code: def evaluate():
      
      print(eval_model)
      eval_model.initialize(mx.init.Xavier(), ctx=context[0])
      eval_model.hybridize(static_alloc=True, static_shape=True)
      epoch = args.from_epoch if args.from_epoch else 0
      while epoch < args.epochs:
          checkpoint_name = '%s.%s'%(args.save, format(epoch, '02d'))
          if not os.path.exists(checkpoint_name):
              print('Wait for a new checkpoint...')
              # check again after 600 seconds
              time.sleep(600)
              continue
          eval_model.load_parameters(checkpoint_name)
          print('Loaded parameters from checkpoint %s'%(checkpoint_name))
          start_epoch_time = time.time()
          final_test_L = test(test_data, test_batch_size, ctx=context[0])
          end_epoch_time = time.time()
          print('[Epoch %d] test loss %.2f, test ppl %.2f'%
                (epoch, final_test_L, math.exp(final_test_L)))
          print('Epoch %d took %.2f seconds.'%(epoch, end_epoch_time - start_epoch_time))
          sys.stdout.flush()
          epoch += 1
docs: Evaluate loop for the trained model