code: def get_augmented_data(self, images, annotations, random_seed):
      tf = _lazy_import_tensorflow()
      with tf.Session(graph=self.graph) as session:
          feed_dict = dict()
  
          # Populate feed_dict with images and annotations
          graph_op = self.resize_op_batch[0 : len(images)]
          for i in range(len(images)):
              feed_dict[
                  self.img_tf[i]
              ] = _utils.convert_shared_float_array_to_numpy(images[i])
              feed_dict[
                  self.ann_tf[i]
              ] = _utils.convert_shared_float_array_to_numpy(
                  annotations[i]
              )
  
          # Populate feed_dict with random seed and random alpha values, used
          # to sample image perturbations. We don't use TensorFlow's built-in
          # support for random number generation, since we want to effectively
          # reset the seed for each session (batch).
          random = np.random.RandomState(seed=random_seed)
          feed_dict[self.alpha_tf] = random.rand(*self.alpha_tf.shape)
          feed_dict[self.random_seed_tf] = random.randint(
              0, 2**32, size=self.batch_size
          )
          aug_output = session.run(graph_op, feed_dict=feed_dict)
          processed_images = []
          processed_annotations = []
          for o in aug_output:
              processed_images.append(o[0])
              processed_annotations.append(
                  np.ascontiguousarray(o[1], dtype=np.float32)
              )
          processed_images = np.array(
              processed_images, dtype=np.float32
          )
          processed_images = np.ascontiguousarray(
              processed_images, dtype=np.float32
          )
          return (processed_images, processed_annotations)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from PIL import Image
  import turicreate.toolkits._tf_utils
  import turicreate
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 2 for calling the DataAugmenter obj's underlying member method code with example usage: obj.get_augmented_data(images, annotations, random_seed) and returns: processed_images,  processed_annotations