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Tools for detecting and locating invalid numerical values like NaN or Inf during execution.
Distinguishing note: Focuses on numerical stability debugging, distinct from general software testing.
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This project is a high-performance numerical computing library designed for large-scale scientific and machine learning workloads. It functions as an automatic differentiation framework and a just-in-time compilation engine, transforming high-level Python code into optimized machine instructions. By enforcing pure functional programming patterns and immutable array semantics, the library ensures that mathematical functions remain compatible with automated graph transformations and symbolic differentiation. The platform distinguishes itself through its distributed array computing capabilities,
Detects and locates the source of invalid numerical values like NaN or Inf during function execution and gradient calculation.