[Build] Publish PyTorch 2.11 CUDA 13 aarch64 wheels for GB200

Is your feature request related to a problem? Please describe.

Installing Transformer Engine on a Linux aarch64 GB200 environment with Python 3.12, PyTorch 2.11, CUDA 13.0, and the C++11 ABI finds no compatible prebuilt transformer-engine-torch wheel.

The PyPI package intentionally ships as an sdist and attempts to download a matching framework-extension wheel from the Transformer Engine GitHub release. v2.17 currently provides only x86_64 extension assets, so ARM64 installation falls back to a native extension build. That adds substantial setup time, build dependencies, and failure modes for GB200 users.

The related CUDA 13 core-wheel regression is tracked in #3309.

Describe the solution you'd like

Publish a prebuilt transformer_engine_torch release asset for this compatibility tuple:

• CPython 3.12
• Linux aarch64
• PyTorch 2.11
• CUDA 13.0
• C++11 ABI enabled
• GB200 / SM100

The asset should follow the existing get_wheel_url() naming convention so installing transformer-engine-torch can retrieve it automatically instead of compiling locally.

Release automation should also:

• Build with the required Blackwell architecture target.
• Verify wheel platform and ABI tags.
• Run an install/import smoke check in a matching ARM64 environment.
• Preserve complete compiler output on failure.

Describe alternatives you've considered

Building the framework extension from source works in a correctly provisioned environment, but requires a full CUDA development toolchain and materially increases installation friction. Increasing build parallelism does not remove those requirements and can increase memory or disk pressure.

Additional context

Current release automation already includes ubuntu-22.04-arm, but the release matrix remains fixed to Python 3.12, PyTorch 2.8.0, and CUDA 12.9.1:

https://github.com/NVIDIA/TransformerEngine/blob/main/.github/workflows/attach-wheels-to-release.yml

Recent ARM64 release jobs have not produced assets:

• v2.16.1 exhausted runner disk space: https://github.com/NVIDIA/TransformerEngine/actions/runs/28210514784/job/83570631411
• v2.17 failed while compiling the PyTorch extension: https://github.com/NVIDIA/TransformerEngine/actions/runs/30397388710/job/90403758771

The composite action currently captures docker run output through tail -n 1, which hides the underlying compiler failure from the job log.

Read .github/workflows/attach-wheels-to-release.yml and trace the existing get_wheel_url() naming convention, ARM matrix, and composite action that runs Docker builds. Add the PyTorch 2.11, CUDA 13.0, CPython 3.12, aarch64, C++11 ABI, and GB200/SM100 coverage, then verify tags, installation/import, and complete compiler output on failure.