Avoid torch.compile graph-break in functional normalize fn

[lw]

The data-dependent check on the standard deviation (in case any of its elements is zero) caused a graph break when using torch.compile. Instead this can be replaced by an in-graph assert, which can even become a on-device assert in order to support CUDA graphs.

[pytorch-bot]

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• Build Aarch64 Linux Wheels / pytorch/vision / build-wheel-py3_10-cuda-aarch6412_8-aarch64 (gh)
• Build Aarch64 Linux Wheels / pytorch/vision / upload / upload-wheel-py3_10-cuda-aarch6412_8-aarch64 (gh)
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• Tests / unittests-linux (3.10, linux.12xlarge, cpu) / linux-job (gh)
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• Tests / unittests-linux (3.10, linux.g5.4xlarge.nvidia.gpu, cuda, 12.6) / linux-job (gh)
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• Tests / unittests-linux-cvcuda (3.10, linux.g5.4xlarge.nvidia.gpu, cuda, 12.6) / linux-job (gh)
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• Tests / unittests-macos (3.10, macos-m1-stable) / macos-job (gh)
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• Tests / unittests-windows (3.10, windows.4xlarge, cpu) / windows-job (gh)
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• Tests / unittests-windows (3.11, windows.4xlarge, cpu) / windows-job (gh)
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• Tests / unittests-windows (3.12, windows.4xlarge, cpu) / windows-job (gh)
  test/test_transforms_v2.py::TestNormalize::test_transform[make_image_tensor]

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