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: Use random word embeddings for initialization.
code: def randomize_embeddings(self, n_dims, scale=0.08):
      """Use random word embeddings for initialization."""
      vocab_size = self.get_vocab_size()
      shape = (vocab_size, n_dims)
      self.embeddings = np.array(
          np.random.uniform(low=-scale, high=scale, size=shape),
          dtype=np.float32,
      )
      self.embeddings[self.PAD] = np.zeros(n_dims)
  
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 6 for calling the Vocab obj's underlying member method code with example usage: obj.randomize_embeddings(n_dims, scale) without return types