input: def construct_MNIST_classifier():
      """Construct a neural network model using Keras to classify the MNIST data set.
      The model should have an input layer, a hidden layer with ReLU activation, and an
      output layer with softmax activation. The model should be compiled with the Adam
      optimizer and sparse categorical crossentropy loss.
      
      Example:
      >>> model = construct_MNIST_classifier()
      >>> model.compile(optimizer='adam', 
                        loss='sparse_categorical_crossentropy',
                        metrics=['accuracy'])
      >>> model.summary()
      """
output:     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(10, activation='softmax'))
  
      model.compile(optimizer='adam', 
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
  
      return model