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","."]
docstr: next_inputs_fn for GreedyEmbeddingHelper.
docstr_tok: ["next_inputs_fn","for","GreedyEmbeddingHelper","."]
code: def next_inputs(self, time, outputs, state, sample_ids, name=None):
      """next_inputs_fn for GreedyEmbeddingHelper."""
      del time, outputs  # unused by next_inputs_fn
      finished = math_ops.equal(sample_ids, self._end_token)
      all_finished = math_ops.reduce_all(finished)
      next_inputs = control_flow_ops.cond(
          all_finished,
          # If we're finished, the next_inputs value doesn't matter
          lambda: self._start_inputs,
          lambda: self._embedding_fn(sample_ids),
      )
      return (finished, next_inputs, state)
  
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 5 for calling the GreedyEmbeddingHelper obj's underlying member method code with example usage: obj.next_inputs(time, outputs, state, sample_ids, name) and returns: finished,  next_inputs,  state