixes; fetch-desktop-runtime.sh on a Mac downloads
  ONNX Runtime 1.29.0 for Apple silicon, which carries CoreML.

docs/dev/macos.md says what exists, how to build it, and which log lines
to ask a Mac user for.

core/dr-inference-engine/Cargo.toml

@@ -38,6 +38,11 @@ ort = { workspace = true, features = ["cuda", "tensorrt"] }
 [target.'cfg(target_os = "android")'.dependencies]
 ort = { workspace = true, features = ["qnn"] }
 
+# The Apple rung: CoreML's option builder, which fills the runtime's generic
+# key/value map. `ort-sys`'s `coreml` feature is empty; nothing links.
+[target.'cfg(target_os = "macos")'.dependencies]
+ort = { workspace = true, features = ["coreml"] }
+
 [features]
 # The floor: `tract` supplies the API table when no runtime file is found, or
 # always, in a build without `native`. Tests want this and nothing else.