[PyTorch] Update to 2.4.0, add distributed

[HGuillemet]

Included in this PR:

• Update to PyTorch 2.4.0
• Add distributed framework (backend Gloo)
• Add a minimal binding for std::chrono, needed by distributed (TBC)
• Add an adapter for intrusive_ptr, enabling transparent usage like for shared_ptr
• Add an adapter for weak_ptr, enabling transparent usage (could be moved to JavaCPP ?)
• Add an optional Maven dependency to cuda. This allows to use types defined in the cuda presets. Users calling a method of PyTroch presets using one of these types should then have the cuda presets. Which also means the cuda library of the presets will be used, and not the system one (unless the user sets org.bytedeco.javacpp.pathsFirst and their system cuda has same version than the cuda presets).
• Merge functions packages to main package
• Fix [PyTorch 2.2.2-1.5.11-SNAPSHOT] Training produces poor MNIST model on Windows #1503
• Fix [PyTorch] Training is very slow on Linux. #1504
• Fix support for OpenMP on macos

Remains to be done:

• Determine if other distributed backends are needed (MPI or UCC. NCCL is not supported on Windows yet).

[HGuillemet]

Pytorch uses cupti for profiling, but it's not provided by the cuda presets. Could we add it ?

[saudet]

Sure, feel free to give it a try

[HGuillemet]

Ready for me. We can look at MPI or UCC distributed backend later if needed.
Artifacts for this PR are available on usual snapshot repository as org.bytedeco:pytorch-platform-gpu:2.4.0-1.5.11-SNAPSHOT or org.bytedeco:pytorch-platform:2.4.0-1.5.11-SNAPSHOT.