k-Fold Cross Validation in Keras python

I have fine-tuning a Convolutional Neural Network using keras and tensorflow as:

from itertools import cycle

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from keras.utils import np_utils
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import argparse
import cv2
import os
import time
import sys

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
ap.add_argument("-p", "--plot", type=str, help="path to output loss/accuracy plot")
ap.add_argument("-m", "--model", type=str, help="path to output model")
args = vars(ap.parse_args())

INIT_LR = 1e-3
EPOCHS = 25
BS = 8

print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
data = []
labels = []

counter = 0
for imagePath in imagePaths:
    label = imagePath.split(os.path.sep)[-2].split("_")
    image = cv2.imread(imagePath)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (224, 224))
    counter = counter +1
    data.append(image)
    labels.append(label)
   
data = np.array(data) / 255.0
labels = np.array(labels)

lb = MultiLabelBinarizer()
labels = lb.fit_transform(labels)

# partition the data into training and testing splits using 80% of
# the data for training and the remaining 20% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels,
    test_size=0.20, random_state=42)

trainAug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
    height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
    horizontal_flip=True, fill_mode="nearest")


# load the VGG16 network, ensuring the head FC layer sets are left
baseModel = VGG16(weights="imagenet", include_top=False,
    input_tensor=Input(shape=(224, 224, 3)))

headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(4, 4))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(64, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(3, activation="softmax")(headModel) # Attention: or "sigmoid" activation

model = Model(inputs=baseModel.input, outputs=headModel)

for layer in baseModel.layers:
    layer.trainable = False

print("[INFO] compiling model...")
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt, 
    metrics=["accuracy"])

# train the head of the network
print("[INFO] training head...")
H = model.fit_generator(
    trainAug.flow(trainX, trainY, batch_size=BS),
    steps_per_epoch=len(trainX) // BS,
    validation_data=(testX, testY),
    validation_steps=len(testX) // BS,
    epochs=EPOCHS)

# make predictions on the testing set
print("[INFO] evaluating network...")
predIdxs = model.predict(testX, batch_size=BS)

predIdxs = np.argmax(predIdxs, axis=1)

print(classification_report(testY.argmax(axis=1), predIdxs))

acc_NN = accuracy_score(testY.argmax(axis=1), predIdxs)
print('Overall accuracy of Neural Network model:', acc_NN)

# serialize the model to disk
print("[INFO] saving model...")
model.save(args["model"], save_format="h5")

I want to perform k-Fold Cross Validation and so far I have seen solutions which add (an example):

# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define 10-fold cross validation test harness
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):
    # do

In this line for train, test in kfold.split(X, Y):I do not understand what to use as X and Y in my case. Can anyone help me with this?