Warning : Custom mask layers require a config and must override when saving the model in keras

I created a model from sequential. when I saved it I got this warning message

home/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.
  warnings.warn('Custom mask layers require a config and must override 

I tested one image and the prediction was good the I saved my model when I loaded it again it started giving me wrong values and the prediction was all wrong.what is the correct way to say the model and load it

import numpy as np 
import matplotlib.pyplot as plt
import glob
import cv2
import os
from tensorflow import keras

from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Input, Dropout, Flatten, Dense

from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.models import Model
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.models import Sequential

input_shape = (3,1134,1134,3)
base_model = tf.keras.applications.ResNet50(
include_top=False,
weights="imagenet",
input_shape=(1134,1134,3),
pooling=max,  
)
for layer in base_model.layers[:-4]:
    layer.trainable = False
model = Sequential()
model.add(Conv2D(3,(3,3),activation='relu',padding='same'))
model.add(base_model)
model.add(Conv2D(3,(3,3),activation='relu',padding='same'))
# model.add(Convolution2D(3,(4,4),activation='relu',padding='same'))
model.add(UpSampling2D(size =(16,16)))
model.add(UpSampling2D())
model.add(BatchNormalization())
model.add(Conv2D(3,(3,3),activation='relu',padding='same'))
model.build(input_shape)
model.summary()

this is how I save it

model.save("/media/TOSHIBA EXT/trained_model/UAV_01.h5")

enter code here

model=keras.models.load_model(
    "/media/TOSHIBA EXT/trained_model/UAV_01.h5")

from Warning : Custom mask layers require a config and must override when saving the model in keras