Worked on the PyTorch repository to address a critical correctness issue in the aten.expand_copy operation. The developer introduced explicit decomposition of an implicit argument, reducing the risk of incorrect tensor expansions in edge cases and aligning the implementation with recent codebase changes. This solution was implemented in Python, leveraging deep learning and machine learning expertise, and was thoroughly validated through comprehensive unit testing to guard against regressions. The export path was updated to reflect the new explicit decomposition, ensuring consistency across the codebase. This focused, test-backed fix contributed to the overall stability and correctness of expand semantics in PyTorch.