class_docstr: A `SentenceSplitter` that uses spaCy's built-in sentence boundary detection.
  
  Spacy's default sentence splitter uses a dependency parse to detect sentence boundaries, so
  it is slow, but accurate.
  
  Another option is to use rule-based sentence boundary detection. It's fast and has a small memory footprint,
  since it uses punctuation to detect sentence boundaries. This can be activated with the `rule_based` flag.
  
  By default, `SpacySentenceSplitter` calls the default spacy boundary detector.
  
  Registered as a `SentenceSplitter` with name "spacy".
code: def split_sentences(self, text: str) -> List[str]:
      if self._is_version_3:
          return [sent.text.strip() for sent in self.spacy(text).sents]
      else:
          return [
              sent.string.strip() for sent in self.spacy(text).sents
          ]
  
file_path: allennlp/allennlp/data/tokenizers/sentence_splitter.py
imports: from typing import List, Dict, Any
  import spacy
  from allennlp.common import Registrable
  from allennlp.common.util import get_spacy_model
label_desc: Use image node_id 2 for calling the SpacySentenceSplitter obj's underlying member method code with example usage: obj.split_sentences(text) and returns: unknown, unknown