Any efficient workaround to extend CNN with Taylor non-linear expansion in keras?

Deep Learning: General Discussion
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I intend to implement expandable CNN by using Tylor non-linear expansion in keras. I used cifar-10 dataset for input data. I looked into the basic concept of Tylor series and tried using Tylor non-linear expansion for input tensor, but the code that I sketched not perfectly fit for a computational graph I am trying to use. I am not sure how to represent the weight after input tensor expanded by tylor non-linear expansion. Can anyone give me a possible idea of how to expand CNN with Tylor non-linear expansion? How to efficiently do Tylor non-linear expansion on CNN? any thought?

computational graph of expandable CNN

Here is the computational graphic model for expandable CNN that I am trying to implement:

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where x is input, alpha is coefficient, p is power, w is weight

my attempt:

import tensorflow as tf
import numpy as np
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
from keras.datasets import cifar10
from keras.utils import to_categorical

(train_imgs, train_label), (test_imgs, test_label)= cifar10.load_data()
output_class = np.unique(train_label)
n_class = len(output_class)

nrows_tr, ncols_tr, ndims_tr = train_imgs.shape[1:]
nrows_ts, ncols_ts, ndims_ts = test_imgs.shape[1:]
train_data = train_imgs.reshape(train_imgs.shape[0], nrows_tr, ncols_tr, ndims_tr)

test_data = test_imgs.reshape(test_imgs.shape[0], nrows_ts, ncols_ts, ndims_ts)
input_shape = (nrows_tr, ncols_tr, ndims_tr)
train_data = train_data.astype('float32')
trast_data = test_data.astype('float32')
train_data //= 255
test_data //= 255
train_label_one_hot = to_categorical(train_label)
test_label_one_hot = to_categorical(test_label)

def pown(x,n):
    return(x**n)

def expandable_cnn(input_shape, output_shape, approx_order):
    inputs=Input(shape=(input_shape))
    x= Dense(input_shape)(inputs)
    y= Dense(output_shape)(x)
    model = Sequential()
    model.add(Conv2D(filters=32, kernel_size=(3,3), padding='same', activation="relu", input_shape=input_shape))
    model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.5))
    for i in range(2, approx_order+1):
        y=add([y, Dense(output_shape)(Activation(lambda x: pown(x, n=i))(x))])
    model.add(Dense(n_class, activation='softmax')(y))
    return model

but when I ran the above model, I had compile error. I assume that the way for Tylor non-linear expansion for CNN model may not be correct. Also, I am not sure how to represent weight. How to make this work? any possible idea of how to correct my attempt?

objective:

I am expecting to extend CNN with Tylor non-linear expansion based on the above computational graph, how to make the above implementation correct and efficient? can anyone point me out how to correctly implement expandable CNN with Tylor series? any possible idea or approach?