class_docstr: Generic class for other subclasses to extend from. This typically
  either splits text into word tokens or character tokens.
class_docstr_tok: ["Generic","class","for","other","subclasses","to","extend","from",".","This","typically","either","splits","text","into","word","tokens","or","character","tokens","."]
docstr: Splitting a corpus with multiple text documents.
  
  Parameters
  ----------
  texts: List[str], required
      Input list of texts to be tokenized.
  
  Returns
  -------
  tokens : ``List[List[str]]``
docstr_tok: ["Splitting","a","corpus","with","multiple","text","documents",".","Parameters","--","--","--","--","--","texts",":","List","[","str","]",",","required","Input","list","of","texts","to","be","tokenized",".","Returns","--","--","--","-","tokens",":","``","List","[","List","[","str","]","]","``"]
code: def batch_tokenize(self, texts: List[str]) -> List[List[str]]:
      """
      Splitting a corpus with multiple text documents.
  
      Parameters
      ----------
      texts: List[str], required
          Input list of texts to be tokenized.
  
      Returns
      -------
      tokens : ``List[List[str]]``
      """
      raise NotImplementedError
  
imports: from typing import List, Dict, Callable, Union
  from collections import defaultdict, Counter, OrderedDict
  import string
  import pickle
  import re
  import numpy
  import scipy.sparse
  from .None import FeatureModality
  from .modality import fallback_feature
  from ..utils import normalize
label_desc: Use image node_id 2 for calling the Tokenizer obj's underlying member method code with example usage: obj.batch_tokenize(texts) without return types