How to use MIPSOL_SOL in gurobipy callback to write solutions meeting certain criteria

Hello,

I am trying to write .sol files in an MIPSOL once the solutions meet some criteria. I looked into several posts, like this, that, and this. But none of these helped all that much. Below is a general structure of the code.

class lazy_callback:
    def __init__(self, sol_cnt, s, x):
        self.sol_cnt = sol_cnt
        self.s = s
        self.x = x

    def __call__(self, model, where):
        if where == gp.GRB.Callback.MIPSOL:
            try:
                self.add_cuts(model)
            except Exception:
                logging.exception("Exception occurred in MIPSOL callback")
                model.terminate()

    def add_cuts(self, model):
        s_vals = model.cbGetSolution(self.s)
        x_vals = model.cbGetSolution(self.x)
        sol = gp.GRB.Callback.MIPSOL_SOL
        C_lower = model.cbGet(gp.GRB.Callback.MIPSOL_OBJBND)

        C_hat = "some_float_value" # Calculating this somehow

        solution_gap = 0 if C_hat == 0 else 1 - (C_lower / C_hat)
        
        if solution_gap < 0.8:
            # model.write(str(exact_dir / f"solution_count_{self.sol_cnt}.sol"))
            write_solutions(self.sol_cnt, s_vals)
            self.sol_cnt += 1

        for i in I:
            if something is True: ## assume something is boolean
                model.cbLazy(self.s[i] >= self.x[i])

Before my m.optimize(lazy_callback(sol_cnt, s, x)), I added "m.Params.SolFiles = str(exact_dir / 'solution_count')". It only writes one solution file although the lazy constraints are added several times. I verified this with an external function called write_solutions(self.sol_cnt, s_vals).

Then, I tried "model.write(str(exact_dir / f"solution_count_{self.sol_cnt}.sol"))", but it did not help and threw an error in callback. If there is a better solution, I do not want to create a custom writer.

Another problem is indeed "m.optimize(lazy_callback(sol_cnt, s, x))". In Jupyter Notebook, this throws error: "GurobiError: Callback argument must be a function". Looking into solutions to this, I ended up using something like:

cb = lazy_callback(sol_cnt, s, x)
m.optimize(lambda model, where: cb(model, where))

Is there a more elegant solution to this, and what is wrong with the former implementation?

Thanks,

Taner