  
      dataset = PTBDataset(all_centers, all_contexts, all_negatives)
  
      data_iter = paddle.io.DataLoader(
          dataset, batch_size=batch_size, shuffle=True,
          collate_fn=batchify, num_workers=num_workers)
      return data_iter, vocab
  
  d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + 'glove.6B.50d.zip',
                                  '0b8703943ccdb6eb788e6f091b8946e82231bc4d')
 
</s> remove         drop_last=True, num_workers=num_workers)
 </s> add         drop_last=True, return_list=True, num_workers=num_workers) </s> remove         shuffle=True, drop_last=True, num_workers=num_workers)
 </s> add         shuffle=True, return_list=True, drop_last=True, num_workers=num_workers) </s> remove     train_iter = paddle.io.DataLoader(dataset=train_set, batch_size=batch_size,
 </s> add     train_iter = paddle.io.DataLoader(dataset=train_set, batch_size=batch_size, return_list=True, </s> remove                                            batch_size=batch_size)
 </s> add                                            batch_size=batch_size, return_list=True)
        dataset, batch_size=batch_size, shuffle=True, return_list=True,
  
      dataset = PTBDataset(all_centers, all_contexts, all_negatives)
  
      data_iter = paddle.io.DataLoader(
        dataset, batch_size=batch_size, shuffle=True, return_list=True,
          collate_fn=batchify, num_workers=num_workers)
      return data_iter, vocab
  
  d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + 'glove.6B.50d.zip',
                                  '0b8703943ccdb6eb788e6f091b8946e82231bc4d')
keep keep keep keep replace keep keep keep keep keep
 [PaddlePaddle]Remove multi gpu training (#1205)

* Remove multi gpu training

* Try to Add return list for avoiding runtime error

* Set num_workers to 0

* Remove numbers of fcn.md

* Fix natural-language-inference-attention.md&bert-pretraining.md 
d2l/paddle.py