Machine-learning operators often have correctness constraints that cut across multiple hyperparameters and/or data. Violating these constraints causes the operator to raise runtime exceptions, but those are usually documented only informally or not at all. This paper presents the first interprocedural weakest-precondition analysis for Python to extract hyperparameter constraints. The analysis is mostly static, but to make it tractable for typical Python idioms in machine-learning libraries, it selectively switches to the concrete domain for some cases. This paper demonstrates the analysis by extracting hyperparameter constraints for 181 operators from a total of 8 ML libraries, where it achieved high precision and recall and found real bugs. Our analysis advances static analysis for Python and is a step towards safer and more robust machine learning.