下面是一个简单的代码示例，展示了如何构建ResNet的Identity块和卷积块。

首先，我们定义一个基本的卷积块，它由两个卷积层和一个批量归一化层组成。

import tensorflow as tf

def conv_block(input_tensor, filters, strides=1):
    x = tf.keras.layers.Conv2D(filters, kernel_size=(3, 3), strides=strides, padding='same')(input_tensor)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Conv2D(filters, kernel_size=(3, 3), padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    
    shortcut = input_tensor
    if strides != 1 or filters != input_tensor.shape[-1]:
        shortcut = tf.keras.layers.Conv2D(filters, kernel_size=(1, 1), strides=strides, padding='same')(input_tensor)
        shortcut = tf.keras.layers.BatchNormalization()(shortcut)
    
    x = tf.keras.layers.add([x, shortcut])
    x = tf.keras.layers.Activation('relu')(x)
    
    return x

接下来，我们定义一个Identity块，它由三个卷积块组成。

def identity_block(input_tensor, filters):
    x = conv_block(input_tensor, filters)
    x = conv_block(x, filters)
    x = conv_block(x, filters)
    return x

最后，我们定义一个ResNet模型，它由一个初始的卷积层和几个Identity块组成。

def ResNet():
    input_tensor = tf.keras.Input(shape=(224, 224, 3))
    x = tf.keras.layers.Conv2D(64, kernel_size=(7, 7), strides=(2, 2), padding='same')(input_tensor)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
    
    x = identity_block(x, 64)
    x = identity_block(x, 64)
    x = identity_block(x, 64)
    
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dense(1000, activation='softmax')(x)
    
    model = tf.keras.Model(inputs=input_tensor, outputs=x)
    
    return model

这样，我们就定义了一个ResNet模型，其中包含了Identity块和卷积块。你可以根据需要调整块的层数和卷积层的参数来构建更复杂的ResNet模型。