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4 changes: 2 additions & 2 deletions DifferentiationInterface/docs/src/tutorial.md
Original file line number Diff line number Diff line change
Expand Up @@ -150,8 +150,8 @@ To compute sparse Jacobians or Hessians, you need three ingredients (read [this
ADTypes.jl v1.0 defines the [`AutoSparse`](@ref) wrapper, which brings together these three ingredients.
At the moment, this new wrapper is not well-supported in the ecosystem, which is why DifferentiationInterface.jl provides the necessary objects to get you started:

1. [`SymbolicsSparsityDetector`](@ref) (requires [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) to be loaded)
2. [`GreedyColoringAlgorithm`](@ref)
1. [`DifferentiationInterface.SymbolicsSparsityDetector`](@ref) (requires [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) to be loaded)
2. [`DifferentiationInterface.GreedyColoringAlgorithm`](@ref)

!!! warning
These objects are not part of the public API, so they can change unexpectedly between versions.
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