I want a code that will for example, after the $20$th or $25$th layer of the resnetV$250$ layer to add a dropout and regularizer layer there, I want to know after which layers of the resnetv$250$ can i add dropout and regularizers. in summary, I want to take the base model of the resnetv$250$ and within those $50$ layers i want to insert dropout and regularizer layer, not after the $50$ layers make a code within the model structure provided below that produces no errors:

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.ResNet$50$V$2($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$)$