在Tensorflow对象检测API中，如何计算多个边界框预测的IoU？

import tensorflow as tf

def compute_iou(box1, box2):
    # 计算相交区域的左上角和右下角坐标
    intersection_x1 = tf.maximum(box1[0], box2[0])
    intersection_y1 = tf.maximum(box1[1], box2[1])
    intersection_x2 = tf.minimum(box1[2], box2[2])
    intersection_y2 = tf.minimum(box1[3], box2[3])

    # 计算相交区域的宽和高
    intersection_width = tf.maximum(0.0, intersection_x2 - intersection_x1)
    intersection_height = tf.maximum(0.0, intersection_y2 - intersection_y1)

    # 计算相交区域的面积
    intersection_area = intersection_width * intersection_height

    # 计算两个边界框的面积
    box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
    box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])

    # 计算并集区域的面积
    union_area = box1_area + box2_area - intersection_area

    # 计算IoU
    iou = intersection_area / union_area

    return iou