class_docstr: Base class for yet to train and pretrained tokenizers
  
  Implements the ``vocabulary_`` attribute and the methods
  ``get_feature_names``, ``transform``, and ``inverse_transform``.
  
  Subclasses should implement the ``fit``  method.
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code: def vocabulary_(self):
      if not hasattr(self, "fast_tokenizer_"):
          raise AttributeError(
              f"{self.__class__.__name__} has no attribute 'vocabulary_', "
              f"did you fit it first?"
          )
      return self.fast_tokenizer_.vocab
  
imports: import io
  import os
  import pathlib
  from copy import deepcopy
  from operator import itemgetter
  import numpy
  import torch
  from sklearn.base import BaseEstimator, TransformerMixin
  from skorch.callbacks import LRScheduler
  from skorch.dataset import unpack_data
  from skorch.utils import check_is_fitted, params_for
label_desc: Use image node_id 1 for calling the _HuggingfaceTokenizerBase obj's underlying member method code with example usage: obj.vocabulary_() and returns: self