docstr: Splits input text into sentences and adds start and end token to each sentence. 
docstr_tok: ["Splits","input","text","into","sentences","and","adds","start","and","end","token","to","each","sentence","."]
code: def __call__(self, data: Tuple[str, str]) -> Tuple[str, str]:
      """Splits input text into sentences and adds start and end token to each sentence."""
      text_encoder, text_decoder = data[0], data[1]
      doc = self.nlp(text_encoder)
      sentences = [self._process_sentence(s) for s in doc.sents]
      text_encoder = " ".join(sentences)
      text_decoder = (
          self.start_token + " " + text_decoder + " " + self.end_token
      )
      return text_encoder, text_decoder
  
imports: from typing import Tuple
  from spacy.pipeline.pipes import Language
label_desc: Use image node_id 2 for calling the BertPreprocessor obj's underlying member method code with example usage: obj.__call__(data) and returns: text_encoder,  text_decoder