class_docstr: A helper for use during training.  Only reads inputs.
  
  Returned sample_ids are the argmax of the RNN output logits.
class_docstr_tok: ["A","helper","for","use","during","training",".","Only","reads","inputs",".","Returned","sample_ids","are","the","argmax","of","the","RNN","output","logits","."]
docstr: next_inputs_fn for TrainingHelper.
docstr_tok: ["next_inputs_fn","for","TrainingHelper","."]
code: def next_inputs(
      self, time, outputs, state, name=None, **unused_kwargs
  ):
      """next_inputs_fn for TrainingHelper."""
      with ops.name_scope(
          name, "TrainingHelperNextInputs", [time, outputs, state]
      ):
          next_time = time + 1
          finished = next_time >= self._sequence_length
          all_finished = math_ops.reduce_all(finished)
  
          def read_from_ta(inp):
              return inp.read(next_time)
  
          next_inputs = control_flow_ops.cond(
              all_finished,
              lambda: self._zero_inputs,
              lambda: nest.map_structure(read_from_ta, self._input_tas),
          )
          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 TrainingHelper obj's underlying member method code with example usage: obj.next_inputs(time, outputs, state, name) and returns: finished,  next_inputs,  state, inp