class_docstr: A ``WordSplitter`` splits strings into words.  This is typically called a "tokenizer" in NLP,
  because splitting strings into characters is trivial, but we use ``Tokenizer`` to refer to the
  higher-level object that splits strings into tokens (which could just be character tokens).
  So, we're using "word splitter" here for this.
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docstr: Spacy needs to do batch processing, or it can be really slow.  This method lets you take
  advantage of that if you want.  Default implementation is to just iterate of the sentences
  and call ``split_words``, but the ``SpacyWordSplitter`` will actually do batched
  processing.
docstr_tok: ["Spacy","needs","to","do","batch","processing",",","or","it","can","be","really","slow",".","This","method","lets","you","take","advantage","of","that","if","you","want",".","Default","implementation","is","to","just","iterate","of","the","sentences","and","call","``","split_words","``",",","but","the","``","SpacyWordSplitter","``","will","actually","do","batched","processing","."]
code: def batch_split_words(self, sentences):
      """
      Spacy needs to do batch processing, or it can be really slow.  This method lets you take
      advantage of that if you want.  Default implementation is to just iterate of the sentences
      and call ``split_words``, but the ``SpacyWordSplitter`` will actually do batched
      processing.
      """
      return [self.split_words(sentence) for sentence in sentences]
  
imports: from __future__ import absolute_import
  import re
  import spacy
  from allennlp.common import Registrable
  from allennlp.common.util import get_spacy_model
  from allennlp.data.tokenizers.token import Token
label_desc: Use image node_id 1 for calling the WordSplitter obj's underlying member method code with example usage: obj.batch_split_words(sentences) and returns: unknown