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 next_inputs(self, time, outputs, state, sample_ids, name=None):
      with ops.name_scope(
          name,
          "%sNextInputs" % type(self).__name__,
          (time, outputs, state),
      ):
          return self._next_inputs_fn(
              time=time,
              outputs=outputs,
              state=state,
              sample_ids=sample_ids,
          )
  
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 CustomHelper obj's underlying member method code with example usage: obj.next_inputs(time, outputs, state, sample_ids, name) and returns: self