BayesFlow Testing Suite

This skill provides specialized guidance and implementation patterns for testing BayesFlow extensions, ensuring robust and reproducible results in simulation-based inference (SBI). It handles critical configuration tasks such as enforcing the PyTorch backend for Keras 3 to prevent runtime conflicts, provides templates for shape-checking simulators, and implements advanced testing for network invariance and equivariance. By automating the boilerplate for mock networks, adapter pipelines, and config roundtrips, it helps developers avoid common pitfalls like non-deterministic tests and slow CI cycles, resulting in a more maintainable and reliable codebase for complex Bayesian modeling.

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