code: def _map_fn_train(img, target):
      # 1. Randomly crop a [height, width] section of the image.
      img = tf.image.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)
      target = tf.reshape(target, ())
      return img, target
  
file_path: TensorLayer/examples/quantized_net/tutorial_quanconv_cifar10.py
imports: import multiprocessing
  import time
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import Dense, Flatten, Input, MaxPool2d, QuanConv2dWithBN, QuanDense
  from tensorlayer.models import Model
label_desc: Use image node_id 4 for calling a global function with example usage: _map_fn_train(img, target) and returns: img,  target