text: 
  
  TITLE
  Bert2bert EncoderDecoderModel from Huggingface is generating a zero tensor for any input
  
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  I am using Bert2Bert EncoderDecoderModel from Huggingface for sentence simplification. But my model is generating a zero tensor of the same length regardless of the input. Could someone help me what is wrong in my project. The GitHub link of my project is this https://github.com/Aakash12980/Sentence-Simplification-using-Transformer. Please help me through this.
  
  My decode module is like this:
  `
  @task.command()
  @click.option('--src_test', default="./drive/My Drive/Mini Project/dataset/src_test.txt", help="test source file path")
  @click.option('--tgt_test', default="./drive/My Drive/Mini Project/dataset/tgt_test.txt", help="test target file path")
  @click.option('--best_model', default="./drive/My Drive/Mini Project/best_model/model.pt", help="best model file path")
  
  def test(**kwargs):
  
      print("Testing Model module executing...")
      logging.info(f"Test module invoked.")
  
      src_test = open_file(kwargs['src_test'])
      tgt_test = open_file(kwargs['tgt_test'])
      len_data = len(src_test)
  
      test_dataset = WikiDataset(src_test, tgt_test)
  
      test_dl = DataLoader(test_dataset, batch_size=TRAIN_BATCH_SIZE, collate_fn=collate_fn, shuffle=True)
  
      _,_,_ = load_checkpt(kwargs["best_model"])
      print("Model loaded...")
      model.to(device)
      model.eval()
  
      test_start_time = time.time()
      epoch_test_loss = evaluate(test_dl, 0)
      epoch_test_loss = epoch_test_loss/len_data
      print(f'avg. test loss: {epoch_test_loss:.5f} | time elapsed: {time.time() - test_start_time}')
      logging.info(f'avg. test loss: {epoch_test_loss:.5f} | time elapsed: {time.time() - test_start_time}')
      print("Test Complete!")
   `
  and the output its generates is this: 
  
  `input: tensor([[  101,  1188,  1108,  1272, 11310,  1125,  1136,  1151,  2548,  1106,
            2732, 20386,  2692,   117,  2693,  1117, 16975,   117,  1105,  1103,
            8183,  1571,  1105,  8183,  1545,  3784,   118,  8581,  1125,  3335,
            1136,  1678,  2629,   119,   102]])
  output: tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
  input: tensor([[  101,  1109,  4665,  1108,  6497,  1173,  1112,  1126,   169,   169,
            6548, 21718, 24226, 25285,   112,   112,   117,  1112,  1103,  1586,
            1108,   169,   169, 16314,  1105,  4489,  1200,  1190,  1103,  6188,
             117,  4411,  1126,  5340,  1895,  4252, 20473,  4626,  2116,  1104,
            9494,   112,   112,   119,   102]])
  output: tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
  input: tensor([[  101, 13030,  1643,  2883,  1144,  3567,  8593,  9648,  1958,   117,
            1259,  1210,  4748,  1453,  4278,   117,  1160,  1635,  8793,   112,
            1635,  4278,  1105,   170,  6068,  1635,  1509,   119,   102]])
  output: tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])`
  