text: import tensorflow as tf
  
  
  DEPTH_WIDTH = 640
  DEPTH_HEIGHT = 480
  
  
  def parse_record(serialized_example):
      """Parse serialized example of TfRecord and extract dictionary of all the information
      """
      features = tf.io.parse_single_example(
          serialized_example,
          features={
              'height': tf.io.FixedLenFeature([], tf.int64),
              'width': tf.io.FixedLenFeature([], tf.int64),
              'image_name': tf.io.FixedLenFeature([], tf.string),
              'depth': tf.io.FixedLenFeature([], tf.string),
              'rgb': tf.io.FixedLenFeature([], tf.string),
          })
  
      height = tf.cast(features['height'], tf.int32)
      width = tf.cast(features['width'], tf.int32)
      image_name = tf.cast(features['image_name'], tf.string)
  
      image = tf.io.decode_raw(features['rgb'], tf.uint8)
      image_shape = tf.stack([height, width, 3])
      image = tf.reshape(image, image_shape)
  
      img_depth = tf.io.decode_raw(features['depth'], tf.float32)
      depth_shape = tf.stack([height, width])
      img_depth = tf.reshape(img_depth, depth_shape)
      image_info = {'image_name': image_name, 'depth': img_depth}
      return [image, image_info]
  