Add attributes jump_variables and jump_constraints in the model

[amontoison]

cc @michel2323

using NLPModels, NLPModelsJuMP, JuMP

x0 = [-1.2; 1.0]
model = Model() # No solver is required
@variable(model, x[i=1:2], start=x0[i])
@objective(model, Min, (x[1] - 1)^2 + 100 * (x[2] - x[1]^2)^2)

nlp = MathOptNLPModel(model)
nlp.jump_variables

julia> nlp.jump_variables
Dict{String, Int64} with 2 entries:
  "x[2]" => 2
  "x[1]" => 1

[amontoison]

@blegat Do you have an idea why the tests are failing?

[blegat]

MOI.copy_to should return the index_map

[blegat]

This is what is making the tests fail

[amontoison]

The index_map was just a dummy dict VariableRef(
i) => VariableRef(i).
Why do you need it?
Is it because of bridges?