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 initialize(self, name=None):
      with ops.name_scope(name, "%sInitialize" % type(self).__name__):
          (finished, next_inputs) = self._initialize_fn()
          if self._batch_size is None:
              self._batch_size = array_ops.size(finished)
      return (finished, next_inputs)
  
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 3 for calling the CustomHelper obj's underlying member method code with example usage: obj.initialize(name) and returns: finished,  next_inputs