class_docstr: This class takes a sequence and returns an iterator providing data in batches suitable for RNN
  prediction.  Meant for use when the entire dataset is small enough to fit in memory.
docstr: Generator that can be used to iterate over this dataset.
  
  Yields:
      tuple : the next minibatch of data.
docstr_tok: ["Generator","that","can","be","used","to","iterate","over","this","dataset",".","Yields",":","tuple",":","the","next","minibatch","of","data","."]
code: def __iter__(self):
      """
      Generator that can be used to iterate over this dataset.
  
      Yields:
          tuple : the next minibatch of data.
      """
      self.batch_index = 0
      while self.batch_index < self.nbatches:
          # get the data for this batch and reshape to fit the device buffer
          # shape
          X_batch = (
              self.X[:, self.batch_index]
              .T.reshape(self.X_dev.shape)
              .copy()
          )
          y_batch = (
              self.y[:, self.batch_index]
              .T.reshape(self.y_dev.shape)
              .copy()
          )
  
          # make the data for this batch as backend tensor
          self.X_dev.set(X_batch)
          self.y_dev.set(y_batch)
  
          self.batch_index += 1
  
          yield self.X_dev, self.y_dev
  
file_path: neon/examples/timeseries/utils.py
imports: from __future__ import division
  import numpy
  import math
  from neon import NervanaObject
label_desc: Use image node_id 3 for calling the DataIteratorSequence obj's underlying member method code with example usage: obj.__iter__() without return types