class_docstr: Map boxes from xyxy to cxcywh and normalize to [0,1].
code: def __call__(self, features):
      h, w = get_hw(features, dtype=tf.float32)
      if "boxes" in features["label"]:
          boxes = features["label"]["boxes"]
  
          # Maps boxes from xyxy to cxcywh.
          x0, y0, x1, y1 = tf.split(boxes, 4, axis=-1)
          boxes = tf.concat(
              [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)],
              axis=-1,
          )
  
          # Normalizes boxes to [0, 1].
          boxes = boxes / tf.reshape(
              tf.stack([w, h, w, h]), shape=[1, 4]
          )
          features["label"]["boxes"] = boxes
  
      return features
  
imports: from typing import Any, Dict
  import tensorflow
label_desc: Use image node_id 1 for calling the NormalizeBoxes obj's underlying member method code with example usage: obj.__call__(features) and returns: features