class_docstr: Wrapper for building vocabulary from HuggingFace algorithms
  
  The wrapper class can be used to build huggingface vocabulary building algorithms
  with DeepChem.
  
  Parameters
  ----------
  model: tokenizers.models.Model
      The core tokenization algorithm to learn the vocabulary.
  trainers: tokenizers.trainers.Trainer
      The trainer capable of training the model
  
  Example
  -------
  >>> from tokenizers import models, trainers
  >>> from tokenizers.pre_tokenizers import Whitespace
  >>> model = models.BPE(unk_token="[UNK]")
  >>> special_tokens = ["[UNK]"]
  >>> trainer = trainers.BpeTrainer(vocab_size=20, special_tokens=special_tokens)
  >>> vb = HuggingFaceVocabularyBuilder(model=model, trainer=trainer)
docstr: Loads vocabulary from a vocabulary file
  
  Parameters
  ----------
  fname: str
      Filename to load vocabulary from
docstr_tok: ["Loads","vocabulary","from","a","vocabulary","file","Parameters","--","--","--","--","--","fname",":","str","Filename","to","load","vocabulary","from"]
code: def load(
      cls, fname: str
  ) -> "transformers.tokenization_utils_fast.PreTrainedTokenizerFast":
      """Loads vocabulary from a vocabulary file
  
      Parameters
      ----------
      fname: str
          Filename to load vocabulary from
      """
      from transformers import PreTrainedTokenizerFast
  
      tokenizer = PreTrainedTokenizerFast(tokenizer_file=fname)
      return tokenizer
  
file_path: deepchem/deepchem/feat/vocabulary_builders/hf_vocab.py
imports: from deepchem.feat.vocabulary_builders.vocabulary_builder import VocabularyBuilder
  from typing import List, TYPE_CHECKING
label_desc: Use image node_id 3 for calling the HuggingFaceVocabularyBuilder obj's underlying member method code with example usage: obj.load(cls, fname) and returns: tokenizer