Code from GitHub example fails

On Julia 1.9.2 with GraphsMatching 0.2.0, the code from the introductory example does not run:

julia> using Graphs, GraphsMatching
julia> g = complete_graph(3)
julia> w = zeros(3,3)
julia> w[1,2] = 1
julia> w[3,2] = 1
julia> w[1,3] = 1
julia> match = maximum_weight_matching(g, with_optimizer(Cbc.Optimizer, logLevel=0), w)
ERROR: UndefVarError: `with_optimizer` not defined

After digging around, I succeeded with:

julia> match = maximum_weight_matching(g, JuMP.optimizer_with_attributes(Cbc.Optimizer,"LogLevel"=>0), w)
Welcome to the CBC MILP Solver 
Version: 2.10.8 
Build Date: Jan  1 1970 

command line - Cbc_C_Interface -LogLevel 0 -solve -quit (default strategy 1)
MatchingResult{Float64}(1.0, [2, 1, -1])

On an unrelated note, I have a question: does the graph optimization ecosystem in Julia cover the functionality of scipy.sparse.csgraph.min_weight_full_bipartite_matching?

Locate the introductory example referenced in the issue and run it with Julia 1.9.2 and GraphsMatching 0.2.0, starting from the failing with_optimizer call. Update the example so it uses the currently supported optimizer API, then rerun it to confirm the matching result; separately determine whether the scipy sparse full bipartite matching question belongs in this issue.