input: Write a function to construct a neural network model in Python using Keras to classify the MNIST data set correctly. The function should take no input parameters and return the compiled Keras model. The model should have an input layer with a shape of (784,), a hidden layer with 128 units and 'relu' activation, and an output layer with 10 units and 'softmax' activation. The model should be compiled with the Adam optimizer and 'sparse_categorical_crossentropy' loss, and should track accuracy as a metric.
  assert type(construct_mnist_classifier()) == keras.Model
output: def construct_mnist_classifier():
      model = keras.Sequential([
          keras.layers.InputLayer(input_shape=(784,)),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model