input: 
  def construct_mnist_classifier():
      """Construct a neural network model in Keras to classify the MNIST data set correctly.
          The input layer should have the shape (784,).
          The model should have one hidden layer with 128 units and 'relu' activation.
          The output layer should have 10 units and 'softmax' activation.
          The model should be compiled with 'adam' optimizer and 'sparse_categorical_crossentropy' loss.
  
      Example:
      for construct_mnist_classifier() == keras.Model(inputs=keras.layers.Input(shape=(784,)),
                                                      outputs=keras.layers.Dense(10, activation='softmax')(
                                                          keras.layers.Dense(128, activation='relu')(
                                                              keras.layers.Input(shape=(784,)))))
      """
  
output:     from tensorflow.keras import layers, models
  
      model = models.Sequential()
      model.add(layers.Input(shape=(784,)))
      model.add(layers.Dense(128, activation='relu'))
      model.add(layers.Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
      return model