有没有办法在tensorflow的几个神经元中只激活一个特定的神经元？

import tensorflow as tf

# 假设有5个神经元
num_neurons = 5

# 定义输入
input_data = tf.placeholder(tf.float32, shape=[None, num_neurons])

# 将输入转换为One-Hot编码
one_hot_data = tf.one_hot(indices=[2], depth=num_neurons)

# 定义神经网络模型
hidden_layer = tf.layers.dense(inputs=input_data, units=num_neurons, activation=tf.nn.relu)
output_layer = tf.layers.dense(inputs=hidden_layer, units=num_neurons, activation=None)

# 计算损失函数
loss = tf.losses.mean_squared_error(labels=one_hot_data, predictions=output_layer)

# 定义优化器和训练操作
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss)

# 在会话中执行训练操作
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    # 训练模型
    for i in range(1000):
        _, loss_val = sess.run([train_op, loss], feed_dict={input_data: [[1, 1, 1, 1, 1]]})
        if i % 100 == 0:
            print("Step {}: Loss = {}".format(i, loss_val))
    
    # 测试模型
    output = sess.run(output_layer, feed_dict={input_data: [[1, 1, 1, 1, 1]]})
    print("Output:", output)