如何在Keras中创建具有相同权重的节点的神经网络，最好是函数式API

from keras.layers import Input, Dense
from keras.models import Model

input_layer = Input(shape=(input_dim,))

shared_layer = Dense(units=hidden_units, activation='relu')

node1 = shared_layer(input_layer)
node2 = shared_layer(input_layer)

output_layer = Dense(units=output_dim, activation='softmax')

output1 = output_layer(node1)
output2 = output_layer(node2)

model = Model(inputs=input_layer, outputs=[output1, output2])

from keras.layers import Input, Dense
from keras.models import Model

input_dim = 10
hidden_units = 64
output_dim = 2

# 定义输入层
input_layer = Input(shape=(input_dim,))

# 定义共享的权重层
shared_layer = Dense(units=hidden_units, activation='relu')

# 使用共享的权重层创建节点
node1 = shared_layer(input_layer)
node2 = shared_layer(input_layer)

# 定义输出层
output_layer = Dense(units=output_dim, activation='softmax')

# 使用输出层创建输出节点
output1 = output_layer(node1)
output2 = output_layer(node2)

# 创建模型
model = Model(inputs=input_layer, outputs=[output1, output2])