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())
docstr: Build vocab from ``vocab_counter`` which is a vocab count dict.
  
  Parameters
  ----------
  vocab_counter : dict
      Vocab counter.
  max_vocab_size : None, optional
      Maximal word vocab size, default: ``None``.
  min_vocab_freq : int, optional
      Minimal word vocab frequency, default: ``1``.
code: def build_vocab(
      self, vocab_counter, max_vocab_size=None, min_vocab_freq=1
  ):
      """Build vocab from ``vocab_counter`` which is a vocab count dict.
  
      Parameters
      ----------
      vocab_counter : dict
          Vocab counter.
      max_vocab_size : None, optional
          Maximal word vocab size, default: ``None``.
      min_vocab_freq : int, optional
          Minimal word vocab frequency, default: ``1``.
      """
      self.word2count = vocab_counter
      self._add_words(vocab_counter.keys())
      self._trim(
          max_vocab_size=max_vocab_size, min_vocab_freq=min_vocab_freq
      )
  
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 2 for calling the Vocab obj's underlying member method code with example usage: obj.build_vocab(vocab_counter, max_vocab_size, min_vocab_freq) without return types