text: import tensorflow as tf
  from tflearn.layers.conv import global_avg_pool
  from tensorflow.contrib.layers import batch_norm, flatten
  from tensorflow.contrib.framework import arg_scope
  
  import numpy as np
  
  
  
  def Global_Average_Pooling(x):
      return global_avg_pool(x, name='Global_avg_pooling')
  
  def Fully_connected(x, units, layer_name='fully_connected') :
      with tf.name_scope(layer_name) :
          return tf.layers.dense(inputs=x, use_bias=True, units=units)
  
  
  def Relu(x):
      return tf.nn.relu(x)
  
  def Sigmoid(x):
      return tf.nn.sigmoid(x)
  
  
  # 引入 Squeeze_excitation_block
  def Squeeze_excitation_layer(input_x, out_dim, ratio, layer_name):
  
      squeeze = Global_Average_Pooling(input_x)
  
      excitation = Fully_connected(squeeze, units=out_dim / ratio, layer_name=layer_name + '_fully_connected1')
      excitation = Relu(excitation)
      excitation = Fully_connected(excitation, units=out_dim, layer_name=layer_name + '_fully_connected2')
      excitation = Sigmoid(excitation)
  
      excitation = tf.reshape(excitation, [-1, 1, 1, out_dim])
  
      scale = input_x * excitation
      print("SE BLOCK SUCCEED")
      return scale