class_docstr: A helper for use during inference.
  
  Uses the argmax of the output (treated as logits) and passes the
  result through an embedding layer to get the next input.
class_docstr_tok: ["A","helper","for","use","during","inference",".","Uses","the","argmax","of","the","output","(","treated","as","logits",")","and","passes","the","result","through","an","embedding","layer","to","get","the","next","input","."]
code: def batch_size(self):
      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 GreedyEmbeddingHelper obj's underlying member method code with example usage: obj.batch_size() and returns: self