Use abstract types for projection

Project.toml

@@ -11,14 +11,16 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 BenchmarkTools = "0.5"
 Compat = "2, 3"
 FiniteDifferences = "0.10"
+OffsetArrays = "1"
 StaticArrays = "0.11, 0.12, 1"
 julia = "1"
 
 [extras]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "BenchmarkTools", "FiniteDifferences", "StaticArrays"]
+test = ["Test", "BenchmarkTools", "FiniteDifferences", "OffsetArrays", "StaticArrays"]

docs/make.jl

@@ -10,7 +10,7 @@ DocMeta.setdocmeta!(
         using Random
         Random.seed!(0)  # frule doctest shows output
 
-        using ChainRulesCore
+        using ChainRulesCore, LinearAlgebra
         # These rules are all actually defined in ChainRules.jl, but we redefine them here to
         # avoid the dependency.
         @scalar_rule(sin(x), cos(x))  # frule and rrule doctest