PyTorch expands dtype/shape checks in CI for better tensor assertions

PyTorch has merged a new pull request (PR #183938) titled 'Expand dtype/shape coverage' into its main trunk. The PR, tagged by contributor eellison, enhances the CSEVariable component of PyTorch's compiler infrastructure. CSEVariable is used in the common subexpression elimination (CSE) pass, which optimizes computation graphs by reusing identical intermediate tensor operations. The improvement broadens the coverage of dtype (data type) and shape assertions within this pass, ensuring that these properties are correctly tracked and verified during compilation.

The update specifically fixes several cases where dtype and shape information was incomplete or incorrect, which could lead to suboptimal optimizations or runtime errors. By expanding the assertion coverage in the CI, PyTorch can now catch more corner cases during development. This PR was approved by PyTorch core developer zou3519. For ML engineers and researchers using PyTorch's torch.compile or JIT scripting, this change improves the reliability and correctness of compiled models, reducing the risk of silent dtype mismatches or shape inconsistencies in production workflows.