code: def infer(self, input_data, input_label):
          
          sum_losses = 0
          len_losses = 0
          for data, label in zip(input_data, input_label):
              pred = self.net(data)
              sum_losses += mx.nd.array(self.loss_fn(pred, label)).sum().asscalar()
              len_losses += len(data)
              pred_convert = char_beam_search(pred)
              label_convert = char_conv(label.asnumpy())
              for target, pred in zip(label_convert, pred_convert):
                  print("target:{t}  pred:{p}".format(t=target, p=pred))
          return sum_losses, len_losses
docs: Description : Print sentence for prediction result