code: def _map_fn(filename, annotation):
      ## read image
      image = tf.io.read_file(filename)
      image = tf.image.decode_jpeg(image, channels=3)
      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
      ## data augmentation for image only  0.02s
      image = tf.image.random_brightness(image, max_delta=63)
      image = tf.image.random_contrast(image, lower=0.2, upper=1.8)
      # subtract off the mean and divide by the variance of the pixels. (optional)
      # img = tf.image.per_image_standardization(img)
      ## data augmentation for image and bounding box
      image, annotation = tf.numpy_function(
          _data_aug_fn, [image, annotation], [tf.float32, tf.string]
      )
      return image, annotation
  
file_path: TensorLayer/examples/data_process/tutorial_tf_dataset_voc.py
imports: import json
  import multiprocessing
  import random
  import time
  import numpy
  import tensorflow
  import tensorlayer
label_desc: Use image node_id 3 for calling a global function with example usage: _map_fn(filename, annotation) and returns: image,  annotation