PyTorch 2.12.0 Release

# PyTorch 2.12.0 Release Notes - [Highlights](#highlights) - [Backwards Incompatible Changes](#backwards-incompatible-changes) - [Deprecations](#deprecations) - [New Features](#new-features) - [Improvements](#improvements) - [Bug fixes](#bug-fixes) - [Performance](#performance) - [Documentation](#documentation) - [Developers](#developers) - [Security](#security) # Highlights Batched linalg.eigh on CUDA is up to 100x faster due to updated cuSolver backend selection. New torch.accelerator.Graph API unifies graph capture and replay across CUDA, XPU, and out-of-tree backends. torch.export.save now supports Microscaling (MX) quantization formats, enabling full export of aggressively compressed models. Adagrad now supports fused=True, joining Adam, AdamW, and SGD with a single-kernel optimizer implementation. torch.cond control flow can now be captured and replayed inside CUDA Graphs. ROCm users gain expandable memory segments, rocSHMEM symmetric memory collectives, and FlexAttention pipelining. For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release. # Backwards Incompatible Changes ## Build Frontend -...