input: 
  def build_mnist_classifier():
      """Construct a neural network model using Keras to classify the MNIST data set correctly.
      The model should have an input layer, a hidden layer, and an output layer.
      The input shape should be (784,), hidden layer should have 128 units with 'relu' activation,
      and the output layer should have 10 units with 'softmax' activation.
      The model should be compiled using the 'adam' optimizer, 'sparse_categorical_crossentropy' loss,
      and 'accuracy' as a metric.
      
      Example:
      >>> build_mnist_classifier()
      <keras.engine.functional.Functional object at 0x...>
      """
  
output:     from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(128, activation='relu', input_shape=(784,)))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model