input: Write a function to construct a neural network model in Python that classifies the MNIST data set correctly. The function should return the constructed model.
  assert model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test)).history['val_accuracy'][-1] > 0.90
  assert model.predict(X_test[:10]).argmax(axis=1) == y_test[:10]
output: def construct_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Flatten
  
      model = Sequential([
          Flatten(input_shape=(28, 28)),
          Dense(128, activation='relu'),
          Dense(64, activation='relu'),
          Dense(10, activation='softmax')
      ])
      
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model