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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docstr: Array mapping from feature integer indices to feature name.
  
  Parameters
  ----------
  input_features : array-like of str or None, default=None
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  feature_names_out : ndarray of str objects
      Transformed feature names.
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code: def get_feature_names_out(self, input_features=None):
      """Array mapping from feature integer indices to feature name.
  
      Parameters
      ----------
      input_features : array-like of str or None, default=None
          Not used, present here for API consistency by convention.
  
      Returns
      -------
      feature_names_out : ndarray of str objects
          Transformed feature names.
  
      """
      # Note: Same implementation as sklearn's CountVectorizer
      return np.asarray(
          [
              t
              for t, i in sorted(
                  self.vocabulary_.items(), key=itemgetter(1)
              )
          ],
          dtype=object,
      )
  
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 2 for calling the _HuggingfaceTokenizerBase obj's underlying member method code with example usage: obj.get_feature_names_out(input_features) and returns: np