segmented reduction

I would like to ask, if someone would be willing to write function(s) for segmented reductions. These functions are handy for multiple-instance learning problems, where sample is represented by an unordered set of vectors (each sample can have different number of vectors). It would be nice to have these functions available for use with Flux.

My implementation of the normal Julia code for the forward pass looks like
```
function segmented_max(x::Matrix{T},bags::Vector) where{T}
assert(checkbounds(x,bags))
o = zeros(eltype(x),size(x,1),length(bags))
fill!(o,typemin(T));
@inbounds for i in 1:length(bags) #iterate over bags
for j in bags[i] #iterate over items (vectors) in bags
for k in 1:size(x,1)
if x[k,j]>o[k,i]
o[k,i]=x[k,j];
end
end
end
end
o
end
```

while for the reverse part it is
```function back_segmented_max(x::Matrix,bags::Vector,Δ)
assert(checkbounds(x,bags))
maxI=zeros(Int,size(x,1),length(bags))
gx = zeros(x)
for i in 1:length(bags) #iterate over bags
bagsize=length(bags[i])
for j in bags[i] #iterate over subbags
for k in 1:size(x,1)
if x[k,j]>gx[k,i]
gx[k,i]=x[k,j];
maxI[k,i]=j;
end
end
end
end

fill!(gx,0)
@inbounds for I in CartesianRange(size(Δ))
if maxI[I]>0
gx[I[1],maxI[I]]=Δ[I];
end
end
gx
end
```

I have to confess that I have never programmed for Cuda, therefore I am writing here.
Thanks for any help.