如何在tensorflow中获取自定义损失中张量的形状

import tensorflow as tf

def custom_loss(y_true, y_pred):
    # 自定义损失函数的计算逻辑
    loss = tf.square(y_true - y_pred)
    return loss

inputs = tf.placeholder(tf.float32, shape=(None, 10))  # 假设输入张量的形状为(None, 10)
targets = tf.placeholder(tf.float32, shape=(None, 10))  # 假设目标张量的形状为(None, 10)

# 计算自定义损失
loss = custom_loss(targets, inputs)

# 获取输入张量和目标张量的形状
inputs_shape = tf.shape(inputs)
targets_shape = tf.shape(targets)

# 打印输入张量和目标张量的形状
with tf.Session() as sess:
    inputs_shape_value, targets_shape_value = sess.run([inputs_shape, targets_shape], feed_dict={inputs: input_data, targets: target_data})
    print("输入张量的形状：", inputs_shape_value)
    print("目标张量的形状：", targets_shape_value)