docstr: Create iterator objects for splits of the SNLI 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.
      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.
      vectors: one of the available pretrained vectors or a list with each
          element one of the available pretrained vectors (see Vocab.load_vectors)
      trees: Whether to include shift-reduce parser transitions.
          Default: False.
      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,
      trees=False,
      **kwargs,
  ):
      """Create iterator objects for splits of the SNLI 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.
          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.
          vectors: one of the available pretrained vectors or a list with each
              element one of the available pretrained vectors (see Vocab.load_vectors)
          trees: Whether to include shift-reduce parser transitions.
              Default: False.
          Remaining keyword arguments: Passed to the splits method.
      """
      if trees:
          TEXT = ParsedTextField()
          TRANSITIONS = ShiftReduceField()
      else:
          TEXT = data.Field(tokenize="spacy")
          TRANSITIONS = None
      LABEL = data.Field(sequential=False)
  
      train, val, test = cls.splits(
          TEXT, LABEL, TRANSITIONS, 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
      )
  
file_path: decanlp/text/torchtext/datasets/snli.py
imports: from ..None import data
label_desc: Use image node_id 3 for calling the SNLI obj's underlying member method code with example usage: obj.iters(cls, batch_size, device, root, vectors, trees) and returns: data