code: def add_lp_feasibility(model):
      
  
      obj_vars = []
      prob = model.problem
      for met in model.metabolites:
          s_plus = prob.Variable("s_plus_" + met.id, lb=0)
          s_minus = prob.Variable("s_minus_" + met.id, lb=0)
  
          model.add_cons_vars([s_plus, s_minus])
          model.constraints[met.id].set_linear_coefficients(
              {s_plus: 1.0, s_minus: -1.0})
          obj_vars.append(s_plus)
          obj_vars.append(s_minus)
  
      model.objective = prob.Objective(Zero, sloppy=True, direction="min")
      model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars})
docs: Add a new objective and variables to ensure a feasible solution.
  
      The optimized objective will be zero for a feasible solution and otherwise
      represent the distance from feasibility (please see [1]_ for more
      information).
  
      Parameters
      ----------
      model : cobra.Model
          The model whose feasibility is to be tested.
  
      References
      ----------
      .. [1] Gomez, Jose A., Kai Höffner, and Paul I. Barton.
      “DFBAlab: A Fast and Reliable MATLAB Code for Dynamic Flux Balance
      Analysis.” BMC Bioinformatics 15, no. 1 (December 18, 2014): 409.
      https://doi.org/10.1186/s12859-014-0409-8.