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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def find_inconsistent_min_stoichiometry(model, atol=1e-13):
 """ Detect inconsistent minimal net stoichiometries. Parameters model : cobra.Model The metabolic model under investigation. atol : float, optional Values below the absolute tolerance are treated as zero. Expected to be very small but larger than zero. Notes ----- See [1]_ section 3.3 for a complete description of the algorithm. References .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008):
 2245. """

    if check_stoichiometric_consistency(model):
        return set()
    Model, Constraint, Variable, Objective = con_helpers.get_interface(model)
    unconserved_mets = find_unconserved_metabolites(model)
    LOGGER.info("model has %d unconserved metabolites", len(unconserved_mets))
    internal_rxns = con_helpers.get_internals(model)
    internal_mets = set(
        met for rxn in internal_rxns for met in rxn.metabolites)
    get_id = attrgetter("id")
    reactions = sorted(internal_rxns, key=get_id)
    metabolites = sorted(internal_mets, key=get_id)
    stoich, met_index, rxn_index = con_helpers.stoichiometry_matrix(
        metabolites, reactions)
    left_ns = con_helpers.nullspace(stoich.T)
    # deal with numerical instabilities
    left_ns[np.abs(left_ns) < atol] = 0.0
    LOGGER.info("nullspace has dimension %d", left_ns.shape[1])
    inc_minimal = set()
    (problem, indicators) = con_helpers.create_milp_problem(
        left_ns, metabolites, Model, Variable, Constraint, Objective)
    LOGGER.debug(str(problem))
    cuts = list()
    for met in unconserved_mets:
        row = met_index[met]
        if (left_ns[row] == 0.0).all():
            LOGGER.debug("%s: singleton minimal unconservable set", met.id)
            # singleton set!
            inc_minimal.add((met,))
            continue
        # expect a positive mass for the unconserved metabolite
        problem.variables[met.id].lb = 1e-3
        status = problem.optimize()
        while status == "optimal":
            LOGGER.debug("%s: status %s", met.id, status)
            LOGGER.debug("sum of all primal values: %f",
                         sum(problem.primal_values.values()))
            LOGGER.debug("sum of binary indicators: %f",
                         sum(var.primal for var in indicators))
            solution = [model.metabolites.get_by_id(var.name[2:])
                        for var in indicators if var.primal > 0.2]
            LOGGER.debug("%s: set size %d", met.id, len(solution))
            inc_minimal.add(tuple(solution))
            if len(solution) == 1:
                break
            cuts.append(con_helpers.add_cut(
                problem, indicators, len(solution) - 1, Constraint))
            status = problem.optimize()
        LOGGER.debug("%s: last status %s", met.id, status)
        # reset
        problem.variables[met.id].lb = 0.0
        problem.remove(cuts)
        cuts.clear()
    return inc_minimal