[cuda.core] Add support for host_launch (host callback nodes / host function launches)

Feature Request

Add a host_launch (or equivalent) API to cuda.core that allows scheduling
Python callables (or C function pointers) to execute on the host as part of a
stream's work order. This is the cuLaunchHostFunc / cudaLaunchHostFunc
path (and its graph-node counterpart, host nodes via cuGraphAddHostNode).

Motivation

cuda.core currently exposes launch(...) for device kernels but has no
symmetric primitive for host work. This makes it impossible to express
mixed host/device work ordering in pure cuda.core terms — users must drop
to cuda.bindings for cuLaunchHostFunc, which breaks the cuda.core
abstraction boundary (streams, events, graphs).

Common use cases:

• Logging / progress callbacks ordered against GPU work without host-side
  stream synchronization.
• Triggering Python-side state transitions (e.g. buffer release, metric
  updates) at a specific point in a stream.
• Host nodes in CUDA graphs for workflows that need host-side compute or
  notification steps between kernels.

Proposed Scope

• A top-level host_launch(stream, fn, *args, **kwargs) (or
  stream.launch_host(fn, ...)) that wraps cuLaunchHostFunc.
• A corresponding graph node type (HostNode) added to
  cuda.core.graph._subclasses, alongside the existing EmptyNode,
  MemcpyNode, etc.
• Clear documentation of the callback threading / reentrancy restrictions
  imposed by the CUDA driver (host functions run on an internal driver
  thread; must not call any CUDA API).
• An example under cuda_core/examples/ demonstrating a host callback
  ordered between two kernels.
• API reference entries in cuda_core/docs/source/api.rst.

Related

• Driver API: cuLaunchHostFunc, cuGraphAddHostNode
• Runtime API: cudaLaunchHostFunc
• Part of cuda.core feature audit gap list (Nov 2025).