docstr: Create iterator objects for splits of the SST dataset.
  
  Args:
      batch_size: Batch_size
      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 trees
          subdirectory the data files will be stored.
      vectors: one of the available pretrained vectors or a list with each
          element one of the available pretrained vectors (see Vocab.load_vectors)
      Remaining keyword arguments: Passed to the splits method.
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code: def iters(
      cls, batch_size=32, device=0, root=".data", vectors=None, **kwargs
  ):
      """Create iterator objects for splits of the SST dataset.
  
      Args:
          batch_size: Batch_size
          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 trees
              subdirectory the data files will be stored.
          vectors: one of the available pretrained vectors or a list with each
              element one of the available pretrained vectors (see Vocab.load_vectors)
          Remaining keyword arguments: Passed to the splits method.
      """
      TEXT = data.Field()
      LABEL = data.Field(sequential=False)
  
      train, val, test = cls.splits(TEXT, LABEL, root=root, **kwargs)
  
      TEXT.build_vocab(train, vectors=vectors)
      LABEL.build_vocab(train)
  
      return data.BucketIterator.splits(
          (train, val, test), batch_size=batch_size, device=device
      )
  
imports: import os
  from ..None import data
label_desc: Use image node_id 4 for calling the SST obj's underlying member method code with example usage: obj.iters(cls, batch_size, device, root, vectors) and returns: data