class_docstr: Wrapper class that wraps HuggingFace tokenizers as DeepChem featurizers
  
  The `HuggingFaceFeaturizer` wrapper provides a wrapper
  around Hugging Face tokenizers allowing them to be used as DeepChem
  featurizers. This might be useful in scenarios where user needs to use
  a hugging face tokenizer when loading a dataset.
  
  Example
  -------
  >>> from deepchem.feat import HuggingFaceFeaturizer
  >>> from transformers import RobertaTokenizerFast
  >>> hf_tokenizer = RobertaTokenizerFast.from_pretrained("seyonec/PubChem10M_SMILES_BPE_60k")
  >>> featurizer = HuggingFaceFeaturizer(tokenizer=hf_tokenizer)
  >>> result = featurizer.featurize(['CC(=O)C'])
class_docstr_tok: ["Wrapper","class","that","wraps","HuggingFace","tokenizers","as","DeepChem","featurizers","The","`","HuggingFaceFeaturizer","`","wrapper","provides","a","wrapper","around","Hugging","Face","tokenizers","allowing","them","to","be","used","as","DeepChem","featurizers",".","This","might","be","useful","in","scenarios","where","user","needs","to","use","a","hugging","face","tokenizer","when","loading","a","dataset",".","Example","--","--","--","-",">",">",">","from","deepchem.feat","import","HuggingFaceFeaturizer",">",">",">","from","transformers","import","RobertaTokenizerFast",">",">",">","hf_tokenizer","=","RobertaTokenizerFast.from_pretrained","(","``","seyonec\/PubChem10M_SMILES_BPE_60k","''",")",">",">",">","featurizer","=","HuggingFaceFeaturizer","(","tokenizer=hf_tokenizer",")",">",">",">","result","=","featurizer.featurize","(","[","'CC","(","=O",")","C","'","]",")"]
docstr: Initializes a tokenizer wrapper
  
  Parameters
  ----------
  tokenizer: transformers.tokenization_utils_fast.PreTrainedTokenizerFast
      The tokenizer to use for featurization
docstr_tok: ["Initializes","a","tokenizer","wrapper","Parameters","--","--","--","--","--","tokenizer",":","transformers.tokenization_utils_fast.PreTrainedTokenizerFast","The","tokenizer","to","use","for","featurization"]
code: def __init__(
      self,
      tokenizer: "transformers.tokenization_utils_fast.PreTrainedTokenizerFast",
  ):
      """Initializes a tokenizer wrapper
  
      Parameters
      ----------
      tokenizer: transformers.tokenization_utils_fast.PreTrainedTokenizerFast
          The tokenizer to use for featurization
      """
      self.tokenizer = tokenizer
  
imports: from typing import TYPE_CHECKING
  from deepchem.feat.base_classes import Featurizer
label_desc: Use image node_id 1 to create a new HuggingFaceFeaturizer object from inherited base classes: Featurizer with example: obj = HuggingFaceFeaturizer(tokenizer)