如何在tensorflow中实现区域兴趣池层？

import tensorflow as tf
from tensorflow.keras.layers import Layer

class ROIPoolingLayer(Layer):
    def __init__(self, pooled_height, pooled_width, **kwargs):
        super(ROIPoolingLayer, self).__init__(**kwargs)
        self.pooled_height = pooled_height
        self.pooled_width = pooled_width

    def call(self, inputs):
        feature_map, rois = inputs

        # 计算每个ROI的高度和宽度
        roi_height = rois[:, 2] - rois[:, 0]
        roi_width = rois[:, 3] - rois[:, 1]

        # 计算每个ROI的高度和宽度的比例
        height_ratio = roi_height / self.pooled_height
        width_ratio = roi_width / self.pooled_width

        # 对每个ROI进行区域兴趣池化
        pooled_rois = []
        for i in range(tf.shape(rois)[0]):
            pooled_roi = tf.image.crop_and_resize(
                feature_map[i],  # 输入特征图
                [[rois[i, 0], rois[i, 1], rois[i, 2], rois[i, 3]]],  # ROI坐标
                [0],  # ROI的标签
                [self.pooled_height, self.pooled_width]  # 池化后的尺寸
            )
            pooled_rois.append(pooled_roi)

        # 将池化后的ROI堆叠起来作为输出
        pooled_rois = tf.stack(pooled_rois, axis=0)

        return pooled_rois

# 创建区域兴趣池层实例
roi_pooling_layer = ROIPoolingLayer(pooled_height=7, pooled_width=7)

# 使用区域兴趣池层
feature_map = tf.keras.Input(shape=(None, None, 256))
rois = tf.keras.Input(shape=(None, 4))
pooled_rois = roi_pooling_layer([feature_map, rois])

# 定义模型
model = tf.keras.Model(inputs=[feature_map, rois], outputs=pooled_rois)