code: def iters(cls, batch_size=32, bptt_len=35, device=0, root='.data',
                vectors=None, **kwargs):
          
          TEXT = data.Field()
  
          train, val, test = cls.splits(TEXT, root=root, **kwargs)
  
          TEXT.build_vocab(train, vectors=vectors)
  
          return data.BPTTIterator.splits(
              (train, val, test), batch_size=batch_size, bptt_len=bptt_len,
              device=device)
docs: Create iterator objects for splits of the WikiText-2 dataset.
  
          This is the simplest way to use the dataset, and assumes common
          defaults for field, vocabulary, and iterator parameters.
  
          Arguments:
              batch_size: Batch size.
              bptt_len: Length of sequences for backpropagation through time.
              device: Device to create batches on. Use -1 for CPU and None for
                  the currently active GPU device.
              root: The root directory that the dataset's zip archive will be
                  expanded into; therefore the directory in whose wikitext-2
                  subdirectory the data files will be stored.
              wv_dir, wv_type, wv_dim: Passed to the Vocab constructor for the
                  text field. The word vectors are accessible as
                  train.dataset.fields['text'].vocab.vectors.
              Remaining keyword arguments: Passed to the splits method.