class_docstr: ACTG strings to ints and back. Optionally chunks bases into single ids.
  
  To use a different character set, subclass and set BASES to the char set. UNK
  and PAD must not appear in the char set, but can also be reset.
  
  Uses 'N' as an unknown base.
class_docstr_tok: ["ACTG","strings","to","ints","and","back",".","Optionally","chunks","bases","into","single","ids",".","To","use","a","different","character","set",",","subclass","and","set","BASES","to","the","char","set",".","UNK","and","PAD","must","not","appear","in","the","char","set",",","but","can","also","be","reset",".","Uses","'N","'","as","an","unknown","base","."]
code: def _tokens(self):
      chunks = []
      for size in range(1, self._chunk_size + 1):
          c = itertools.product(self.BASES + [self.UNK], repeat=size)
          num_pad = self._chunk_size - size
          padding = (self.PAD,) * num_pad
          c = [el + padding for el in c]
          chunks.extend(c)
      return chunks
  
file_path: tensor2tensor/tensor2tensor/data_generators/dna_encoder.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from six.moves import range
  from tensor2tensor.data_generators import text_encoder
label_desc: Use image node_id 2 for calling the DNAEncoder obj's underlying member method code with example usage: obj._tokens() and returns: chunks