给出GoogLeNet的 Inception 结构python代码

以下是GoogLeNet的Inception结构的Python代码示例：

import tensorflow as tf

def inception_module(x, params, is_training):
    # 分离出3x3卷积和1x1卷积的输出通道数
    branch1x1, branch3x3_reduce, branch3x3, branch5x5_reduce, branch5x5, branch_pool = params
    
    # 1x1卷积层
    conv1x1 = tf.layers.conv2d(x, filters=branch1x1, kernel_size=[1, 1], strides=[1, 1], padding='same', activation=tf.nn.relu)
    
    # 3x3卷积层
    conv3x3_reduce = tf.layers.conv2d(x, filters=branch3x3_reduce, kernel_size=[1, 1], strides=[1, 1], padding='same', activation=tf.nn.relu)
    conv3x3 = tf.layers.conv2d(conv3x3_reduce, filters=branch3x3, kernel_size=[3, 3], strides=[1, 1], padding='same', activation=tf.nn.relu)
    
    # 5x5卷积层
    conv5x5_reduce = tf.layers.conv2d(x, filters=branch5x5_reduce, kernel_size=[1, 1], strides=[1, 1], padding='same', activation=tf.nn.relu)
    conv5x5 = tf.layers.conv2d(conv5x5_reduce, filters=branch5x5, kernel_size=[5, 5], strides=[1, 1], padding='same', activation=tf.nn.relu)
    
    # 3x3最大池化层
    pooling = tf.layers.max_pooling2d(x, pool_size=[3, 3], strides=[1, 1], padding='same')
    branch_pool = tf.layers.conv2d(pooling, filters=branch_pool, kernel_size=[1, 1], strides=[1, 1], padding='same', activation=tf.nn.relu)
    
    # 将各个分支的输出按通道合并
    output = tf.concat([conv1x1, conv3x3, conv5x5, branch_pool], axis=3)
    
    return output

其中，`x`为输入的特征图，`params`为每个分支的输出通道数，`is_training`为是否处于训练状态。`inception_module`函数将输入的特征图分别进行1x1卷积、3x3卷积、5x5卷积和3x3最大池化操作，并将各个分支的输出按通道合并。