在Tensorflow 2.0中，如何计算中间层过滤器激活相对于输入图像的梯度？

import tensorflow as tf

model = tf.keras.applications.VGG16(weights='imagenet', include_top=True)

layer_name = 'block3_conv1'
intermediate_layer_model = tf.keras.Model(inputs=model.input, outputs=model.get_layer(layer_name).output)

img_path = 'path_to_image.jpg'
img = tf.keras.preprocessing.image.load_img(img_path, target_size=(224, 224))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, axis=0)

with tf.GradientTape() as tape:
    tape.watch(img_array)
    intermediate_output = intermediate_layer_model(img_array)

grads = tape.gradient(intermediate_output, img_array)

grads /= tf.math.reduce_std(grads) + 1e-8