code: def data_aug_train(img, ann):
      # 1. Randomly crop a [height, width] section of the image.
      img = tf.random_crop(img, [24, 24, 3])
      # 2. Randomly flip the image horizontally.
      img = tf.image.random_flip_left_right(img)
      # 3. Randomly change brightness.
      img = tf.image.random_brightness(img, max_delta=63)
      # 4. Randomly change contrast.
      img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
      # 5. Subtract off the mean and divide by the variance of the pixels.
      img = tf.image.per_image_standardization(img)
      return img, ann
  
file_path: TensorLayer/examples/distributed_training/tutorial_cifar10_distributed_trainer.py
imports: import multiprocessing
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import BatchNormLayer, Conv2d, DenseLayer, FlattenLayer, InputLayer, MaxPool2d
label_desc: Use image node_id 2 for calling a global function with example usage: data_aug_train(img, ann) and returns: img,  ann