Write a function within this code that will add dropout and regularizer layers in between the imported model:

import tensorflow as tf

from tensorflow.keras import layers

from tensorflow.keras.layers.experimental import preprocessing

# Create base model

input$\_$shape $=(224,224,3)$

base$\_$model $=$ tf$.$keras.applications.VGG$16($include$\_$top$=$False$)$

base$\_$model.trainable $=$ False #freeze all base model layers

# CReate functional model

inputs $=$ layers.Input$($shape$=$input$\_$shape, name $=$"input$\_$layer"$)$

x $=$ data$\_$augmentation$($inputs$)$ # augment images $($only happens during training phase$)$

x $=$ base$\_$model$($inputs$,$ training$=$False$)$ # set base model to inference mode only

x $=$ layers.GlobalAveragePooling$2$D$($name$=$"pooling$\_$layer"$)($x$)$

x $=$ tf$.$keras.layers.Dropout$(0\mathrm{.}5)($x$)$

outputs $=$ layers.Dense$($len$($class$\_$names$),$ activation$=$"softmax", name$=$"output$\_$layer"$)($x$)$

model $=$ tf$.$keras.Model$($inputs$,$ outputs$)$