code: def example_multicore_augmentation():
      print("Example: Multicore Augmentation")
      import skimage.data
      import imgaug as ia
      import imgaug.augmenters as iaa
      from imgaug.augmentables.batches import UnnormalizedBatch
  
      # Number of batches and batch size for this example
      nb_batches = 10
      batch_size = 32
  
      # Example augmentation sequence to run in the background
      augseq = iaa.Sequential(
          [iaa.Fliplr(0.5), iaa.CoarseDropout(p=0.1, size_percent=0.1)]
      )
  
      # For simplicity, we use the same image here many times
      astronaut = skimage.data.astronaut()
      astronaut = ia.imresize_single_image(astronaut, (64, 64))
  
      # Make batches out of the example image (here: 10 batches, each 32 times
      # the example image)
      batches = []
      for _ in range(nb_batches):
          batches.append(
              UnnormalizedBatch(images=[astronaut] * batch_size)
          )
  
      # Show the augmented images.
      # Note that augment_batches() returns a generator.
      for images_aug in augseq.augment_batches(
          batches, background=True
      ):
          ia.imshow(ia.draw_grid(images_aug.images_aug, cols=8))
  
file_path: imgaug/checks/check_readme_examples.py
imports: from __future__ import print_function, division
  import functools
label_desc: Use image node_id 14 for calling a global function with example usage: example_multicore_augmentation() without return types