Failure with ComposedFunction `∘`

I'm pretty confident this worked in September, but have no idea whether changes here or in ChainRules broke it: Edit -- the change is https://github.com/JuliaDiff/ChainRulesCore.jl/pull/495, discussed there.

julia> Diffractor.gradient(cbrt, 1.23)
(0.29036348772107673,)

julia> Diffractor.gradient(identity∘cbrt, 1.23)
ERROR: ArgumentError: Tangent for the primal Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}} should be backed by a AbstractDict type, not by NamedTuple{(:data,), Tuple{ChainRulesCore.ZeroTangent}}.
Stacktrace:
  [1] _backing_error(P::Type, G::Type, E::Type)
    @ ChainRulesCore ~/.julia/packages/ChainRulesCore/qzYOG/src/tangent_types/tangent.jl:62
  [2] ChainRulesCore.Tangent{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, NamedTuple{(:data,), Tuple{ChainRulesCore.ZeroTangent}}}(backing::NamedTuple{(:data,), Tuple{ChainRulesCore.ZeroTangent}})
    @ ChainRulesCore ~/.julia/packages/ChainRulesCore/qzYOG/src/tangent_types/tangent.jl:33
  [3] (::Diffractor.var"#162#164"{Symbol, DataType})(Δ::ChainRulesCore.ZeroTangent)
    @ Diffractor ~/.julia/packages/Diffractor/HYuxt/src/stage1/generated.jl:308
  [4] (::Diffractor.EvenOddOdd{1, 1, Diffractor.var"#162#164"{Symbol, DataType}, Diffractor.var"#163#165"{Symbol}})(Δ::ChainRulesCore.ZeroTangent)
    @ Diffractor ~/.julia/packages/Diffractor/HYuxt/src/stage1/generated.jl:288
  [5] ∂⃖¹₁merge
    @ ./none:1
  [6] ∂⃖¹₁
    @ ./none:1
  [7] (::Diffractor.ApplyOdd{1, 1})(Δ::Float64)
    @ Diffractor ~/.julia/packages/Diffractor/HYuxt/src/stage1/generated.jl:371
  [8] ∂⃖¹₁ComposedFunction
    @ ./none:1
  [9] (::Diffractor.∇{ComposedFunction{typeof(identity), typeof(cbrt)}})(args::Float64)
    @ Diffractor ~/.julia/packages/Diffractor/HYuxt/src/interface.jl:122
 [10] Diffractor.∇(::Function, ::Float64)
    @ Diffractor ~/.julia/packages/Diffractor/HYuxt/src/interface.jl:128
 [11] top-level scope
    @ REPL[3]:1

Reproduce the failure with the two Diffractor.gradient calls shown in the issue. Start with src/stage1/generated.jl around lines 288-308 and src/interface.jl around lines 122-128, then review the linked ChainRulesCore pull request. Done means gradient(identity∘cbrt, 1.23) completes without the Tangent backing error and preserves the working direct cbrt result.