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.
class_docstr_tok: ["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","."]
docstr: Decode encodings back into strings
  
  Be aware that depending on the tokenizer used, the tokenization can lead
  to loss of information (e.g. words outside the vocabulary). In that
  case, the inverse transformation might not restore the original string
  completely.
  
  X : transformers.tokenization_utils_base.BatchEncoding
    The transformed object obtained from calling ``.transform``.
  
  Returns
  -------
  Xt : list of str
    The decoded text.
docstr_tok: ["Decode","encodings","back","into","strings","Be","aware","that","depending","on","the","tokenizer","used",",","the","tokenization","can","lead","to","loss","of","information","(","e.g",".","words","outside","the","vocabulary",")",".","In","that","case",",","the","inverse","transformation","might","not","restore","the","original","string","completely",".","X",":","transformers.tokenization_utils_base.BatchEncoding","The","transformed","object","obtained","from","calling","``",".transform","``",".","Returns","--","--","--","-","Xt",":","list","of","str","The","decoded","text","."]
code: def inverse_transform(self, X):
      """Decode encodings back into strings
  
      Be aware that depending on the tokenizer used, the tokenization can lead
      to loss of information (e.g. words outside the vocabulary). In that
      case, the inverse transformation might not restore the original string
      completely.
  
      X : transformers.tokenization_utils_base.BatchEncoding
        The transformed object obtained from calling ``.transform``.
  
      Returns
      -------
      Xt : list of str
        The decoded text.
  
      """
      check_is_fitted(self, ["fast_tokenizer_"])
  
      Xt = self.fast_tokenizer_.batch_decode(
          X["input_ids"],
          skip_special_tokens=True,
          clean_up_tokenization_spaces=True,
      )
      return np.asarray(Xt)
  
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 6 for calling the _HuggingfaceTokenizerBase obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: np