input: 
  def mnist_classifier() -> keras.Model:
      """ 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 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.
      >>> model = mnist_classifier()
      >>> model.compile.__name__
      'compile'
      """
  
output:     model = keras.Sequential([
          keras.layers.InputLayer(input_shape=(28, 28)),
          keras.layers.Flatten(),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model