class_docstr: Vocab class.
  
  Parameters
  ----------
  lower_case : boolean
      Specify whether to lowercase text, default: ``True``.
  tokenizer: function, optional
      Word tokenization function, default: nltk.tokenize.word_tokenize.
  
  Examples
  -------
  >>> word_vocab = Vocab()
  >>> word_vocab.build_vocab({'i': 10, 'like': 5, 'nlp': 3})
  >>> print(word_vocab.get_vocab_size())
code: def to_index_sequence(self, sentence):
      sentence = sentence.strip()
      if self.lower_case:
          sentence = sentence.lower()
  
      seq = []
      if self.tokenizer is None:
          for word in sentence.split():
              idx = self.getIndex(word)
              seq.append(idx)
      else:
          for word in self.tokenizer(sentence):
              idx = self.getIndex(word)
              seq.append(idx)
      return seq
  
imports: import os
  import pickle
  import re
  import warnings
  from collections import Counter
  from functools import lru_cache
  import numpy
  import torch
  from nltk.tokenize import word_tokenize
  from torchtext.vocab import GloVe, Vectors
  from .None import constants
label_desc: Use image node_id 14 for calling the Vocab obj's underlying member method code with example usage: obj.to_index_sequence(sentence) and returns: seq