Hello,

I am quite a Julia newbe and currently migrating from Python/JAX ecosystem into Julia and I have encounter frustrating error while computing a gradient of quite a simple function.

The problem here is the function `mean`

which causes the error. If you change `mean`

to for example `norm.`

, then the gradient will be computed without any problem.

Could someone point me why function `mean`

generate such error and how can I solve it?

Minimum working example

```
using Statistics
using LinearAlgebra
using Zygote
D = 2
N = [rand(D) for i β 1:5]
S = [rand(D) for i β 1:5]
function cost(N, S)
sum(mean(N.-S))
end
cost(N, S)
β = gradient(cost, N, S)
```

```
MethodError: no method matching zero(::Type{Vector{Float64}})
Closest candidates are:
zero(!Matched::Union{Type{P}, P}) where P<:Dates.Period at /build/julia/src/julia-1.6.1/usr/share/julia/stdlib/v1.6/Dates/src/periods.jl:53
zero(!Matched::CartesianIndex{N}) where N at multidimensional.jl:106
zero(!Matched::LinearAlgebra.UniformScaling{T}) where T at /build/julia/src/julia-1.6.1/usr/share/julia/stdlib/v1.6/LinearAlgebra/src/uniformscaling.jl:136
...
zero(::Vector{Vector{Float64}})@abstractarray.jl:1085
_backmean(::Vector{Vector{Float64}}, ::FillArrays.Fill{Float64, 1, Tuple{Base.OneTo{Int64}}}, ::Colon)@array.jl:327
(::Zygote.var"#656#657"{Colon, Vector{Vector{Float64}}})(::FillArrays.Fill{Float64, 1, Tuple{Base.OneTo{Int64}}})@array.jl:325
(::Zygote.var"#2769#back#658"{Zygote.var"#656#657"{Colon, Vector{Vector{Float64}}}})(::FillArrays.Fill{Float64, 1, Tuple{Base.OneTo{Int64}}})@adjoint.jl:59
Pullback@Local: 7[inlined]
(::typeof(β(cost)))(::Float64)@interface2.jl:0
(::Zygote.var"#41#42"{typeof(β(cost))})(::Float64)@interface.jl:41
gradient(::Function, ::Vector{Vector{Float64}}, ::Vararg{Vector{Vector{Float64}}, N} where N)@interface.jl:59
top-level scope@Local: 10
```