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: Load pretrained word embeddings for initialization
code: def load_embeddings(
      self,
      pretrained_word_emb_name="840B",
      pretrained_word_emb_url=None,
      pretrained_word_emb_cache_dir=".vector_cache/",
      pretrained_word_emb_dim=300,
      dtype=np.float32,
  ):
      """Load pretrained word embeddings for initialization"""
      word_model = WordEmbModel(
          pretrained_word_emb_name=pretrained_word_emb_name,
          pretrained_word_emb_url=pretrained_word_emb_url,
          pretrained_word_emb_cache_dir=pretrained_word_emb_cache_dir,
          pretrained_word_emb_dim=pretrained_word_emb_dim,
      )
  
      word_list = list(self.word2index.keys())
  
      word_emb, hit, cnt = word_model.get_vecs_by_tokens(
          tokens=word_list, lower_case_backup=self.lower_case
      )
  
      print(
          "Pretrained word embeddings hit ratio: {}".format(hit / cnt)
      )
      self.embeddings = word_emb.numpy()
      self.embeddings[self.PAD] = np.zeros(word_model.dim)
  
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 5 for calling the Vocab obj's underlying member method code with example usage: obj.load_embeddings(pretrained_word_emb_name, pretrained_word_emb_url, pretrained_word_emb_cache_dir, pretrained_word_emb_dim, dtype) without return types