input: Write a function to construct a neural network model in Python using the Keras library that can classify the MNIST data set correctly. The function should return the compiled model.
  assert construct_mnist_model().__class__.__name__=="Model"
  assert construct_mnist_model().loss=="sparse_categorical_crossentropy"
  assert construct_mnist_model().optimizer.get_config()['name']=="adam"
  assert len(construct_mnist_model().layers)==3
  assert construct_mnist_model().layers[0].input_shape==[(None, 784)]
  assert construct_mnist_model().layers[1].units==128
  assert construct_mnist_model().layers[2].units==10
  assert construct_mnist_model().layers[2].activation.__name__=="softmax"
output: def construct_mnist_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential([
          Dense(128, activation='relu', input_shape=(784,)),
          Dense(128, activation='relu'),
          Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
      return model