code: def optimize_batch(self, batchsize=10, returns='best', paralell=True):
          if returns not in ('best', 'all'):
              raise ValueError('returns must be either "best" or "all"')
          starts = [np.random.rand(self.m * 2) * 10 for i in range(batchsize)]
          if paralell:
              with Pool() as p:
                  results = p.map(self.optimize, starts)
          else:
              results = map(self.optimize, starts)
          results = sorted(results, key=lambda x: x.stress)
          return results if returns == 'all' else results[0]