class_docstr: Base class for image data iterators.
  
  Every `Iterator` must implement the `_get_batches_of_transformed_samples`
  method.
  
  # Arguments
      n: Integer, total number of samples in the dataset to loop over.
      batch_size: Integer, size of a batch.
      shuffle: Boolean, whether to shuffle the data between epochs.
      seed: Random seeding for data shuffling.
docstr: For python 2.x.
  
  # Returns
      The next batch.
docstr_tok: ["For","python","2.x",".","#","Returns","The","next","batch","."]
code: def next(self):
      """For python 2.x.
  
      # Returns
          The next batch.
      """
      with self.lock:
          index_array = next(self.index_generator)
      # The transformation of images is not under thread lock
      # so it can be done in parallel
      return self._get_batches_of_transformed_samples(index_array)
  
imports: import os
  import threading
  import numpy
  from keras_preprocessing import get_keras_submodule
  from .utils import array_to_img, img_to_array, load_img
label_desc: Use image node_id 10 for calling the Iterator obj's underlying member method code with example usage: obj.next() and returns: self