class_docstr: A training helper that adds scheduled sampling directly to outputs.
  
  Returns False for sample_ids where no sampling took place; True elsewhere.
class_docstr_tok: ["A","training","helper","that","adds","scheduled","sampling","directly","to","outputs",".","Returns","False","for","sample_ids","where","no","sampling","took","place",";","True","elsewhere","."]
code: def sample(self, time, outputs, state, name=None):
      with ops.name_scope(
          name,
          "ScheduledOutputTrainingHelperSample",
          [time, outputs, state],
      ):
          sampler = bernoulli.Bernoulli(
              probs=self._sampling_probability
          )
          return math_ops.cast(
              sampler.sample(
                  sample_shape=self.batch_size, seed=self._seed
              ),
              dtypes.bool,
          )
  
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 3 for calling the ScheduledOutputTrainingHelper obj's underlying member method code with example usage: obj.sample(time, outputs, state, name) and returns: math_ops