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Merge pull request #57 from SciML/gd/fields
Restore full API in docstrings
2 parents 3d4e912 + 0921565 commit 9ea5887

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+90
-36
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‎Project.toml

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Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@ uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
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authors = [
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"Vaibhav Dixit <[email protected]>, Guillaume Dalle and contributors",
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]
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version = "1.2.0"
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version = "1.2.1"
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[deps]
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ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

‎src/dense.jl

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@@ -1,11 +1,15 @@
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"""
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AutoChainRules(; ruleconfig)
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AutoChainRules{RC}
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Struct used to select an automatic differentiation backend based on [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl) (see the list [here](https://juliadiff.org/ChainRulesCore.jl/stable/index.html#ChainRules-roll-out-status)).
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoChainRules(; ruleconfig)
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# Keyword Arguments
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# Fields
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- `ruleconfig::RC`: a [`ChainRulesCore.RuleConfig`](https://juliadiff.org/ChainRulesCore.jl/stable/rule_author/superpowers/ruleconfig.html) object.
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"""
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mode(::AutoChainRules) = ForwardOrReverseMode() # specialized in the extension
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"""
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AutoDiffractor()
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AutoDiffractor
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Struct used to select the [Diffractor.jl](https://github.com/JuliaDiff/Diffractor.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoDiffractor()
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"""
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struct AutoDiffractor <: AbstractADType end
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mode(::AutoDiffractor) = ForwardOrReverseMode()
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"""
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AutoEnzyme(; mode=nothing)
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AutoEnzyme{M}
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Struct used to select the [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) backend for automatic differentiation.
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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AutoEnzyme(; mode=nothing)
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# Keyword Arguments
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# Fields
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- `mode::M`: can be either
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- an object subtyping `EnzymeCore.Mode` (like `EnzymeCore.Forward` or `EnzymeCore.Reverse`) if a specific mode is required
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mode(::AutoEnzyme) = ForwardOrReverseMode() # specialized in the extension
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"""
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AutoFastDifferentiation()
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AutoFastDifferentiation
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Struct used to select the [FastDifferentiation.jl](https://github.com/brianguenter/FastDifferentiation.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoFastDifferentiation()
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"""
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struct AutoFastDifferentiation <: AbstractADType end
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mode(::AutoFastDifferentiation) = SymbolicMode()
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"""
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AutoFiniteDiff(; fdtype=Val(:forward), fdjtype=fdtype, fdhtype=Val(:hcentral))
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AutoFiniteDiff{T1,T2,T3}
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Struct used to select the [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoFiniteDiff(; fdtype=Val(:forward), fdjtype=fdtype, fdhtype=Val(:hcentral))
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# Keyword Arguments
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# Fields
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- `fdtype::T1`: finite difference type
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- `fdjtype::T2`: finite difference type for the Jacobian
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mode(::AutoFiniteDiff) = ForwardMode()
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"""
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AutoFiniteDifferences(; fdm)
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AutoFiniteDifferences{T}
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Struct used to select the [FiniteDifferences.jl](https://github.com/JuliaDiff/FiniteDifferences.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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# Keyword Arguments
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AutoFiniteDifferences(; fdm)
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# Fields
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- `fdm::T`: a [`FiniteDifferenceMethod`](https://juliadiff.org/FiniteDifferences.jl/stable/pages/api/#FiniteDifferences.FiniteDifferenceMethod)
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"""
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mode(::AutoFiniteDifferences) = ForwardMode()
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"""
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AutoForwardDiff(; chunksize=nothing, tag=nothing)
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AutoForwardDiff{chunksize,T}
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Struct used to select the [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Keyword Arguments
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# Constructors
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AutoForwardDiff(; chunksize=nothing, tag=nothing)
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# Type parameters
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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# Fields
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- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
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"""
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struct AutoForwardDiff{chunksize, T} <: AbstractADType
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mode(::AutoForwardDiff) = ForwardMode()
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"""
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AutoPolyesterForwardDiff{chunksize,T}
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Struct used to select the [PolyesterForwardDiff.jl](https://github.com/JuliaDiff/PolyesterForwardDiff.jl) backend for automatic differentiation.
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoPolyesterForwardDiff(; chunksize=nothing, tag=nothing)
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Struct used to select the [PolyesterForwardDiff.jl](https://github.com/JuliaDiff/PolyesterForwardDiff.jl) backend for automatic differentiation.
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# Type parameters
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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# Keyword Arguments
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# Fields
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
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"""
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struct AutoPolyesterForwardDiff{chunksize, T} <: AbstractADType
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mode(::AutoPolyesterForwardDiff) = ForwardMode()
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"""
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AutoReverseDiff(; compile=false)
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AutoReverseDiff
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Struct used to select the [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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# Keyword Arguments
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AutoReverseDiff(; compile=false)
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# Fields
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- `compile::Bool`: whether to [compile the tape](https://juliadiff.org/ReverseDiff.jl/api/#ReverseDiff.compile) prior to differentiation
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"""
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mode(::AutoReverseDiff) = ReverseMode()
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"""
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AutoSymbolics()
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AutoSymbolics
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Struct used to select the [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoSymbolics()
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"""
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struct AutoSymbolics <: AbstractADType end
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mode(::AutoSymbolics) = SymbolicMode()
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"""
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AutoTapir()
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AutoTapir
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Struct used to select the [Tapir.jl](https://github.com/withbayes/Tapir.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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# Keyword Arguments
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AutoTapir(; safe_mode=true)
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# Fields
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- `safe_mode::Bool`: whether to run additional checks to catch errors early. On by default. Turn off to maximise performance if your code runs correctly.
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"""
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Struct used to select the [Tracker.jl](https://github.com/FluxML/Tracker.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructor
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# Constructors
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AutoTracker()
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"""
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mode(::AutoTracker) = ReverseMode()
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"""
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AutoZygote()
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AutoZygote
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Struct used to select the [Zygote.jl](https://github.com/FluxML/Zygote.jl) backend for automatic differentiation.
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Exported from [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoZygote()
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"""
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struct AutoZygote <: AbstractADType end
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