input: Write a function to construct a neural network model in Python using Keras to classify the MNIST dataset correctly. The function should return the compiled model.
  assert construct_mnist_model().__class__.__name__=='Model'
  assert construct_mnist_model().compiled==True
  assert construct_mnist_model().get_config()['layers'][0]['config']['batch_input_shape']==(None, 784)
  assert construct_mnist_model().get_config()['layers'][-1]['config']['units']==10
output: def construct_mnist_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential()
      model.add(Dense(128, activation='relu', input_shape=(784,)))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model