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 sample(self, time, outputs, state, name=None):
      with ops.name_scope(
          name, "%sSample" % type(self).__name__, (time, outputs, state)
      ):
          return self._sample_fn(
              time=time, outputs=outputs, state=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 4 for calling the CustomHelper obj's underlying member method code with example usage: obj.sample(time, outputs, state, name) and returns: self