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 _to_params(self) -> Dict[str, Any]:
      return {
          "type": "spacy",
          "language": self._language,
          "rule_based": self._rule_based,
      }
  
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 4 for calling the SpacySentenceSplitter obj's underlying member method code with example usage: obj._to_params() and returns: dict