how to visualize the DNN or CNN model with the values of the input and output layers?

Question

I have applied visualization to the DNN model, but the image just contains a dense layer Without the value of the input and output layers! The code below explains the visualization process without any error, I tried to show the values of the input and output layers in image.

import pandas as pd
.
. 
tf.keras.utils.plot_model
.
.
def create_model():

model = Sequential()
model.add(Input(n_features))
model.add(BatchNormalization())
model.add(Dense(51, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(68, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(85, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(85, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(68, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(51, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_outputs, activation='sigmoid'))
model.compile(loss='binary_crossentropy', 
              optimizer='Adam', 
              metrics=['accuracy'])
tf.keras.utils.plot_model(model, to_file='model_combined.png')
#model.summary()
return model    

#I have tried to use 
#from keras.utils.vis_utils import plot_model
# but i found this error : TypeError: 'InputLayer' object is not iterable
# so i use the above library to implement visualization without any error.

Please note that I have downloaded all of these libraries: Graphviz, pydot, pydotplus, python-graphviz