What are the limitations of PyTea in detecting tensor shape errors

1. Path Explosion and Complexity: While PyTea uses conservative pruning to manage the number of execution paths, complex models with many conditional statements or loops could still lead to path explosion, potentially limiting its scalability in very large or highly dynamic codebases[1][3].

2. Unimplemented APIs: PyTea may raise false alarms if it encounters PyTorch or other third-party APIs that have not been implemented within its framework. This means that users need to be aware of which APIs are supported to avoid unnecessary warnings[5].

3. Offline Analysis Limitations: The offline analysis using the Z3 SMT solver can result in undecidable paths if the constraints are too complex or non-linear, leading to timeouts. This might not always provide a clear indication of whether a path is valid or not[5].

4. Interpretation of Results: Users need to interpret the results carefully, as PyTea identifies potential errors based on constraints that might not always lead to actual runtime errors. This requires understanding the context and potential execution paths of the code[5].

5. Integration and Support: While PyTea supports major libraries like torchvision, numpy, and PIL, its integration with development environments like VSCode is still in development. This means that interactive debugging features are not yet fully mature[2].