trunk/809884d07971328578a5441aae67da6eaa88748d: [user-streams] Add support functions for stream codegen (#165504)

The PyTorch development team has merged a significant pull request (#165504) that adds utility functions for stream codegen to the popular deep learning framework. This technical update, committed under hash 809884d07971328578a5441aae67da6eaa88748d and tagged by contributor mlazos, introduces new support functions specifically designed for managing streams and implementing a stream pool system. The change represents PyTorch's continued evolution toward more efficient GPU resource management, particularly important as AI models grow larger and more complex. The PR was approved by core contributor eellison and builds upon previous work in dependency #165390, showing systematic improvements to PyTorch's low-level infrastructure.

These new utility functions enable developers to better manage GPU streams—sequences of operations that can execute concurrently on a GPU—and implement stream pools for more efficient memory and computation management. For technical users, this means potentially significant performance improvements in scenarios requiring concurrent GPU operations, such as training large models or running multiple inference tasks simultaneously. The stream codegen utilities abstract away some of the complexity of direct CUDA stream management while providing more control over GPU resource allocation. This update reflects PyTorch's commitment to both performance optimization and developer experience, making advanced GPU programming more accessible while maintaining the framework's reputation for flexibility and control.