BUG: torch: fix up clip

[ev-br]

Work around pytorch/pytorch#172067 :

>>> torch.clamp(torch.ones(3), None, torch.as_tensor(float('nan')))
tensor([nan, nan, nan])
>>> torch.clamp(torch.ones(3), None, float('nan'))     # oops
tensor([1., 1., 1.])

Tested locally with (yes, $10^5$ examples)

$ ARRAY_API_TESTS_SKIP_DTYPES=uint16,uint32,uint64 ARRAY_API_TESTS_MODULE=array_api_compat.torch pytest array_api_tests/test_operators_and_elementwise_functions.py::test_clip -v --max-examples 100_000 -s

which passes.

[Copilot]

Pull request overview

This PR fixes a PyTorch bug where torch.clamp incorrectly handles NaN values differently depending on whether they are passed as scalars or tensors (pytorch/pytorch#172067). The fix adds special handling for NaN values when both min and max are scalars.

Key changes:

• Added import math for NaN detection
• Refactored clip function logic to handle mixed scalar/tensor signatures better
• Added special case to return NaN-filled tensors when NaN bounds are provided as scalars

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[Copilot]

Removing **kwargs from the function signature breaks support for the out= parameter. The test_clip_out test in tests/test_common.py expects torch's clip to support out=, which torch.clamp does support. The **kwargs should be retained in the signature and passed to torch.clamp calls at lines 865 and 877 to maintain backward compatibility.

[ev-br]

Merging as a follow-up to gh-353 to fix an edge case which surfaced in more thorough testing.