input: def construct_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 with 128 neurons and ReLU activation, 
      and an output layer with 10 neurons and softmax activation. The model should be compiled 
      using Adam optimizer and sparse categorical crossentropy loss.
      >>> construct_mnist_classifier()
      <keras.engine.training.Model object at 0x...>
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(128, activation='relu', input_shape=(28 * 28,)))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model