class_docstr: Word piece tokenizer for BERT models.
class_docstr_tok: ["Word","piece","tokenizer","for","BERT","models","."]
docstr: Loads a vocabulary file into a dictionary.
docstr_tok: ["Loads","a","vocabulary","file","into","a","dictionary","."]
code: def load_vocab(vocab_file):
      """Loads a vocabulary file into a dictionary."""
      vocab = OrderedDict()
      with PathManager.open(vocab_file, "r") as reader:
          tokens = reader.readlines()
      for index, token in enumerate(tokens):
          token = token.rstrip("\n")
          vocab[token] = index
      return vocab
  
imports: import copy
  import json
  import re
  from collections import OrderedDict
  from typing import List, NamedTuple, Optional, Union
  import torch
  from fairseq.data.encoders.gpt2_bpe import get_encoder
  from fairseq.data.encoders.gpt2_bpe_utils import Encoder
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType, create_component
  from pytext.data.pickleable_gpt2bpe_encoder import PickleableGPT2BPEEncoder
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer, ScriptWordTokenizer
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from transformers.tokenization_bert import BasicTokenizer, WordpieceTokenizer
label_desc: Use image node_id 3 for calling the WordPieceTokenizer obj's underlying member method code with example usage: obj.load_vocab(vocab_file) and returns: vocab