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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 flux_minimization(model, fixed, solver, weights={}):
 """Minimize flux of all reactions while keeping certain fluxes fixed. The fixed reactions are given in a dictionary as reaction id to value mapping. The weighted L1-norm of the fluxes is minimized. Args: model: MetabolicModel to solve. fixed: dict of additional lower bounds on reaction fluxes. solver: LP solver instance to use. weights: dict of weights on the L1-norm terms. Returns: An iterator of reaction ID and reaction flux pairs. """


    fba = FluxBalanceProblem(model, solver)

    for reaction_id, value in iteritems(fixed):
        flux = fba.get_flux_var(reaction_id)
        fba.prob.add_linear_constraints(flux >= value)

    fba.minimize_l1()

    return ((reaction_id, fba.get_flux(reaction_id))
            for reaction_id in model.reactions)