class_docstr: A training helper that adds scheduled sampling.
  
  Returns -1s for sample_ids where no sampling took place; valid sample id
  values elsewhere.
class_docstr_tok: ["A","training","helper","that","adds","scheduled","sampling",".","Returns","-1s","for","sample_ids","where","no","sampling","took","place",";","valid","sample","id","values","elsewhere","."]
code: def initialize(self, name=None):
      return super(ScheduledEmbeddingTrainingHelper, self).initialize(
          name=name
      )
  
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 2 for calling the ScheduledEmbeddingTrainingHelper obj's underlying member method code with example usage: obj.initialize(name) and returns: super