Register a Cortex-M flow in the backend test suite

[rascani]

Summary

The shared suite at backends/test/suite runs 23 torchvision and torchaudio models against every other major backend; Cortex-M had none, so its model coverage was hand-written one file at a time. This adds one, running on the Corstone-300 FVP. Five models pass and 15 are listed in CORTEX_M_SKIPS with a comment recording why each fails; read that list as a to-do. A pass means the model lowered and ran rather than that it was accurate, for the reason the module docstring gives.

The suite drives to_edge_transform_and_lower, so CortexMTester grows a stage of that name: to_edge with no partitioner followed by CortexMPassManager, since Cortex-M rewrites operators in place rather than delegating a subgraph. The flow converts 4D inputs to channels_last, without which CortexMConv2DCheck rejects every convolution in every model and raises the FVP timeout, which an ImageNet-sized model exceeds at the 120 second default.

Test plan

CI goes through test_backend.sh like every other backend, path-gated because every case installs the Arm toolchain, builds a runner and drives the FVP. The operator suite is excluded and the exclusion says why.

Authored with Claude Code.

[pytorch-bot]

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