feat: Switch to using nanobind from pybind11 (!10)

Now that there is a stable v1.0.0 release of nanobind, this MR follows the nanobind docs instructions on getting setup with nanobind and CMake and porting a pybind11 project to nanobind.

The main reasons that nanobind is attractive for the Columnar Analysis project are (in my mind):

More concretely, benchmarks show ~2-3× faster compile time, ~3× smaller binaries, and up to ~8× lower runtime overheads compared to pybind11.

• https://nanobind.readthedocs.io/en/latest/index.html

nanobind can exchange n-dimensional arrays (henceforth “ndarrays”) with popular array programming frameworks including NumPy, PyTorch, TensorFlow, and JAX. It supports zero-copy exchange using two protocols:

• The classic buffer protocol.
• DLPack, a GPU-compatible generalization of the buffer protocol.

nanobind knows how to talk to each framework and takes care of all the nitty-gritty details.

• https://nanobind.readthedocs.io/en/latest/ndarray.html

Both of those seem like big wins to me!

I also want to thank @gstark and (especially big thanks to) Angus Hollands for their help in debugging some small C++ things that I messed up over in the IRIS-HEP Slack (my C++-foo in my postdoc years had become quite degraded compared to my Ph.D. days 😬).

---

• Use nanobind v1.0.0 to provide C++/Python bindings.

  • Migrating from pybind11 to nanobind is done by mainly following the v1.0.0 docs on installing as a Git submodule and then providing the necessary information in the CMake CMakeLists.txt to properly find and build nanobind, though while the use of Development.module is recommended

    find_package(Python 3.8 COMPONENTS Interpreter Development.Module REQUIRED)

    Development is used instead to allow for use of Python::Python.

  • On the Python side most of what is done is just changing names from 'pybind11' to 'nanobind', and then taking advantage of the nb::ndarray API to abstract away all interactions with buffers, which simplifies things quite a bit.

  • To properly allow for the Python object that has the array data to live long enough for its data to be referenced by a consumer dynamically allocate the vector and then use nb::capsule to wrap the C++ pointer for it and provide a cleanup routine when the capsule is garbage collected.