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 _oov_bucket(self, token):
      if self._oov_buckets <= 0:
          return None
      if self._oov_buckets == 1:
          return len(self._vocab_list)
      hash_val = int(
          hashlib.md5(tf.compat.as_bytes(token)).hexdigest(), 16
      )
      return len(self._vocab_list) + hash_val % self._oov_buckets
  
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 9 for calling the TokenTextEncoder obj's underlying member method code with example usage: obj._oov_bucket(token) and returns: unknown, None, len