class_docstr: This class loads in image and sentence features for testing.
docstr: Generator that can be used to iterate over this dataset.
  
  Yields:
      tuple, tuple: first tuple contains image features and empty input Tensor
                    second tuple contains list of reference sentences and
                    placeholder for mask.
code: def __iter__(self):
      """
      Generator that can be used to iterate over this dataset.
  
      Yields:
          tuple, tuple: first tuple contains image features and empty input Tensor
                        second tuple contains list of reference sentences and
                        placeholder for mask.
      """
      for batch_idx in xrange(self.nbatches):
          start = batch_idx * self.be.bsz
          end = (batch_idx + 1) * self.be.bsz
  
          image_batch = self.images[start:end, :].T.astype(
              np.float32, order="C"
          )
          self.dev_image.set(image_batch)
  
          yield (self.dev_image, self.dev_X), (
              self.ref_sents[start:end],
              None,
          )
  
imports: from collections import Counter
  import numpy
  import os
  from neon import logger
  from neon.data.dataiterator import NervanaDataIterator
  from neon.data.datasets import Dataset
  from neon.util.compat import xrange
label_desc: Use image node_id 2 for calling the ImageCaptionTest obj's underlying member method code with example usage: obj.__iter__() without return types