class_docstr: This class defines an iterator for loading and iterating the sentences in
  a structure to encode sentences into the skip-thought vectors
class_docstr_tok: ["This","class","defines","an","iterator","for","loading","and","iterating","the","sentences","in","a","structure","to","encode","sentences","into","the","skip-thought","vectors"]
docstr: Generator that can be used to iterate over this dataset
docstr_tok: ["Generator","that","can","be","used","to","iterate","over","this","dataset"]
code: def __iter__(self):
      """
      Generator that can be used to iterate over this dataset
      """
      self.batch_index = 0
      while self.batch_index < self.nbatches:
          self.X_np.fill(0)
  
          idx = range(
              self.batch_index * self.be.bsz,
              (self.batch_index + 1) * self.be.bsz,
          )
          for i, ix in enumerate(idx):
              s_len = self.sent_len[ix]
              self.X_np[-s_len:, i] = (
                  self.X[ix][-s_len:] + self.index_from
              )
  
          self.dev_X.set(self.X_np)
  
          self.batch_index += 1
  
          yield (self.dev_X, None)
  
file_path: neon/examples/skip-thought/data_iterator.py
imports: from __future__ import division
  import numpy
  import h5py
  import copy
  from neon.data.dataiterator import NervanaDataIterator
label_desc: Use image node_id 3 for calling the SentenceEncode obj's underlying member method code with example usage: obj.__iter__() without return types