code: def example_simple_training_setting():
      print("Example: Simple Training Setting")
      import numpy as np
      import imgaug.augmenters as iaa
  
      def load_batch(batch_idx):
          # dummy function, implement this
          # Return a numpy array of shape (N, height, width, #channels)
          # or a list of (height, width, #channels) arrays (may have different image
          # sizes).
          # Images should be in RGB for colorspace augmentations.
          # (cv2.imread() returns BGR!)
          # Images should usually be in uint8 with values from 0-255.
          return np.zeros((128, 32, 32, 3), dtype=np.uint8) + (
              batch_idx % 255
          )
  
      def train_on_images(images):
          # dummy function, implement this
          pass
  
      # Pipeline:
      # (1) Crop images from each side by 1-16px, do not resize the results
      #     images back to the input size. Keep them at the cropped size.
      # (2) Horizontally flip 50% of the images.
      # (3) Blur images using a gaussian kernel with sigma between 0.0 and 3.0.
      seq = iaa.Sequential(
          [
              iaa.Crop(px=(1, 16), keep_size=False),
              iaa.Fliplr(0.5),
              iaa.GaussianBlur(sigma=(0, 3.0)),
          ]
      )
  
      for batch_idx in range(100):
          images = load_batch(batch_idx)
          images_aug = seq(images=images)  # done by the library
          train_on_images(images_aug)
  
          # -----
          # Make sure that the example really does something
          if batch_idx == 0:
              assert not np.array_equal(images, images_aug)
  
file_path: imgaug/checks/check_readme_examples.py
imports: from __future__ import print_function, division
  import functools
label_desc: Use image node_id 3 for calling a global function with example usage: example_simple_training_setting() and returns: unknown