code: def evaluate(self, model):
          
          with model:
              if self.medium is not None:
                  self.medium.apply(model)
              if self.objective is not None:
                  model.objective = self.objective
              model.add_cons_vars(self.constraints)
              max_val = model.slim_optimize()
              essen = single_gene_deletion(
                  model, gene_list=self.data["gene"], processes=1)
          essen["gene"] = [list(g)[0] for g in essen.index]
          essen.index = essen["gene"]
          essen["essential"] = (essen["growth"] < (max_val * 0.1)) \
              | essen["growth"].isna()
          return essen
docs: Use the defined parameters to predict single gene essentiality.