tensorflow如何使用cudnn卷积？

import tensorflow as tf

# 指定GPU设备
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # 设置GPU内存增长
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        # 设置默认使用的GPU设备
        tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        print(e)

# 开启cudnn卷积加速
tf.config.experimental.set_memory_growth(gpus[0], True)
tf.config.experimental.set_per_process_memory_growth(True)
tf.config.experimental.set_memory_growth(tf.config.experimental.list_physical_devices('GPU')[0], True)

import tensorflow as tf

# 输入数据
input_data = tf.random.normal([1, 32, 32, 3])

# 卷积核
filter_data = tf.random.normal([3, 3, 3, 64])

# 使用cudnn卷积
output_data = tf.nn.conv2d(input_data, filter_data, strides=[1, 1, 1, 1], padding='SAME')