class_docstr: Base abstract class that allows the user to customize sampling.
class_docstr_tok: ["Base","abstract","class","that","allows","the","user","to","customize","sampling","."]
code: def batch_size(self):
      if self._batch_size is None:
          raise ValueError(
              "batch_size accessed before initialize was called"
          )
      return self._batch_size
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  from tensorflow.contrib.distributions.python.ops import bernoulli
  from tensorflow.contrib.distributions.python.ops import categorical
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.layers import base
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.util import nest
  from seq2seq.contrib.seq2seq import decoder
label_desc: Use image node_id 2 for calling the CustomHelper obj's underlying member method code with example usage: obj.batch_size() and returns: self