class_docstr: TextEncoder backed by a list of tokens.
  
  Tokenization splits on (and drops) non-alphanumeric characters with
  regex "\W+".
class_docstr_tok: ["TextEncoder","backed","by","a","list","of","tokens",".","Tokenization","splits","on","(","and","drops",")","non-alphanumeric","characters","with","regex","``","\\W+","''","."]
code: def decode(self, ids):
      ids = pad_decr(ids)
  
      tokens = []
      for int_id in ids:
          if int_id < len(self._vocab_list):
              tokens.append(self._vocab_list[int_id])
          else:
              tokens.append(self._oov_token)
      return self._decode_token_separator.join(tokens)
  
imports: from __future__ import unicode_literals
  import abc
  import hashlib
  import json
  import re
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 3 for calling the TokenTextEncoder obj's underlying member method code with example usage: obj.decode(ids) and returns: self