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Showing content from https://github.com/JuliaDiff/ChainRulesCore.jl below:

JuliaDiff/ChainRulesCore.jl: AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

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⚠️ The ChainRules project is currently seeking a new primary maintainer ⚠️ See this Discourse thread. We currently have many secondary maintainers and contributors with commit rights, but noone who has committed to leading the efforts. Until this is resolved PRs and issues may not recieve timely response.

The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself.

This will allow your package to be used with ChainRules.jl, which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.

This package is a work in progress; PRs welcome!

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