Skip torch random ops in CoreML partitioner

[john-rocky]

Summary

coremltools' MIL converter fails the input-count check for the torch random
ops (rand / randn / rand_like / randn_like / randint /
randint_like) and aborts during compilation with an internal error
(see issue for the full traceback).

Add these ops to should_override_support so the partitioner refuses to
delegate them and they fall back to the portable backend, the same way
acosh / asinh are handled today.

Fixes #11722.

Test plan

Added test_random_ops_are_skipped which lowers a model that adds
torch.randn + torch.rand outputs and asserts the random ops remain in
the top-level graph (not delegated). Also reproduced the original
randn_like repro from the issue and confirmed it now lowers without
crashing:

$ python -m unittest -v executorch.backends.apple.coreml.test.test_coreml_partitioner.TestCoreMLPartitioner.test_random_ops_are_skipped
Ran 1 test in 0.475s

OK

Authored with Claude.

[pytorch-bot]

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⚠️ 12 Awaiting Approval

As of commit 3c651e8 with merge base 94d2881 ():

AWAITING APPROVAL - The following workflows need approval before CI can run:

• Apple (gh)
• Build documentation (gh)
• Build Presets (gh)
• Cadence Build & Test (gh)
• Lint (gh)
• periodic (gh)
• pull (gh)
• Test ARM Backend (gh)
• Test CoreML Backend (gh)
• Test QNN Backend (gh)
• Test Vulkan Backend (gh)
• Test XNNPACK Backend (gh)

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