class_docstr: Tokenizer that uses the en_core_sci_sm Spacy model and some special heuristics.
  
  Implementation of :class:`Tokenizer` which uses the en_core_sci_sm Spacy model
  extended by special heuristics to consider characters such as "(", ")" "-" as
  additional token separators. The latter distinguishes this implementation from
  :class:`SpacyTokenizer`.
  
  Note, you if you want to use the "normal" SciSpacy tokenization just use
  :class:`SpacyTokenizer`.
class_docstr_tok: ["Tokenizer","that","uses","the","en_core_sci_sm","Spacy","model","and","some","special","heuristics",".","Implementation","of",":","class",":","`","Tokenizer","`","which","uses","the","en_core_sci_sm","Spacy","model","extended","by","special","heuristics","to","consider","characters","such","as","``","(","``",",","``",")","''","``","-","''","as","additional","token","separators",".","The","latter","distinguishes","this","implementation","from",":","class",":","`","SpacyTokenizer","`",".","Note",",","you","if","you","want","to","use","the","``","normal","''","SciSpacy","tokenization","just","use",":","class",":","`","SpacyTokenizer","`","."]
code: def tokenize(self, text: str) -> List[str]:
      sentence = self.model(text)
      words: List[str] = []
      for word in sentence:
          words.append(word.text)
      return words
  
imports: import logging
  import sys
  from abc import ABC, abstractmethod
  from typing import Callable, List
  from segtok.segmenter import split_single
  from segtok.tokenizer import split_contractions, word_tokenizer
label_desc: Use image node_id 2 for calling the SciSpacyTokenizer obj's underlying member method code with example usage: obj.tokenize(text) and returns: words