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)
code: def __init__(
      self,
      model: "tokenizers.models.Model",
      trainer: "tokenizers.trainers.Trainer",
  ):
      from tokenizers import Tokenizer
  
      self.model = model
      self.trainer = trainer
      self.tokenizer = Tokenizer(model)
  
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 1 to create a new HuggingFaceVocabularyBuilder object from inherited base classes: VocabularyBuilder with example: obj = HuggingFaceVocabularyBuilder(model, trainer)