给出一个使用ResNet网络的函数拟合网络，要求具有三个特征输入和三个输出

以下是一个使用ResNet网络的函数拟合网络，具有三个特征输入和三个输出：

import tensorflow as tf
import numpy as np

# 定义输入和输出的shape
input_shape = (None, 3)
output_shape = (None, 3)

# 定义ResNet的卷积层
def resnet_conv2d(inputs, filters, kernel_size, strides, activation=tf.nn.relu, padding='same'):
    x = tf.keras.layers.Conv2D(filters, kernel_size, strides=strides, padding=padding)(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = activation(x)
    return x

# 定义ResNet的残差块
def resnet_block(inputs, filters, kernel_size, strides, activation=tf.nn.relu):
    shortcut = inputs
    x = resnet_conv2d(inputs, filters, kernel_size, strides, activation=activation)
    x = resnet_conv2d(x, filters, kernel_size, strides=1, activation=activation)
    if strides != 1 or inputs.shape[-1] != filters:
        shortcut = tf.keras.layers.Conv2D(filters, 1, strides=strides)(shortcut)
        shortcut = tf.keras.layers.BatchNormalization()(shortcut)
    x = tf.keras.layers.Add()([x, shortcut])
    x = activation(x)
    return x

# 定义ResNet网络
def resnet(input_shape, output_shape):
    inputs = tf.keras.layers.Input(shape=input_shape)
  
    # 第一层
    x = resnet_conv2d(inputs, 32, 3, strides=1)
    x = tf.keras.layers.MaxPooling2D(2)(x)
    
    # 第二层
    filters = 64
    kernel_size = 3
    strides = 1
    x = resnet_block(x, filters, kernel_size, strides)
    x = resnet_block(x, filters, kernel_size, strides)
    
    # 输出层
    x = tf.keras.layers.Flatten()(x)
    outputs = tf.keras.layers.Dense(output_shape[-1], activation='linear')(x)
    
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# 定义数据
np.random.seed(42)
X = np.random.randn(100, 3)
Y = np.column_stack((X[:, 0] + X[:, 1], X[:, 0] - X[:, 1], X[:, 2]))

# 构建模型
model = resnet(input_shape, output_shape)

# 训练模型
optimizer = tf.optimizers.Adam()
model.compile(optimizer=optimizer, loss='mse')
model.fit(X, Y, epochs=100)

# 测试模型
x_test = np.random.randn(10, 3)
y_pred = model.predict(x_test)
print(x_test)
print(y_pred)

该模型定义了一个具有三个特征输入和三个输出的ResNet网络，其中输入数据是一个大小为`(None, 3)`的二维数组，表示三个特征的取值。输出数据是一个大小为`(None, 3)`的二维数组，表示三个预测值。模型使用均方误差作为损失函数，并使用Adam优化器进行训练。在测试阶段，我们可以使用模型对新的输入数据进行预测并输出预测结果。