code: def evaluate(dataloader):
      
      total_L = 0.0
      total_sample_num = 0
      total_correct_num = 0
      start_log_interval_time = time.time()
      print('Begin Testing...')
      for i, ((data, valid_length), label) in enumerate(dataloader):
          data = mx.nd.transpose(data.as_in_context(context))
          valid_length = valid_length.as_in_context(context).astype(np.float32)
          label = label.as_in_context(context)
          output = net(data, valid_length)
          L = loss(output, label)
          pred = (output > 0.5).reshape((-1,))
          total_L += L.sum().asscalar()
          total_sample_num += label.shape[0]
          total_correct_num += (pred == label).sum().asscalar()
          if (i + 1) % args.log_interval == 0:
              print('[Batch {}/{}] elapsed {:.2f} s'.format(
                  i + 1, len(dataloader), time.time() - start_log_interval_time))
              start_log_interval_time = time.time()
      avg_L = total_L / float(total_sample_num)
      acc = total_correct_num / float(total_sample_num)
      return avg_L, acc
docs: Evaluate network on the specified dataset