SE注意力机制问题，在Squeeze操作.GlobalAveragePooling2D之后维度发生改变，导致输入下面的卷积出现问题，怎么解决呢

Question

x = keras.layers.Conv2D(64,(3,3),padding='same')(input_tensor)

in_channels = K.int_shape(x)[-1]
###注意力机制
in_tensor = x

#Squeeze操作
x = layers.GlobalAveragePooling2D()(x)

ratio1 = 0.5

#Exicitation
out = Conv2D(filters=in_channels//ratio1, kernel_size=(1,1))(x)
out = Activation("relu")(out)
out = Conv2D(filters=in_channels, kernel_size=(1,1))(out)
out = Activation('sigmoid')(out)
out = layers.Reshape((1, in_channels))(out)

scale = tf.multiply(in_tensor, out)