How to resolve Value Error when building neural network with tensorflow

Question

I am building a neural network and at this point, I wish to know what my network looks like. But the model summary function is raising an error

code:
print(model.summary())  

Error:
raise ValueError(f"Cannot convert '{shape}' to a shape.")
ValueError: Cannot convert '784' to a shape. 

After writing the code below, I expected a some summary of my neural network structure, instead I get an error?

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28*28).astype("float32") / 255.0
x_test = x_test.reshape(-1, 28*28).astype("float32") / 255.0

# sequential model for one input to one output
model = keras.Sequential(
    [
        keras.Input(shape=(28*28)),
        layers.Dense(512, activation='relu'),
        layers.Dense(256, activation='relu'),
        layers.Dense(10),
    ]
)


print(model.summary()),
import sys
sys.exit()


model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    metrics=["accuracy"],
)

model.fit(x_train, y_train, batch_size=32, epochs=5, verbose=2)
model.evaluate(x_test, y_test, batch_size=32, verbose=2)